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A Statistical Method for Predicting Automobile Driving Posture
Abstract
A new model for predicting automobile driving posture is presented. The model, based on data from a study of 68 men and women in 18 vehicle package and seat conditions, is designed for use in posturing the human figure models that are increasingly used for vehicle interior design. The model uses a series of independent regression models, coupled with data-guided inverse kinematics, to fit a whole-body linkage. An important characteristic of the new model is that it places greatest importance on prediction accuracy for the body locations that are most important for vehicle interior design: eye location and hip location. The model predictions were compared with the driving postures of 120 men and women in five vehicles. Errors in mean eye location predictions in the vehicles were typically less than 10 mm. Prediction errors were largely independent of anthropometric variables and vehicle layout. Although the average posture of a group of people can be predicted accurately, individuals' postures cannot be predicted precisely because of interindividual posture variance that is unrelated to key anthropometric variables. The posture prediction models developed in this research can be applied to posturing computer-rendered human models to improve the accuracy of ergonomic assessments of vehicle interiors.
... Driver posture is calculated with the statistical models developed by Park et al. (2016a). Such models require representing the driver with the kinematic linkage shown in Figure 1 (Park et al., 2016a;Reed et al., 2002). The points hip P , 5 1 L S P − , 12 1 T L P − , 7 1 C T P − , tragion P , eye P , and the origin of the system of coordinates are in the sagittal plane, with 0 x = at the ball of foot (BOF) and 0 z = at the accelerator heel point (AHP). ...
... Source: Adapted from Reed et al. (2002) and Park et al. (2016b) ...
... The statistical models applied in the calculations of occupant posture (Park et al., 2016a;2016b;Reed et al., 2002;2019) were developed in conditions that reflect the packaging of current passenger vehicles, where the occupants usually travel in forwardfacing seats in a sitting or, occasionally, reclined position. Once similar statistical models are available for fully automated vehicles with alternative seating layouts, it will also be possible to extend the method presented in Section 3 with procedures that are applicable to vehicles with unconventional seating configurations. ...
... Mesh morphing methods were then used to morph a baseline human model into target geometries while maintaining high geometric accuracy and good mesh quality. Given a target sex, age, stature, and BMI, the statistical human geometry models developed previously predict thousands of points that define the body posture (Park et al. 2016;Reed et al. 2000Reed et al. , 2002, the size and shape of the external body surface (Reed and Parkinson 2008), and rib cage (Shi et al. 2014;Wang et al. 2016) and lower extremity (Klein 2015;Klein et al. 2015) bone geometries. The skeleton and external body shape geometries were integrated based on the landmarks and joint locations shared in both skeleton and external body shape models. ...
... For each crash simulation, the morphed human model was positioned as a driver according to a driving posture model developed based on measurements from 68 volunteers (Reed et al. 2002). The driving posture model predicts occupant posture and position variables as a function of occupant body dimensions and vehicle package factors, as shown in Figure 2. In this study, a constant ratio of sitting height to stature (¼ 0.52) was assumed for all 103 morphed models; thus, only stature and BMI were used as the input parameters to define the driver dimensions. ...
... This study is the first to use a large set of human body models to investigate the combined effects of age, sex, stature, and BMI on injury risks in frontal crashes and is the first to use a validated driving posture model to rigorously position a large set of morphed human models into a driving compartment. (Reed et al., 2002). ...
Objective: Analyses of crash data have shown that older, obese, and/or female occupants have a higher risk of injury in frontal crashes compared to the rest of the population. The objective of this study was to use parametric finite element (FE) human models to assess the increased injury risks and identify safety concerns for these vulnerable populations.
Methods: We sampled 100 occupants based on age, sex, stature, and body mass index (BMI) to span a wide range of the U.S. adult population. The target anatomical geometry for each of the 100 models was predicted by the statistical geometry models for the rib cage, pelvis, femur, tibia, and external body surface developed previously. A regional landmark-based mesh morphing method was used to morph the Global Human Body Models Consortium (GHBMC) M50-OS model into the target geometries. The morphed human models were then positioned in a validated generic vehicle driver compartment model using a statistical driving posture model. Frontal crash simulations based on U.S. New Car Assessment Program (U.S. NCAP) were conducted. Body region injury risks were calculated based on the risk curves used in the US NCAP, except that scaling was used for the neck, chest, and knee–thigh–hip injury risk curves based on the sizes of the bony structures in the corresponding body regions. Age effects were also considered for predicting chest injury risk.
Results: The simulations demonstrated that driver stature and body shape affect occupant interactions with the restraints and consequently affect occupant kinematics and injury risks in severe frontal crashes. U-shaped relations between occupant stature/weight and head injury risk were observed. Chest injury risk was strongly affected by age and sex, with older female occupants having the highest risk. A strong correlation was also observed between BMI and knee–thigh–hip injury risk, whereas none of the occupant parameters meaningfully affected neck injury risks.
Conclusions: This study is the first to use a large set of diverse FE human models to investigate the combined effects of age, sex, stature, and BMI on injury risks in frontal crashes. The study demonstrated that parametric human models can effectively predict the injury trends for the population and may now be used to optimize restraint systems for people who are not similar in size and shape to the available anthropomorphic test devices (ATDs). New restraints that adapt to occupant age, sex, stature, and body shape may improve crash safety for all occupants.
... Landmark data from the hard seat and vehicle seat were used to characterize participant posture. Figure 2 illustrates the primary variables, which are based on the posture models reported by Reed et al. (2002) and Park et al. (2016aPark et al. ( , 2016b. The torso posture was defined based on a kinematic linkage consisting of pelvis, lumbar, thorax, neck, and head segments. ...
... The lumbar segment spans L5/S1 to T12/L1 and the thorax segment connects T12/L1 with C7/T1. Note that these spine "joints" are defined as the estimated center of the intervertebral disk (see Reed et al. 2002). The neck segment spans C7/T1 to the estimated atlanto-occipital joint location. ...
... This study is unusual in finding hip locations slightly rearward of seat H-point, on average, even at the 23 seat back angle. Many previous studies have found hip locations somewhat forward of the H-point (e.g., Reed et al. 2002;Park et al. 2016aPark et al. , 2016b. More-rearward hip locations were expected in the more-reclined conditions, because the position of the seat back angle pivot results in the seat back moving rearward behind the pelvis as it reclines. ...
Objective: Highly reclined postures may be common among passengers in future automated vehicles. A laboratory study was conducted to address the need for posture and belt fit in these seating configurations.
Methods: In a laboratory vehicle mockup, the postures of 24 men and women with a wide range of body size were measured in a typical front vehicle seat at seat back angles of 23°, 33°, 43°, and 53°. Data were gathered with and without a sitter-adjusted headrest. Posture was characterized by the locations of skeletal joint centers estimated from digitized surface landmarks.
Results: Regression analysis demonstrated that the pelvis rotated rearward and lumbar spine flexion decreased with increasing recline. The lap portion of the 3-point belt was more rearward relative to the pelvis in more-reclined postures, and the torso portion crossed the clavicle closer to the midline of the body. Regression equations were developed to predict posture and belt fit variables as a function of passenger characteristics, seat back angle, and the use of the headrest.
Conclusions: Spine posture changes as the torso reclines in an automotive seat, and belt fit is altered by the change in posture. The results can be used to accurately position crash test dummies and computation human models and to guide the design of belt restraints.
... However, the performance information of the SAE HL and EL models is not provided in the related literature. Reed et al. (2002) and Park et al. (2016a) developed statistical models for HL and EL using driver's anthropometric variables: stature and sitting height divided by stature, body mass index (BMI) and OPL variables such as H30, cushion angle, and steering wheel location. In addition, Reed (2011) developed statistical models to predict eyellipse for female and male using stature and SAE J826 manikin's torso angle (SAE A40). ...
... The developed SGMs are differentiated from the existing models in terms of input variables. The existing models (Park et al., 2016a(Park et al., , 2016bReed et al., 2002;and Zerehsaz et al., 2017) include anthropometric dimensions, vehicle package dimensions (e.g., steering wheel fore-aft position), and seat configurations (e.g., seat height and cushion angle) as input variables in predicting a driver/soldier's hip, eye, and other joint locations. On the other hand, the proposed models include the geometrical relationships between anthropometric dimensions and driving posture as input variables. ...
... As shown in Table 3, the overall variabilities of the hip locations (SD = 53 mm for Hip x and 67 mm for Hip z ) were smaller than those of the eye locations (SD = 74 mm for Eye x and 70 mm for Eye z ) because the drivers' hip locations were more restrained by the seat and determined by a simpler kinematic linkage system than the eye locations. Note that the HP and EP estimation results of the present study are similar to the corresponding results of Reed et al. (2002) and Park et al. (2016a). However, the RMSE values of the Hip x and Eye x models (RMSE = 21.1 mm for Hip x and 29.2 mm for Eye x ) developed in the present study are lower than those of the corresponding linear models (RMSE = 31.5 ...
