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Investigation of Inertial Properties of the Human Body
Abstract and Figures
Knowledge of the anthropometric parameters of the human body is essential for understanding of human kinetics and particularly for the design and testing of impact protective systems. Considerable information is available on the size, weight and center of mass of the body and its segments. This report supplements existing information with data regarding mass distribution characteristics of the human body as described by the principal moments of inertia and their orientation to body and segment anthropometry. The weight, center of mass location and principal moments of inertia of six cadavers were measured, the cadavers were then segmented and the mass, center of mass, moments of inertia and volume were measured on the fourteen segments from each cadaver. Standard and three-dimensional anthropometry of the body and segments was also determined.
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... The digital model has specific contact joints and forces, to be able to simulate real-world movements [19,20]. The model is generated using body segments and inertial data from the literature [21]. Head and torso geometries are defined as spheres, and the upper arm, forearm, thigh, and calf are defined as ellipses with their inertial properties given using the inertial properties presented by R. F. Chandler et al. in report [21], which is a thorough investigation that looks at the physical characteristics connected to the dynamics of the human body with an emphasis on the inertial properties. ...
... The model is generated using body segments and inertial data from the literature [21]. Head and torso geometries are defined as spheres, and the upper arm, forearm, thigh, and calf are defined as ellipses with their inertial properties given using the inertial properties presented by R. F. Chandler et al. in report [21], which is a thorough investigation that looks at the physical characteristics connected to the dynamics of the human body with an emphasis on the inertial properties. The data provided in article [21] is important because it clarifies how the human body reacts to motion and force, which is important in domains like biomechanics, ergonomics, automobile safety, and robotics. ...
... Head and torso geometries are defined as spheres, and the upper arm, forearm, thigh, and calf are defined as ellipses with their inertial properties given using the inertial properties presented by R. F. Chandler et al. in report [21], which is a thorough investigation that looks at the physical characteristics connected to the dynamics of the human body with an emphasis on the inertial properties. The data provided in article [21] is important because it clarifies how the human body reacts to motion and force, which is important in domains like biomechanics, ergonomics, automobile safety, and robotics. The study looks at the distribution of mass in the human body and how that influences the responses of various body components to motions and forces from the outside world. ...
Fall risk assessment is becoming an important concern, with the realization that falls, and more importantly fainting occurrences, in most cases require immediate medical attention and can pose huge health risks, as well as financial and social burdens. The development of an accurate inertial sensor-based fall risk assessment tool combined with machine learning algorithms could significantly advance healthcare. This research aims to investigate the development of a machine learning approach for falling and fainting detection, using wearable sensors with an emphasis on forward falls. In the current paper we address the problem of the lack of inertial time-series data to differentiate the forward fall event from normal activities, which are difficult to obtain from real subjects. To solve this problem, we proposed a forward dynamics method to generate necessary training data using the OpenSim software, version 4.5. To develop a model as close to the real world as possible, anthropometric data taken from the literature was used. The raw X and Y axes acceleration data was generated using OpenSim software, and ML fall prediction methods were trained. The machine learning (ML) accuracy was validated by testing with data acquired from six unique volunteers, considering the forward fall type.
... Furthermore, for exoskeletons that aim to compensate for the weight of the human limb, it is essential to accurately know the properties of the limb to verify the controller's performance. However, the properties of human limb, such as their weight and centre of mass (CoM), can only be estimated indirectly (e.g., using tables from the literature [10,11]). Especially in the case of populations with neuromuscular disorders, the anthropometrics might deviate from the available tables due to, for instance, muscle atrophy, bone density reductions, joint contractures, or deformations. ...
... • A representative and adjustable segment mass and CoM [10,11,[20][21][22]; • A representative forearm anthropometric to fit the DAROR interface sleeve(s); • A similar DAROR range of motion; see Table 1; • An attachment at the base of the frame at the shoulder joint; • An adjustable (linear) eJimp; • An interface to attach additional objects at the wrist location. ...
... The location for the mounting of the steel plates is based on a CAD model, which is used to estimate the CoM. The dimensions of the dummy arm are based on the biometric data from [10], resulting in an approximate length of 250 mm for the lower arm and 300 mm for the upper arm. The segment mass parameters are based on the work of [10,11,20,22], with a mass of approximately 1.6 to 2 kg for the upper arm and 0.9-1.1 kg for the forearm. ...
Motorised arm supports for individuals with severe arm muscle weakness require precise compensation for arm weight and elevated passive joint impedance (e.g., joint stiffness as a result of muscle atrophy and fibrosis). Estimating these parameters in vivo, along with the arm’s centre of mass, is challenging, and human evaluations of assistance can be subjective. To address this, a dummy arm was designed to replicate the human arm’s anthropometrics, degrees of freedom, adjustable segment masses, and passive elbow joint impedance (eJimp). This study presents the design, anthropometrics, and verification of the dummy arm. It successfully mimics the human arm’s range of motion, mass, and centre of mass. The dummy arm also demonstrates the ability to replicate various eJimp torque-angle profiles. Additionally, it allows for the tuning of the segment masses, centres of mass, and eJimp to match a representative desired target population. This simple, cost-effective tool has proven valuable for the development and verification of the Duchenne ARm ORthosis (DAROR), a motorised arm support, or ‘exoskeleton’. This study includes recommendations for practical applications and provides insights into optimising design specifications based on the final design. It supplements the CAD design, enhancing the dummy arm’s application for future arm-assistive devices.
... The entries at positions (2,3) and (3,3) in 0 T 2 represent x(t) and y(t), respectively. Using the law of cosines, the following expression is then obtained: ...
... Conversely, the numerical values of the parameters for the 2D robot, denoted as l 1 , l 2 , l c1 , l c2 , m 1 , m 2 , I 1 , I 2 and illustrated in Figure 2-a, represent the average values for humanoid arms. These values were derived from the study conducted in [3]. In particular, the first and second links have masses of m 1 = 1.8425 kg and m 2 = 1.1132 kg, respectively, while their moments of inertia are I 1 = 0.0133 kg m 2 and I 2 = 0.0021 kg m 2 . ...
Online Signature Verification commonly relies on function-based features, such as time-sampled horizontal and vertical coordinates, as well as the pressure exerted by the writer, obtained through a digitizer. Although inferring additional information about the writers arm pose, kinematics, and dynamics based on digitizer data can be useful, it constitutes a challenge. In this paper, we tackle this challenge by proposing a new set of features based on the dynamics of online signatures. These new features are inferred through a Lagrangian formulation, obtaining the sequences of generalized coordinates and torques for 2D and 3D robotic arm models. By combining kinematic and dynamic robotic features, our results demonstrate their significant effectiveness for online automatic signature verification and achieving state-of-the-art results when integrated into deep learning models.
... For this model, since the masses in each link are distributed, the links have inertias I i , where i is the link number. Some of the inertial parameters used are based on the results found in [37]. ...
Devices based on walker robotics research sometimes require a reference