The Society of Automotive Engineers (SAE) J1517 and J941 models of a driver-selected seat position and a driver's eye location mainly rely on their statistical linear relationships with seat configuration and package variables. Although the SAE models are useful for vehicle interior design, their prediction performance was not provided. The present study was intended to develop accurate prediction models of a driver's hip location (HL) and eye location (EL) based on their statistical geometric relationships with anthropometric dimensions and driving postures. A driving simulation experiment was conducted with 40 Korean drivers (20 males and 20 females) in a seating buck reconfigurable to various package conditions. The anthropometric measurements, HLs, ELs, and joint angles of the participants were collected using an anthropometer, a motion capture system, and a digital human model simulation program. Two types (full model and simplified model) of statistical geometric models (SGMs) for HL and EL prediction were developed by multiple regression analysis of the anthropometric measurements and driving postures on the HLs and ELs. The average adjusted R² and RMSE of the SGMs were .82 (± .06) and 25.7 (±3.3) mm, respectively. The SGMs showed accurate and stable prediction performance because the SGMs additionally incorporated the geometric relationships of HL and EL with anthropometric dimensions and joint angles. The SGMs would be useful to predict the HLs and ELs of drivers with various body sizes and joint angles in occupant packaging.
... Zerehsaz, Jin, Ebert, and Reed (2017) used regression to analyse the driving posture of male soldiers and established a prediction model of the driving posture for military vehicles in combat (Zerehsaz et al., 2017). Reed, Manary, and Schneider (1999) and Chai (2005) developed a vehicle driving posture prediction model using statistical methods (Reed, Manary & Schneider, 1999;Reed, Manary, Flannagan, & Schneider, 2000, 2002Reed, Eberthamilton, & Schneider, 2005;Park, Ebert, Reed, and Hallman, 2016;Sun, Wu, Chai, & Xiong, 2006;Chai, 2005). These studies can provide a good reference for studying tractor driving posture but they do not provide sufficient understanding. ...
... Hence, the closer the steering wheel is to the seat, the smaller the angles of the elbow, shoulder, and knee joints are during normal driving. This is consistent with the results of previous research (Reed, Manary et al., 2000, 2002 and it can be seen from the physiological characteristics of the human body. ...
... Hence, the results are not representative of all the postures used b i o s y s t e m s e n g i n e e r i n g x x x ( x x x x ) x x x during their work. However, Reed, Manary, Flannagan, and Schneider (2002) demonstrated that posture changes in longterm sitting are small relative to the between-subject variance. ...
Recently, tractor research and development has focused on ergonomic performance design and proposed improved requirements for driving comfort and safety. The digital human model is important for virtual ergonomics design because whether or not the driving posture of the model matches reality significantly influences the design results. Because of the poor accuracy and integrity of predictions for tractor driving posture, simulations for the front and rear driving postures of high-power tractors were analysed. Fifteen dependent variables were determined by simplifying driving posture according to the representative and measurability of the joint angle of the human body and the effective segment size. The data from 54 subjects in the forward driving and backward working postures were measured using a portable three-coordinate measuring machine. The key factors influencing each dependent variable were determined through comprehensive correlation, scatter plot, and linear regression analyses. Correlation analysis of the dependent variables revealed that regression of the dependent variable was arranged in descending order. The regression prediction model of each dependent variable was established by analysing the lower-level regression variables while introducing strong correlation and high-level variables into the model. To verify the accuracy and rationality of the model, statistical tests and experimental verification were carried out, respectively. The model was accurate (p < 0.001). To further verify the fitting effect of the model, data of five other subjects were compared. The relative error of the variables was less than 5%, which proved that the prediction model has high accuracy and was aligned as expected.
... According to the principles of anthropometry, utilizing the angles and distances between key points can result in a better representation of a driver's driving state [27]. Taking By reducing the original 19 heatmaps, including the background, to 11 and then feeding them into the branches for training, several benefits were achieved: (1) The number of key points calculated by the PCMs was reduced from 18 to 10, meaning that the number of channels in branch 1 became 10. (2) The number of output channels in PAFs equaled the number of bone segments multiplied by 2, which represented the number of bones times two. ...
... According to the principles of anthropometry, utilizing the angles and distances between key points can result in a better representation of a driver's driving state [27]. Taking the skeleton sequence diagram of a single driver's normal driving state as an example, as shown in Figure 4, the coordinates of key point i were denoted as (x i , y i ) and the Euclidean distance between two key points could be expressed as ...
To reduce safety accidents caused by distracted driving and address issues such as low recognition accuracy and deployment difficulties in current algorithms for distracted behavior detection, this paper proposes an algorithm that utilizes an improved KNN for classifying driver posture features to predict distracted driving behavior. Firstly, the number of channels in the Lightweight OpenPose network is pruned to predict and output the coordinates of key points in the upper body of the driver. Secondly, based on the principles of ergonomics, driving behavior features are modeled, and a set of five-dimensional feature values are obtained through geometric calculations. Finally, considering the relationship between the distance between samples and the number of samples, this paper proposes an adjustable distance-weighted KNN algorithm (ADW-KNN), which is used for classification and prediction. The experimental results show that the proposed algorithm achieved a recognition rate of 94.04% for distracted driving behavior on the public dataset SFD3, with a speed of up to 50FPS, superior to mainstream deep learning algorithms in terms of accuracy and speed. The superiority of ADW-KNN was further verified through experiments on other public datasets.
... However, the results of these works are typically not specified in terms of actual vehicle reference points, making it difficult or impossible to apply the results in current design contexts without significant effort. Moreover, authors often consider and observe variability in posture among different factors, including gender, anthropometric measurements, age, symmetry, seat design, vehicle model, and driving venue (Reed et al., 2002;Schmidt et al., 2014;Park et al., 2016a). While these studies focus on human driving posture and body joint angles, they rarely define the relation of those angles to the driver seat geometries, limiting their current influence on DHM tools and design processes in general. ...
... At the same time, the differences could have been due to the estimations of the mid-hip point in the human body meshes used in this study. While previous studies have found mid-hip locations typically forward of the H-point (Reed et al., 2002;Park et al., 2016aPark et al., , 2016b, it can be seen in this study that various mid-hip locations are slightly rearward of seat H-point, as shown in Reed et al. (2019). Delving deeper into these mid-hip to H-point differences, we should consider the postural diversity within a population. ...
... Reed et al. (1999a) provides a detailed overview of this. On the basis of the ASPECT programme and own experiments, Reed et al. (1999bReed et al. ( , 2002 has developed the methodology of the "ASPECT" programme "cascade prediction model"for driver seat design, which is summarised in . On the basis of the cascade prediction model, Parkinson et al. (2006) developed the so called "optimization method". ...
... Fig. 7.9 Flowchart and workflow of the "cascade prediction model"(afterReed et al. 2002; quoted from Müller 2012). a Prediction of hip and eye position, b: by inverse kinematics determined suitable torso posture, c by inverse kinematics, the appropriate posture of the extremities is determined, d: complete human modelling in a suitable ...
The classic field of ergonomic vehicle design is the so-called vehicle packaging which defines the free space for drivers and passengers. Extensive SAE regulations have been developed for this purpose. The use of modern digital human models complements and partially redefines the application of these regulations. The following fields of work in vehicle-related anthropometric ergonomics are presented in detail: Since driving can in principle only take place in a seated position and long distances are often traveled, the highest demands must be placed on seats and seating positions. More than 90% of the information to be recorded for driving is done via the sight. Therefore, the design of technical elements that can impair visibility and support has elementary importance. Also operating and display components must be accommodated in the so-called visual and grasping space of the human being. An important role continues to be played by the room feeling. This refers not only to the driver’s workplace, but also to that of the front passenger and to the second and third rows of seats. Special attention is paid to the entryand exit, especially with regard to the age shift of the population. To this end, various models have been developed to optimise access to the vehicle. In addition to driving, the loading of the vehicle plays an important role for acceptance. A separate subchapter is dedicated to the consideration of specific user groups, especially older vehicle users and children. The chapter is concluded by an examination of the so-called craftsmanship, which among other things is the joy of the product and its desirability.
... Moreover, SAE J941 (2010) suggested horizontal EL (Eye x) and vertical EL (Eye z) prediction models using OPL variables (e.g., steering wheel height and pedal location). Lastly, Reed et al. developed statistical HL and EL prediction models using driver's anthropometric variables (stature and sitting height/stature), OPL variable (horizontal location of steering wheel from BOF), and seat configuration variables (seat height and cushion angle) as shown in Figure 2 [12]. ...
... Reference point for hip and eye location [12] SAE J1517's HL prediction model was developed by considering a seat height as an independent variable and SAE J941's EL prediction model was considered the simple statistical linear relationship between OPL variables such as seat height and steering wheel location; however, they didn't considered a driver's human variables such as the driver's anthropometric dimensions and driving postures ( Figure 2). Also, Reed et al.'s models were only considered the statistical linear relationship between a driver's anthropometric variables (e.g., stature and sitting height/stature) and OPL variables (e.g., H30 and cushion angle); however, there are no driving posture variables [13]. ...
The visual ergonomic is a very effective tool in preventing visual problems through environmental and postural advice. One important field in visual ergonomics is the eye ellipse. Eye ellipse may be defined as the imaginary elliptical boundaries beyond which the iris of the eye cannot reach. So far, the eye ellipse is defined only with the manikin features i.e., different manikin has different eye ellipse and this eye ellipse remains the same all times. Ergonomical evaluation software like RAMSIS, Jack, etc... also uses the same principle to define the parameters. As of now, a truck driver in his neutral position will have the same eye ellipse as when he drives a race car in the neutral position. So, we are doing a study in which we estimate the eye ellipse of manikin with respect to its H-point and also to find the parameter which has a peculiar effect on the eye ellipse.
... A simplified human body model was developed using anthropometric data derived from a volunteer participant [14,15]. The volunteer's biometric characteristics (Table 1) represented an average user profile for automotive seating studies. ...
This study explores the integration of a custom-designed pneumatic spring into a car-seat cushion and its interaction with a simplified human body model using the Finite Element Method (FEM). A 3D half-symmetry FEM framework, developed from experimental data, ensured computational efficiency and convergence. This research bridged experimental and numerical approaches by analyzing the contact pressure distributions between a seat cushion and a volunteer with representative biometric characteristics. The model incorporated two material groups: (1) human body components (bones and muscles) and (2) seat cushion materials (polyurethane foam, latex, and fabric tape). Mechanical properties were obtained from both the literature and experiments, and simulations were conducted using MSC.Marc software under realistic boundary and initial conditions. The simulation results exhibited strong agreement with experimental data, validating the model’s reliability in predicting contact pressure distribution and optimizing seat cushion designs. Contrary to the conventional notion that uniformly distributed contact pressure inherently enhances comfort, this study emphasizes that the precise localization of pressure plays a crucial role in static and long-term seating ergonomics. Both experimental and simulation results demonstrated that modulating the pneumatic spring’s internal pressure from 0 kPa to 25 kPa altered peak contact pressure by approximately 3.5 kPa (around 20%), significantly influencing pressure redistribution and mitigating high-pressure zones. By validating this FEM-based approach, this study reduces dependence on physical prototyping, lowering design costs, and accelerating the development of ergonomically optimized seating solutions. The findings contribute to a deeper understanding of human–seat interactions, offering a foundation for next-generation automotive seating innovations that enhance comfort, fatigue reduction, and adaptive pressure control.
... In this context, particular attention was paid to the impact that integrated devices such as telematics, Bluetooth, and OLED screens have on driver distraction [15]. Simultaneously, investigations were conducted on how adjustments in vehicle configurations and anthropometric characteristics influence driving posture [13,[20][21][22][23][24], highlighting the imperative of ergonomic optimization to ensure both comfort and safety. ...
This research provides an integrated perspective on the application of ergonomics and human factors engineering (HFE) to enhance comfort and efficiency in military transport systems (MTS) and civilian transport systems (CTS), identified as imperative research needs. Comfort has become the definition of vehicle quality in CTS, while in MTS, comfort directly influences crew performance in operational environments, and protecting their health is essential. The study emphasizes the importance of adopting an integrative and scientifically grounded approach that eliminates systematic errors to meet user needs and ensure optimal vehicle performance in diverse operational settings. It further highlights how applying ergonomic principles optimizes human–machine interactions, improving user comfort and safety while providing a basis for future innovations in artificial intelligence and adaptive ergonomics. This study introduces a novel hybrid methodology combining bibliometric analysis and case studies to provide fresh insights into the application of ergonomics and human factors engineering, bridging gaps between civilian and military transport systems. This approach aims to promote efficiency, comfort, and performance in operational environments. The results confirm the research hypotheses, demonstrating that ergonomic principles and human factors engineering (HFE) can significantly enhance the comfort, safety, and performance of vehicle users in both civilian and military contexts.
... For each crash simulation, the scaled occupant model was positioned as a driver according to a driving posture model developed based on measurements from 68 volunteers (17). The driving posture model predicts occupant posture and position variables as a function of occupant body dimensions and vehicle package factors. ...
Objective
Using population-based simulations and machine-learning algorithms to develop an adaptive restraint system that accounts for occupant anthropometry variations to further enhance safety balance throughout the whole population.
Methods
Two thousand MADYMO full frontal impact crash simulations at 35 mph using two validated vehicle/restraint models representing a sedan and an SUV along with a parametric occupant model were conducted based on the maximal projection design of experiments, which considers varying occupant covariates (sex, stature, and body mass index) and vehicle restraint design variables (three for airbag, three for safety belt, and one for knee bolster). A Gaussian-process-based surrogate model was trained to rapidly predict occupant injury risks and the associated uncertainties. An optimization framework was formulated to seek the optimal adaptive restraint design policy that minimizes the population injury risk across a wide range of occupant sizes and shapes while maintaining a low difference in injury risks among different occupant subgroups. The effectiveness of the proposed method was tested by comparing the population-wise injury risks under the adaptive design policy and the traditional state-of-the-art design.
Results
Compared to the traditional state-of-the-art design for midsize males, the optimal design policy shows the potential to further reduce the joint injury risk (combining head, chest, and lower extremity injury risks) among the whole population in the sedan and SUV models. Specifically, the two subgroups of vulnerable occupants including tall obese males and short obese females had higher reductions in injury risks.
Conclusions
This study lays out a method to adaptively adjust vehicle restraint systems to improve safety balance. This is the first study where population-based crash simulations and machine-learning methods are used to optimize adaptive restraint designs for a diverse population. Nevertheless, this study shows the high injury risks associated with obese and female occupants, which can be mitigated via restraint adaptability.
... Referring to the previous research results, for general human soft tissue, the values of C 10 , C 01 and D were 1.65kPa, 3.35kPa and 5.71 respectively [9]. set the pose of the human body model [21]. The FE model of the human body based on the geometric model is shown in Figure 4, including three parts: skin, bone and soft tissue, which simplified the part between skin and bone to soft tissue. ...
It is generally considered that the material properties of foam are the most important factors in vehicle seat, which affect the human-seat interface pressure. Therefore, only the role of foam is usually considered when the finite element method is used to simulate the human-seat interface pressure. In this paper, the mechanical properties and the modeling method of commonly used seat cover material were studied. The models of the seat with and without cover were established respectively according to the real-vehicle seat geometric data, and the human-seat interface pressure was simulated after the seat and human model consisting of bones, soft tissue and skin were assembled. The simulation result was compared with the actual measurement results from test, which verified the accuracy of the simulation and the role of seat cover in the human-seat interface pressure simulation. The comparison showed that the surface stiffness of the seat was greatly affected by the seat cover, which further affected the deformation and interaction caused by the contact between seat and human body, the interface pressure distribution and even the driver's sitting posture in the simulation. It was concluded that the effect of seat cover should not be easily ignored. The simulation method described in this paper can also predict the human-seat interface pressure distribution more accurately and quickly at the early stage of design, and can be used for functional verification or comfort evaluation.
... This means that there is substantial residual variance in the seat horizontal position (SeatX)in other words, people who have identical body dimensions (stature and BMI) can choose very different seat horizontal positions. This finding is congruent with those of multiple previous studies (Flannagan et al., 1998;Parkinson and Reed, 2006;Porter et al., 2004;Reed et al., 2000b;Reed et al., 2002;Reed and Flannagan, 2000;Reed et al., 2000a). The previous studies referred to the observation as "postural variability" or "non-anthropometric variability." ...
This study identified and characterized the relationship between driver personal variables and preferred vehicle interior components setting. A two-phase modeling approach was employed to characterize the temporal, logical process involved in the driver selection of a preferred vehicle interior components setting. The modified Bayesian multivariate adaptive regression splines (BMARS) modeling method was employed to identify nonlinear and interactive relationships. Forty-two male and forty-four female drivers with a wide range of ages, stature, and BMI participated in the data collection. A highly adjustable vehicle mock-up was used to empirically obtain each participant's preferred vehicle interior components setting. The study results indicated substantial non-anthropometric variability in the driver-selected seat horizontal positions and identified various interpretable nonlinearities and interactions. The study findings improve the understanding of the relationship between driver personal variables and preferred vehicle interior configuration and further inform the vehicle interior package design for driver accommodation.
... According to the height, sitting height and weight, the human body size was calculated by using the human body size prediction model [20]. Referring to the human body surface point cloud and human body slice data obtained from the typical manikins, the geometric model including the body surface and bone contour was established by using the geometric zoom method, and the pose model was used to predict and set the pose of the human body model [21]. The FE model of the human body based on the geometric model is shown in Figure 6, including three parts: skin, bone and soft tissue, which simplified the part between skin and bone to soft tissue. ...
The human body models consisting of bone, soft tissue and skin were created based on the latest anthropometry data. The mechanical modeling of vehicle seat cover was studied, as well as the simulation of human-seat interface pressure. As a case study, the seat FE model was established using the real-vehicle seat geometric data considering the condition with and without seat cover. The seat and body were assembled to conduct the simulation of human-seat interface pressure. By comparing the simulative result with those of test, the accuracy of the simulation and the important role of cover material in body pressure simulation were validated. The result also showed that cover material could not be ignored in the simulation of human-seat interface pressure. The method of interface pressure simulation presented in this paper is a systematic and useful way of prediction of the human-seat interface pressure, which can be further used for functional verification and pre-evaluation of the comfort characteristics of vehicle seat, and it is of great value of application.
... Kinematics-and physics-based posture prediction has previously been studied [14,23,26,33,34]. However, these studies only considered the final posture without considering holding the posture for a certain time, i.e., for a box-carrying task. ...
In this study, the three-compartment controller fatigue model is integrated with an inverse dynamics optimization routine to predict the optimal posture, joint fatigue, and endurance time for a box carrying task. The two-dimensional human model employed has 10 degrees of freedom. For the box carrying task, the feet are fixed on the ground, and the hand location and box weight are given. In the joint fatigue-based posture prediction formulation, the design variables are joint angles, three-compartment control values, and total box carrying duration (endurance time). The objective is to maximize the total time subject to task and fatigue constraints, including compartment unity constraint, residual capacity constraint, and a novel coupled failure constraint. The optimization successfully predicts the optimal posture, joint torque, endurance time, joint fatigue progression, and joint failure conditions. The proposed novel joint fatigue-based formulation predicts the optimal posture for maximizing the endurance time with a given box weight for a box-carrying task. Finally, the simulation is computationally efficient, and the optimal results are achieved in about 5 seconds CPU time on a regular computer.
... The pelvis kinematics and the lumbar and thoracic muscle system allow posture to be maintained even during a prolonged drive, which may be reflected by constant contact pressure on the backrest (Pau et al., 2016;Michida et al., 2001). Reed et al. (2002) confirmed this unchanged pressure on the backrest, with major limb changes but a relatively stable trunk position regardless of the driver's morphology. This result seems to be consistent with the postural fixity involved in driving. ...
During a driving task, the seat-driver interface is particularly influenced by the external environment and seat features. This study compares the effect of two different seats (S1 – soft & S2 – firm) and the effect of visual simulation of different road types (city, highway, mountain, country), on pressure distribution and perceived discomfort during prolonged driving. Twenty participants drove two 3-h sessions (one per seat) on a static simulator. Contact Pressure (CP), Contact Surface (CS), and Seat Pressure Distribution Percentage (SPD%) were analyzed throughout, using two pressure mats positioned on seat cushion and backrest. Whole-body and local discomfort for each body part were rated every 20 min. The softer seat, S1, induced a greater contact surface on cushion and backrest and a lower SPD%, reflecting better pressure distribution. Pressure profiles were asymmetrical for both S1 and S2, with higher CP under left buttock (LBu) and right lower back (RLb) and greater CS under thighs and RLb. Pressure distribution was less homogeneous on mountain and city roads than on monotonous roads (highway and country). Despite the pressure differences between the seats, however, both led to similar increases in perceived whole-body discomfort throughout the driving session. Moreover, the highest discomfort scores were in the neck and the lower back areas, whatever the seat. These findings on pressure variables may have implications for the design of backrests and cushions to ensure more homogeneous pressure distribution, even though this is not shown to minimize perceived driver discomfort.
... Moreover, pressure parameters depend on driving conditions. Seat cushion contact over time was reported to lead to sitting changes observable through a loss of pressure homogeneity and stabilization of contact pressure and surface as time increased [25][26][27]. ...
Prolonged driving under real conditions can entail discomfort linked to driving posture, seat design features, and road properties like whole-body vibrations (WBV). This study evaluated the effect of three different seats (S1 = soft; S2 = firm; S3 = soft with suspension system) on driver’s sitting behavior and perceived discomfort on different road types in real driving conditions. Twenty-one participants drove the same 195 km itinerary alternating highway, city, country, and mountain segments. Throughout the driving sessions, Contact Pressure (CP), Contact Surface (CS), Seat Pressure Distribution Percentage (SPD%) and Repositioning Movements (RM) were recorded via two pressure mats installed on seat cushion and backrest. Moreover every 20 minutes, participants rated their whole-body and local discomfort. While the same increase in whole-body discomfort with driving time was observed for all three seats, S3 limited local perceived discomfort, especially in buttocks, thighs, neck, and upper back. The pressure profiles of the three seats were similar for CP, CS and RM on the backrest but differed on the seat cushion. The soft seats (S1 & S3) showed better pressure distribution, with lower SPD% than the firm seat (S2). All three showed highest CP and CS under the thighs. Road type also affected both CP and CS of all three seats, with significant differences appearing between early city, highway and country segments. In the light of these results, automotive manufacturers could enhance seat design for reduced driver discomfort by combining a soft seat cushion to reduce pressure peaks, a firm backrest to support the trunk, and a suspension system to minimize vibrations.
... A large amount of detailed information is available on driver posture and position from laboratory studies (Reed et al. 2000(Reed et al. , 2002Park et al. 2016a;Reed and Ebert 2018) and video data from naturalistic driving studies has been analyzed to quantify driver head pose (Paone et al. 2014). More detailed measurements have been made using inertial sensors to quantify head orientation (Fice et al. 2018). ...
Objective
Recent studies have suggested that a relationship exists between crash injury risk and occupant posture, particularly in postures different from those used with anthropomorphic test devices (ATDs) in crash testing. The objective of this study was to increase scientific understanding of typical front-seat passenger postures through a naturalistic study.
Method
Video cameras were installed in the passenger cabins of the vehicles of 75 drivers. Reflective targets were attached to the seats and the seat position and seat back angle was moved through their available ranges during instrumentation. The video data, along with vehicle acceleration and location data, were downloaded after the vehicles were operated as usual by their owners for two weeks. Video frames were manually coded to identify characteristics of front-seat passenger posture and position. Seat position and seat back angle were estimated using the calibration data obtained during vehicle instrumentation.
Results
Video frames from a total of 2733 trips were coded for 306 unique front-seat passengers. For these trips, a total of 13638 frames were coded; each frame represents about four minutes of travel time. The head was rotated left or right in 33% of frames, and the torso was rotated left or right about 10% of the time and pitched forward in almost 10% of frames. No seat position or seat back angle change was noted in 40 (53%) of vehicles and the distributions of seat position and seat back angle on arrival were essentially unchanged during travel. The seat was positioned full-rear on the seat track about 23% of the time and rearward of the mid-track position in 92% of frames. The mean seat back angle was 25.4 degrees (standard deviation 6.4 degrees); seat back angle was greater than 30 degrees in 15% of frames and greater than 35 degrees in less than 1% of frames.
Conclusions
This study is the first to report distributions of postures, seat positions, and seat back angles for front-seat passengers. Seat positions rearward of the middle of the seat adjustment range are common, but highly reclined postures are infrequent. Non-nominal torso and head postures also are nontrivial.
... When considering driver, passenger, and vehicle interior ergonomics in the automotive industry, it is important to be able to realistically predict the initial, more static, seated body postures of the vehicle occupants. There are several different models for posture prediction of driving and seated postures [5][6][7][8][9][10][11][12][13][14][15]. Among these models it is possible to identify two different methods for posture prediction: 1) postures are predicted through an optimization process by minimizing deviations from so called neutral comfort angles, or 2) through statistical regression equations that predicts coordinates for specific key positions on the human body or in the car interior [16]. ...
When considering vehicle interior ergonomics in the automotive design and development process, it is important to be able to realistically predict the initial, more static, seated body postures of the vehicle occupants. This paper demonstrates how published statistical posture prediction models can be implemented into a digital human modelling (DHM) tool to evaluate and improve the overall posture prediction functionality in the tool. The posture prediction functionality uses two different posture prediction models in a sequence, in addition to the DHM tool´s functionality to optimize postures. The developed posture prediction functionality is demonstrated and visualized with a group of 30 digital human models, so called manikins, by using accurate car geometry in two different use case scenarios where the sizes of the adjustment ranges for the steering wheel and seat are altered. The results illustrate that it is possible to implement previously published posture prediction models in a DHM tool. The results also indicate that, depending on how the implemented functionality is used, different results will be obtained. Having access to a digital tool that can predict and visualize likely future vehicle occupants’ postures, for a family of manikins, enables designers and developers to consider and evaluate the human-product interaction and fit, in a consistent and transparent manner.
... Considering findings of published studies, four sitting configurations were identified and characterised, both qualitatively and quantitatively, to match one pair of primary activities [44,45,46,47,48,49,50,51,52,53,54,55,56,57] and three pairs of secondary activities [8,22,23,24,58,59,60]. Sitting configurations, shown in Figure 1, were labelled (self-) entertaining/socialising (ES), relaxing/sleeping (RS), travelling/driving (TD), and working/eating (WE). ...
Proceedings of the 54th UK Conference on Human Responses to Vibration. Edinburgh, 24-26 September 2019. The UK Conference on Human Responses to Vibration was an opportunity for specialists from the UK and further afield to exchange information, disseminate research findings and get updated on current issues related to human exposure to vibration. The conference was hosted by Edinburgh Napier University School of Engineering & the Built Environment and Reactec Ltd. The Conference provided a supportive technical forum for exchange of information, dissemination of research findings and the opportunity to be updated on current issues related to human exposure to vibration. Presented papers will cover all aspects of hand-transmitted vibration, whole-body vibration and motion sickness. Revised procedding 03/03/2020 Prof Chris Oliver was a plenary speaker at this meeting.
... Considering findings of published studies, four sitting configurations were identified and characterised, both qualitatively and quantitatively, to match one pair of primary activities [44,45,46,47,48,49,50,51,52,53,54,55,56,57] and three pairs of secondary activities [8,22,23,24,58,59,60]. Sitting configurations, shown in Figure 1, were labelled (self-) entertaining/socialising (ES), relaxing/sleeping (RS), travelling/driving (TD), and working/eating (WE). ...
The proceedings of the 54th UK Conference on Human Responses to Vibration. Held at Edinburgh Napier University from the 24th to 26th September 2019. The conference wa co-hosted by Reactec Ltd. Keynote speakers included Paul Pitts (UK, Health & Safety Executive) and Professor Chris Oliver (King James IV Professor Royal College of Surgeons of Edinburgh). The conference included international delegates from USA, Japan, Italy, China and across the UK.
... Considering findings of published studies, four sitting configurations were identified and characterised, both qualitatively and quantitatively, to match one pair of primary activities [44,45,46,47,48,49,50,51,52,53,54,55,56,57] and three pairs of secondary activities [8,22,23,24,58,59,60]. Sitting configurations, shown in Figure 1, were labelled (self-) entertaining/socialising (ES), relaxing/sleeping (RS), travelling/driving (TD), and working/eating (WE). ...
Next-generation cars will be electric, connected, autonomous, and shared. Aboard, primary activities such as driving or travelling will coexist with secondary activities such as (self-) entertaining, socialising, relaxing, sleeping, working, and eating. Although secondary activities have already been identified, related seating issues have only been touched. A three-factor mixed-design laboratory experimental study was conducted to test whether the concept of 'sitting configuration' (introduced and defined in this paper) is appropriate to characterise the seat-occupant system as a whole. Specifically, investigated were main effects and interaction effects of (biological) sex, vibration magnitude, and sitting configuration on in-line transmission of vertical vibration at seat cushion. With the Six-Axis Motion Simulator of the University of Southampton, six men and six women occupying a production reclining car seat were subjected to four vibration magnitudes in four sitting configurations corresponding to four pairs of primary and secondary activities. Transmissibility and coherence functions were calculated from acceleration measurements. An ANOVA model of first-resonance frequency of transmissibility showed an appreciable main effect of both vibration magnitude (F(2.21, 22.11) = 369.54, p < 0.001, η² = 0.28, ηP² = 0.97, ηG² = 0.54) and sitting configuration (F(1.98, 19.80) = 82.27, p < 0.001, η² = 0.48, ηP² = 0.89, ηG² = 0.67) but failed to show an appreciable main effect of sex (F(1, 10) < 0.001, p > 0.99, η² < 0.001, ηP² = 0.001, ηG² < 0.001) and any appreciable interaction effects (p > 0.99, η² ≤ 0.004, ηP² ≤ 0.12, ηG² ≤ 0.02). Results suggest that the concept of sitting configuration is appropriate to characterise the seat-occupant system as a whole. Ultimately, in design and development of seats for next-generation cars, secondary activities and corresponding sitting configurations should be taken into consideration to optimise not only functionality, but also comfort and protection (and related affective/emotional attributes).
... In an effort to realistically position a digital human on a vehicle seat, statistical models were introduced by Reed, Manary, Flannagan, and Schneider (2002). The posture models were developed based on a large cohort of US drivers. ...
... Methods can also be analytical. In [7], the researchers used a two-step approach. First, the Society of Automotive Engineering (SAE) standard on seating reference point (SgRP) is used. ...
This paper presents an approach that can be used to measure height of driver’s eyes and rear position lamps from a video, i.e., two important metrics used to set sight distance standards. This data plays an important role in the definition of geometric design of highways and streets. Our method automatically estimates the camera pose with respect to the road. It then requires selecting two points to obtain the height. New vehicles tend to be higher and larger. Consequently, this information shoud be updated. This approach has been applied on a large panel of vehicles. Our method was evaluated on vehicle height measurements. Our results suggest that our method achieves less than 1.8 cm (0.7 in) mean absolute error. Our experiments show an increase in the height of driver’s eyes and taillights.
... The results quantify the importance of driver-selected seat back angle on backset and accommodation. Previous work has demonstrated that the mean driver-selected seat back angle varies across vehicles between about 19 and 27 , though driver torso postures vary little Reed et al. 2002). No relationship between manufacturer-specified "design" seat back angle and driver posture exists , nor do drivers sit with systematically different torso recline angles in vehicles from different categories (SUVs and sedans, for example). ...
Objective: U.S. FMVSS 202a requires that a vehicle head restraint lie within a specified distance (55 mm) from the physical headform on the head restraint measurement device (HRMD). Smaller values of this distance, known as backset, are frequently associated with improved protection against neck injury in rear impact. In some vehicles, small backsets are also associated with complaints of head restraint interference with drivers’ preferred head positions. The objective of this study is to examine head/head restraint distances using data from a lab study of driving posture to provide guidance for safe and comfortable head restraint design.
Methods: Head positions were measured for 88 U.S. drivers in a laboratory mockup using a seat from a mid-size sedan. The head restraint was removed to allow measurement of drivers’ preferred head locations without interference from the head restraint. Rates of disaccommodation, defined as interference between predicted possible head restraint locations and drivers’ preferred head locations, were analyzed at HRMD-referenced backsets of 25, 50, 75, and 100 mm measured at 22° and 25° seat back angles.
Results: With HRMD-referenced backsets of 25 mm and 50 mm measured at 25°, the head restraint intersected the preferred head locations of 17.9 and 5.2% of the drivers, respectively. An HRMD-referenced backset measured at 22° produced larger accommodation rates than the same backset measured at 25°.
Conclusions: The reported distribution of occupant head positions and the resulting restrictions on comfortable head restraint position at various HRMD-referenced backsets and seat back angles help provide guidance for head restraint design. Knowing the actual mean driver-selected seat back angle for a particular vehicle seat and the model presented in this work, a manufacturer can choose a head restraint location that will have a high likelihood of complying with FMVSS backset requirements while also achieving minimal disaccommodation. The findings in this study support the flexibility in the current FMVSS 202a that permits testing at more upright seat back angles than the 25° originally proposed.
... Mesh morphing methods developed previously were then used to rapidly morph a baseline human model into target geometries while maintaining high geometry accuracy and good mesh quality. Given a target sex, age, stature, and BMI, the statistical human geometry models predict thousands of points that define the body posture [24][25][26], the size and shape of the external body surface [27], and ribcage [28,29] and lower extremity bone geometries [30,31]. The skeleton and external body shape geometries were integrated together based on the landmark and joint locations shared in both skeleton and external body shape models [19]. ...
Among the whole population, small, obese, and/or older occupants are at increased risk of death and serious injury in motor-vehicle crashes compared with mid-size young men. Current adult finite element (FE) human body models (HBM) have been developed in a few body sizes (large male, midsize male, and small female) with reference body dimensions similar to those of the available physical anthropomorphic test devices (ATDs). The limited number of body sizes available has resulted in part because the time needed to develop an FE HBM using typical methods is measured in months or even years. The objective of the current study was to apply a recently developed FE HBM morphing method to generate hundreds of FE human models for occupants with a wide range of stature and body shape and using the diverse human models for impact simulations. The midsize male THUMS and GHBMC models were used as the baseline models to be morphed into occupants with different combinations of stature and body shape. The target geometries were predicted using statistical geometry models of external body shape and the skeleton (ribcage, pelvis, femur and tibia) developed previously based on 3D body scan and CT data from a total of more than 500 subjects. A landmark-based radial basis function (RBF) interpolator was used to morph the baseline models into target geometries. Anthropometric targets for 112 men were sampled based on US population statistics for age, stature and body mass index (BMI). Using these targets, 100 HBMs were developed by morphing THUMS and 12 by morphing the GHBMC model. Pendulum thorax impact conditions were applied to 36 morphed THUMS models and 12 morphed GHBMC models to investigate effects of occupant characteristics on chest impact responses. The morphed models were all automatically generated without any manual adjustment, and their mesh quality was reasonable and suitable for impact simulations. The mesh morphing process required about 10-30 minutes per model on a contemporary PC. Peak impact forces and chest deflections in the chest pendulum impact simulations varied substantially with different models, confirming the need to consider population variation in evaluating the occupant responses. The age, stature, BMI, and weight effects on chest impact responses were found to be complex but consistent between the morphed THUMS and GHBMC models. The method developed in this study can help future safety designs for occupants with a wide range of stature and body shape. Hu 2
... It should be noted that few studies have been investigated on effects of the road vision on driving posture. For instance, in Reed's cascade prediction model (CPM) [7], only seat height (H30), cushion angle and steering wheel position were considered as vehicle dimensions. There is a need for future investigation on effects of both vehicle interior and exterior dimensions on driving posture. ...
The aim of the present study was to identify the f actors affecting the driving posture and their order of importance for t he postural prediction when using a digital human modelling tool. An experiment was carried out with 35 volunteers testing 5 different vehicles with a clutch pedal, covering a large range of European drivers and passenger vehicle types. The seat and steering wheel positions for each vehicle were first adjusted in a lab condition without riding. Then subjects were asked to drive the vehicle on road for about 5 minutes. Afterwards, they were asked to fill in a questionnaire in order to know the use of available vehicle interior adjustments and to identify the order of priority of the factors affecting the adjustment of vehicle interior dimensions. Results show that 47 out of 175 person-vehicle combinations (27%) made at least one re-adjustment during the road driving session, suggesting the stationary lab condition could not fully represent road driving. F or 55 of 175 volunteers-vehicle combinations (31.4%), at least one adjustment was judged too restrictive. As expected, short volunteers complained more frequently than others did. The most important factor considered for adjusting the seat and steering wheel positions was the accessibility of the pedals for all participants. The second most important factor depended on stature group. For tall volunteers, the accessibility of the steering wheel was classified as the second most important, while it was the road visibility for short and average height volunteers. These observations could be helpful not only for identifying possible vehicle interior design issues but also for identifying task priority for d riving posture prediction when using a DHM tool
... Es werden die unterschiedlichen Eigenschaften der Menschen in den Bereichen Sinnesphysiologie und Körperabmessungen berücksichtigt (Bubb 2015). Trotzdem treten interindividuelle Unterschiede, u. a. bei der Haltung, auf (Reed et al. 2002). Beispielsweise hängt die Sitzposition in einem gegebenen Sitzverstellfeld auch von den subjektiven Empfindungen bzgl. ...
Bei der anthropometrischen Auslegung wird der Einfluss von Persönlichkeitsmerkmalen im psychologischen Sinne nur wenig und nicht einheitlich berücksichtigt. In diesem Beitrag werden potenziell relevante Persönlichkeitsmerkmale für die anthropometrische Auslegung des Fahrerarbeitsplatzes sowie der Einfluss der Persönlichkeitsmerkmale mittels einer zweistufigen Expertenbefragung ermittelt. Für einen praxisnahen Zugang im Bereich der Ergonomie sind die verwendeten Persönlichkeitsmerkmale nicht allgemeiner Art, sondern weisen einen ausgeprägten Bezug zur anthropometrischen Auslegung auf. Insgesamt werden acht wichtige Persönlichkeitsmerkmale abgeleitet.
... In addition, detecting changes based on variable levels is relatively easy. Hence, numerous studies have used quantitative modeling methods based on measurable parameters to evaluate human behavior or the perception of interface components in vehicle environments, such as satisfaction (You, Ryu, Oh, Yun, & Kim, 2006), visual complexity Ling, Lopez, & Shehab, 2013;Yoon, Lim, & Ji, 2015a, 2015b, and driver posture (Park, Ebert, Reed, & Hallman, 2015Reed, Manary, Flannagan, & Schneider, 2002). In this study, we developed and validated a quantitative model to evaluate in-vehicle controller complexity. ...
Smart functions in vehicles have led to an increase in the complexity of control interfaces. This study aims to develop a model for evaluating in-vehicle controller complexity and to investigate the relationship between complexity and task performance. A research framework consisting of three complexity dimensions (functional, behavioral, and structural dimensions) and controller-related variables was developed based on previous literature. A user experiment was conducted using 10 vehicles and 91 participants. A regression analysis was used to examine the relationship between the measurement variables and perceived controller complexity, and the results indicated correlations between them. An increase in functional dimension variables caused an increase in the perceived complexity level, while behavioral dimension variables are not a statistically significant predictor. Structural dimension variables showed different results depending on the characteristics of the variables. The results of the control task experiment showed a negative correlation between task performance and the perceived complexity level. In addition, satisfaction decreased with increasing levels of complexity. These results provide insights for managing in-vehicle controller complexity.
... In addition, most published studies were performed using an experimental setup without the clutch pedal. For example, the statistical predictive models proposed by Reed and his colleagues (Park, Ebert, & Reed, 2016;Reed, Manary, Flannagan, & Schneider, 2002) are based on data without considering the clutch pedal. They may not be applicable to vehicles with the clutch, knowing that the majority of vehicles in Europe, particularly in France, have the clutch. ...
Objective
The effects of seat height and anthropometric dimensions on drivers? preferred postures were investigated using a multiadjustable vehicle mock-up with a large number of adjustments and extended ranges.Background
Many studies have been conducted on preferred driving posture under different test conditions showing mixed and even contradictory findings. No studies thus far have considered the clutch and compared Chinese and European drivers.Method
Four seat height conditions were tested: free and three imposed heights (250, 300, and 350 mm). Sixty-one subjects (40 French-born and 21 Chinese-born) participated in the experiment, covering a large range of stature and sitting height?to?stature ratio. The RAMSIS kinematic model was used to reconstruct postures, and main intersegmental angles were extracted for characterizing posture.ResultsUnder the free seat height condition, no significant differences in preferred intersegmental angles were observed between different participant groups. Seat height mainly affected trunk?thigh angle, whereas it had almost no effect on trunk orientation and other intersegmental angles. Chinese participants sat more forward in the seat, leading to a more opened trunk?thigh angle and a more reclined trunk.Conclusions
Results suggest that intersegmental angles of preferred posture are not dependent on anthropometric dimensions, although shorter drivers prefer a slightly less reclined trunk. Self-selected driving posture results from a compromise between maintaining the intersegmental angles in one?s preferred range and a preferred trunk orientation in space.ApplicationsThe findings contribute to a better understanding of preferred driving postures and would be helpful for improving vehicle interior design.
Sitting posture affects driver comfort and health. To investigate driver posture under autonomous vehicle conditions, this study measured the three-dimensional coordinates of external marker points on the driver, considering different backrest tilt angles and the presence or absence of a headrest. The posture Angle corresponds to the human body segment and represents the human body posture. Using the anatomical relationship between the external marker points and the endpoints of the posture angle, the posture angle was calculated and analysed to obtain the pattern of variation of the posture angle. The results show that increasing the backrest angle generally increases the angles of the head, neck, thoracic, abdomen, and elbows, while the knee angle remains unaffected. The pelvic angle is influenced by the headrest, showing consistent behavior when a headrest is present but not without it. At a 70° backrest angle, the pelvis tilts backward, indicating insufficient lumbar support in the current seat design. These findings provide an important reference for future seat design.
The study measured the body marking points using a coordinate measuring machine. Calculations were made using the points to analyse the pattern of changes in sitting posture and it was concluded from the idiosyncratic changes that adjustable lumbar support as well as headrests are important for sitting posture comfort.
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Objective:
The objective of this study is to use parametric human modeling and machine learning methods to identify representative occupants that can account for injury variations among a more diverse population with a limited simulation budget.
Method:
A maximal projection method was used to sample 100 occupants, considering the variations in stature, weight, and sitting height. An automated mesh morphing method was used to morph the THUMS v4.1 midsize male model into the target geometries. US-NCAP frontal crash simulations were conducted with morphed human models and validated vehicle/restraint models. Surrogate models based on the Gaussian Process (GP) method were trained to find inducing points (IP), here defined as a small number of representative occupants whose outcomes could be used to accurately estimate the variations in the injury risks and patterns throughout the population. Statistical analysis was conducted to validate the IPs' coverage of total variation by illustrating the IP distribution. Restraint optimization was performed at IPs to yield an enhanced restraint system. The method was validated through comparisons among the predicted injury risks under the optimal and baseline designs.
Results:
Only 20 IPs were needed to sufficient to properly represent the variations in the injury risks and patterns in the whole population with acceptable accuracy. Compared to the surrogate model built from 100 crash simulations, the IP-based surrogate models incurred only 0.4% and 1.8% errors in head injury risks for males and females, respectively. Regarding the injury risks on the chest and lower extremities, the IP-based surrogate models resulted in less than 0.1% and 0.5% errors for males and females, respectively. The FE simulations indicated that the optimal restraint system design reduced the injury risk by relatively 16% and 13% for male and female respectively when delta-V = 25 (mph), and 47% and 27% for male and female when delta-V = 35 (mph).
Significance of results:
The study proposed a method to generate more accurate injury risk predictions for a more diverse population under a limited simulation budget. Simulations using IPs may enable restraint system optimization to be conducted more efficiently while reducing injury risks across a more diverse population.
div class="section abstract"> The increased use of computational human models in evaluation of safety systems demands greater attention to selected methods in coupling the model to its seated environment. This study assessed the THUMS v4.0.1 in an upright driver posture and a reclined occupant posture. Each posture was gravity settled into an NCAC vehicle model to assess model quality and HBM to seat coupling.
HBM to seat contact friction and seat stiffness were varied across a range of potential inputs to evaluate over a range of potential inputs. Gravity settling was also performed with and without constraints on the pelvis to move towards the target H-Point. These combinations resulted in 18 simulations per posture, run for 800 ms. In addition, 5 crash pulse simulations (51.5 km/h delta V) were run to assess the effect of settling time on driver kinematics. HBM mesh quality and HBM to seat coupling metrics were compared at kinetically identical time points during the simulation to an end state where kinetic energy was near zero.
A gravity settling time of 350 ms was found to be optimal for the upright driver posture and 290 ms for the reclined occupant posture. This suggests that reclined passengers can be settled for less time than upright passengers, potentially due to the increased contact area. The pelvis constrained approach was recommended for the upright driver posture and was not recommended for the reclined occupant posture. The recommended times were sufficient to gravity settle both postures to match the quality metrics of the 800 ms gravity settled time. Driver kinematics were found to be vary with gravity settling time. Future work will include verifying that these recommendations hold for different HBMs and test modes.
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Meta-heuristics are widely used methods in OR literature. Whale Optimization Algorithm (WOA) is one of these meta-heuristic methods which is recently developed. The objective of this study is to find the best possible job schedule while minimizing the make-span (i.e., the length of time elapsed from the beginning of first job to the end of the last job.) of the system. This problem is initially solved by using Optimization Programming Language namely CPLEX Studio IDE 20.1.0. Then, WOA which is a current meta-heuristic, used to solve the same problem. Some toy instances of different sizes are created and the results obtained by using CPLEX and WOA are compared. Although, in some studies in the literature, WOA is used to solve job shop scheduling problems, there is not a study which uses WOA as a solution methodology for parallel machine job scheduling problem with machine eligibility consideration to the best of our knowledge. Thus, the main contribution of this study is to include machine eligibility to the conventional job scheduling problem and to use WOA while solving the corresponding problem.
This study solves a one-dimensional cutting stock problem with multiple stock lengths. It is applied in a manufacturing setting where rolls of steel rods of different lengths are cut according to customer requirements. The one-dimensional cutting stock problem (CSP) is an NP-hard problem, including discrete demands and capacitated planning objectives. It is solved using column generation techniques. This study aims to develop a production plan that minimizes the waste of cutting steel rods of different lengths and diameters in required lengths. The approach to solving the problem has two steps. The first step is a heuristic algorithm that produces a cutting pattern at every iteration, which is then fed into a novel mathematical model to determine an optimal solution. An initial solution is obtained using randomly generated cutting patterns for the mathematical model. The algorithm terminates after a given number of iterations. The paper also proposes a Decision Support System, addresses application issues, and concludes with further studies.
The digitization of the working world is changing almost everything – the way we work, plan, and control processes. This article is about the application of digital humans and describes the change in times and paradigms toward digital human models and virtual ergonomics for the apparel industry and ergonomics. The digital body is now using with everywhere in course of industry 4.0. Representation of man could now be three-dimensional, but that one with it also has properties that go beyond the purely geometric production in the so could program the resulting digital human models. These digital human models were wholly integrated directly into CAD programs so that now the desired human scale could be available to the designer. The anthropometric properties of the digital human body are started to be used more widely. The development of digital human models should be parallel to the anthropometrically oriented models, also developed biomechanical models, which initially represented the physical inertia properties of the human body elements, but increasingly with the forces and muscles have been equipped so that complex movement sequences can also be represented with them. When it comes to personal attention to the vehicle, the design appeals to the “emotion” and the ergonomics to the “ratio”. Although ergonomics, usability, and related disciplines have existed for a long time, but have developed into decisive competitive factors in many companies, especially in recent years. The automobile is probably the most fascinating industrial product of all.
The present work deals with the optimization of ergonomic vehicle design by considering aspects of personality. Despite the consideration of many influencing factors, such as anthropometry or sensory physiology, major interindividual differences remain in many aspects of macroergonomics in terms of industrial design engineering. In order to optimize the fit between user and vehicle, personality variation must be taken into account in the future. However, it is not yet known which personality traits are relevant to ergonomics, nor is there a specific measurement instrument for this. From this problem arises the aim to detect personality traits relevant to ergonomics, to make them measurable and to determine the first influences of these personality traits on macroergonomics. Using a multi-stage systematic expert-based procedure, it is determined that the personality traits Driving Pleasure, Need for Safety, Affinity for Comfort, Discomfort Sensitivity, Infotainment Orientation, Habit, Ergonomics Awareness, and Effort Avoidance have the greatest influence on macroergonomics. To measure the characteristics, the "Ergonomics-Relevant Personality Inventory for the Auto" (ERPI-A) questionnaire is systematically developed using various methods with experts and car drivers. The examination of the quality criteria shows that ERPI-A measures the personality traits validly and economically. In order to test ERPI-A in practical use and to detect first correlations with macroergonomics, a broadly based application study is designed and conducted in the vehicle ergonomics test bench. The focus is on body posture, seat and steering wheel position, the adjustment process of seat and steering wheel, the consideration of the infotainment system while driving as well as the evaluation of vehicle, dimensional and operating concept. In this context, all personality traits have an impact on partial aspects of macroergonomics. Based on the test results, recommendations are given for the future use of ERPI-A with regard to the implementation and interpretation of tests and the adaptation of the vehicle design.
Traffic congestion in big cities in Malaysia has become a common scenario among the communities. The journey between homes to working place twice a day at considerable distances is no longer a strange situation. Being in traffic for hours in a sitting position requires recurrent tasks of manual pressing the pedal and brake excessively and if they are done without the correct sitting posture, it may trigger fatigue faster, particularly for the leg and back of the driver. In the long term, it will negatively affect the health of the driver, particularly in the form of physical, psychological, and emotional. Therefore, this paper is trying to investigate the recurrent brake pedal pressings as well as the leg postures while driving in traffic jam. The research is started with the experimental setup and data acquisition on brake pedal pressing as well as leg posture followed by the modelling and analysis of the obtained data using particle swarm optimization (PSO) modelling technique. The validation step was then executed to verify the model derived using open loop and closed loop performance analysis. The results show that the pedal pressing force of leg posture can be closely represented using 2ndorder transfer function and mimics the actual pedal pressing pattern during road traffic delay.
The driver’s whole-body posture at the time of a collision is a key factor in determining the magnitude of injury to the driver. However, current researchs on driver posture models only consider the upper body posture of the driver, and the lower body area which is not perceived by sensors is not studied. This paper investigates the driver’s posture and establishes a 3D posture model of the driver’s whole body through the application of machine vision algorithms and regression model statistics. This study proposes an improved Kinect-OpenPose algorithm for identifying the 3D spatial coordinates of nine keypoints of the driver’s upper body. The posture prediction regression model of four keypoints of the lower body is established by conducting volunteer posture acquisition experiments on the developed simulated driving seat and analyzing the volunteer posture data through using the principal components of the upper body keypoints and the seat parameters. The experiments proved that the error of the regression model in this paper is minor than that of current studies, and the accuracy of the keypoint location and the keypoint connection length of the established driver whole body posture model is high, which provides implications for future studies.
Kraftfahrzeuge ermöglichen unserer Gesellschaft Mobilität. Mit ihrer Hilfe können Menschen und Güter von einem Ursprungs- zu einem Zielort bewegt und menschliche Bedürfnisse z. B. nach Nahrung, Anerkennung und Selbstverwirklichung gestillt werden. Erreicht werden kann dieses Stillen von Bedürfnissen aber nur durch eine enge Kooperation zwischen Menschen (innerhalb und außerhalb der Fahrzeuge) sowie dem technischen System. So wirken zum Beispiel Fahrende mit Hilfe von Bedienhandlungen zur Längs- und Quersteuerung (d. h. Beschleunigen/Bremsen, Richtungswechsel) auf das Kraftfahrzeug ein.
OCCUPATIONAL APPLICATIONS
This contribution provides a framework for modeling user-product interactions (in CAD) for in-depth ergonomic analysis of product design, using digital human models. The framework aims to be applicable to a wide range of different products while being suitable for designers – especially those who do not have specialized ergonomic expertise or training in human behavior – by providing an intuitive, standardized, and time-efficient modeling procedure. The framework contains 31 elementary affordances, which describe mechanical dependencies between product geometries and human end effectors. These elementary affordances serve as a tool for interaction modeling. Additionally, the paper provides a taxonomy of elementary affordances, which can be used to formalize / abstract the nature of user-product interactions and to describe them as elementary affordances. Furthermore, an implementation of the interaction-modeling framework is presented in a CAD environment and provides an example of how the framework could be used in terms of a computer aided ergonomics tool.
Kraftfahrzeuge ermöglichen unserer Gesellschaft Mobilität. Mit ihrer Hilfe können Menschen und Güter von einem Ursprungs- zu einem Zielort bewegt und menschliche Bedürfnisse z. B. nach Nahrung, Anerkennung und Selbstverwirklichung gestillt werden. Erreicht werden kann dieses Stillen von Bedürfnissen aber nur durch eine enge Kooperation zwischen Menschen (innerhalb und außerhalb der Fahrzeuge) sowie dem technischen System. So wirken zum Beispiel Fahrende mit Hilfe von Bedienhandlungen zur Längs- und Quersteuerung (d. h. Beschleunigen/Bremsen, Richtungswechsel) auf das Kraftfahrzeug ein.
Objective
Use volunteer data and parametric finite element (FE) human body models to investigate how restraint systems can be designed to adapt to a diverse population and pre-crash posture changes induced by active safety features.
Methods
Four FE human models were generated by morphing the midsize male GHBMC simplified model into geometries representing a midsize male, midsize female, short obese female (BMI 40 kg/m²), and large obese male (BMI 40 kg/m²) based on statistical skeleton and body shape geometry models. Each human model was positioned in a generic vehicle driver environment using two occupant pre-crash postures based on volunteer test results including one resulting from 1-g abrupt braking events. Improved restraint designs were manually developed for each occupant model in a 56 km/h frontal crash condition by adding a knee airbag, adjusting the shoulder belt load limit, steering column force, and driver airbag properties (tethers, inflation, and vent size). The improved designs were then tested at both pre-crash postures. Injury risks for the head, neck, chest, and lower extremities were analyzed.
Results
Human size and shape dominated the occupant injury measures, while the pre-crash-braking induced posture had minimal effects. Some of the safety concerns observed for large occupants include head strike-through the airbag and a conflict between head and chest injuries, which were mitigated by a stiffer restraint system with properly-tuned driver airbag. Chest injuries were a prominent safety concern for female occupants, mitigated by a softer seatbelt and smaller airbag size near the chest. Obese occupants exhibited a higher likelihood of lower extremity injuries indicating a need for a knee airbag. A diverse set of improved restraint designs were effective in lowering injury risks, indicating that restraint adaptability is necessary for accounting for occupant diversity.
Conclusions
This study investigated the effects of occupant size and shape variability, posture, and restraint design on injury risk for high-speed frontal crashes. More forward initial postures due to active safety features may decrease head, neck, and lower extremity injury risk, but may also increase chest injury risk. Safety concerns observed for large occupants include head strike-through and a conflict between head and chest injuries. Obese occupants had higher knee-thigh-hip injury risk. New restraints that adapt to occupant size and body shape may improve crash safety for all occupants. Further investigation is needed to confirm and extend the findings of this study.
Computational models of the human neck have been developed to human response in impact scenarios; however, the assessment and validation of such models is often limited to a small number of experimental data sets despite being used to evaluate the efficacy of safety systems and potential for injury risk in motor vehicle collisions. In the present study, a full neck model with active musculature was developed from previously validated motion segment models of the cervical spine. Tissue mechanical properties were implemented from experimental studies, and were not calibrated in any way. The neck model was assessed with experimental studies at three levels of increasing complexity: ligamentous cervical spine in axial rotation, axial tension, frontal impact, and rear impact; post mortem human subject rear sled impact; and human volunteer frontal and lateral sled tests using an open-loop muscle control strategy. The neck model demonstrated good correlation with the experiments ranging from quasi-static to dynamic, assessed using kinematics, kinetics and tissue level response. The contributions of soft tissues, neck curvature and muscle activation were associated with higher stiffness neck response, particularly for low severity frontal impact. Experiments presenting single-value data limited assessment of the model, while complete load history data and cross correlation enabled improved evaluation of the model over the full loading history. Tissue-level metrics exhibited higher variability and therefore lower correlation, also demonstrating a high dependence on the local tissue geometry. Thus, it is critical to assess models at the gross kinematic and the tissue levels.
Digital human models (DHM) allow for a proactive ergonomic assessment of products by applying different models describing the user-product interaction. In engineering design, DHM tools are currently not established as computer-aided ergonomics tools, since (among other reasons) the interaction models are either cumbersome to use, unstandardised, time-demanding or not trustworthy. To understand the challenges in interaction modelling, we conducted a systematic literature review with the aim of identification, classification and examination of existing interaction models. A schematic user–product interaction model for DHM is proposed, abstracting existing models and unifying the corresponding terminology. Additionally, nine general approaches to proactive interaction modelling were identified by classifying the reviewed interaction models. The approaches are discussed regarding their scope, limitations, strength and weaknesses. Ultimately, the literature review revealed that prevalent interaction models cannot be considered unconditionally suitable for engineering design since none of them offer a satisfactory combination of genuine proactivity and universal validity.
Practitioner summary: This contribution presents a systematic literature review conducted to identify, classify and examine existing proactive interaction modelling approaches for digital human models in engineering design. Ultimately, the literature review revealed that prevalent interaction models cannot be considered unconditionally suitable for engineering design since none of them offer a satisfactory combination of genuine proactivity and universal validity.
Abbreviations: DHM: digital human model; CAE: computer-aided engineering; RQ: research question
The aim of this study was to test the capacity of the force feasible set formalism to predict maximal force exertion during isometric handbraking. Maximal force exertion and upper-limb posture were measured with a force sensor embedded in a handbrake and an optoelectronic system, respectively. Eleven subjects participated in the experiment which consisted of exerting the maximal force in isometric conditions considering five hand brake positions relative to the seat H-point. Then, maximal force was predicted by the force feasible set obtained from an upper-limb musculoskeletal model. The root-mean-square (RMS) of the angle between measured and predicted forces was 8.4° while the RMS error (RMSE) for amplitude prediction was 95.4 N. However, predicted, and measured force amplitudes were highly correlated (r = 0.88, p < 0.05, slope = 0.97, intercept = 73.3N) attesting the capacity of the model to predict force exertion according to the subject’s posture. The implications in the framework of ergonomics are then discussed.
Practitioner summary: Maximal force exertion is of paramount importance in digital human modelling. We used the force feasible set formalism to predict maximal force exertion during handbraking from posture and anthropometric data. The predicted and measured force orientation showed a RMS of 8.4° while amplitude presented a RMSE of 95.4 N with a strong correlation (r = 0.88, p < 0.05, slope 0.97, intercept 77.3 N).
The mechanical reliability problem of passenger car cockpit facilities layout is increasingly complex and has potential and uncertain risks for human safety while the number of private cars is increasing. A new system of layout design optimization is proposed to solve this problem. First, the optimization sequences of facilities are determined using a hybrid method of multiple-attribute decision-making and entropy. Second, the degree of feeling crowded in the cockpit layout can be adjusted based on customers’ preference. Third, an adapted particle swarm optimization algorithm is proposed to solve the problem of three-dimensional layout optimization in car cockpit human–machine interface according to the ergonomic principles, and the adapted algorithm called smoothing iteration particle swarm optimization is contrasted with those of other common algorithms to demonstrate its advantages. Finally, the optimized layout is analyzed by virtual simulations and compared with the original layout to show the feasibility and effectiveness of the proposed design system. Analysis results indicate that the optimized layout by the new particle swarm optimization can make the operation easier and safer than the original one to enhance ergonomic reliability.
RESULTS OF PREVIOUS SAE DRIVERS' EYE-LOCATION STUDIES HAVE BEEN USED TO DEVELOP A FIXED SEAT EYELLIPSE AND CONTOURS THAT DESCRIBE DRIVERS' HEAD LOCATIONS. CENTROID DATA FROM THESE AND OTHER EYE-LOCATION STUDIES ARE USED AS A MEANS OF LOCATING THE SAE EYELLIPSE ACCORDING TO SEAT BACK ANGLE. PART I COMPRISES THE DISCUSSION OF THESE DATA. STUDIES RECENTLY COMPLETED PROVIDE DATA ON DRIVERS' EYE LOCATIONS FOR VARIED VEHICLE PACKAGES RANGING FROM SPORTS CARS TO HEAVY TRUCKS. THE RESULTS ARE SUMMARIZED IN PART II AS A SERIES OF TABLES. WHICH INCLUDE STATISTICAL DEFINITIONS OF TANGENT CUTOFF EYELLIPSES. PART III OF THIS REPORT DESCRIBES A METHOD FOR POSITIONING A FIXED SEAT EYELLIPSE ACCORDING TO SEAT BACK ANGLE. A METHOD IS ALSO SHOWN FOR MEASURING HEADROOM RELATIVE TO SEAT BACK ANGLE. /SAE/
The effects of vehicle package, seat, and anthropometric variables on posture were studied in a laboratory vehicle mockup. Participants (68 men and women) selected their preferred driving postures in 18 combinations of seat height, fore-aft steering wheel position, and seat cushion angle. Two seats differing in stiffness and seat back contour were used in testing. Driving postures were recorded using a sonic digitizer to measure the 3D locations of body landmarks. All test variables had significant independent effects on driving posture. Drivers were found to adapt to changes in the vehicle geometry primarily by changes in limb posture, whereas torso posture remained relatively constant. Stature accounts for most of the anthropometrically related variability in driving posture, and gender differences appear to be explained by body size variation. Large intersubject differences in torso posture, which are fairly stable across different seat and package conditions, are not closely related to standard anthropometric measures. The findings can be used to predict the effects of changes in vehicle and seat design on driving postures for populations with a wide range of anthropometric characteristics.
An anatomical anthropometric study of adult human cadaveric pelves was performed to investigate the relationship between hip joint center (HJC) and selected aspects of pelvic geometry. Sixty-five pelves (35 female and 30 male) were examined. Measurements of pelvic geometry and HJC (center of bony acetabular rim) were taken from bony landmarks of de-fleshed pelves. Correlation analysis revealed that HJC cannot be accurately located as a function of pelvic width alone, but requires estimation as a function of pelvic height and depth as well. HJC was optimally located relative to the respective ASIS: 14% of pelvic width medial (mean error 0.58 cm), 34% of pelvic depth posterior (mean error 0.30 cm), and 79% of pelvic height inferior (mean error 0.35 cm). No significant differences were found between males and females in HJC estimation.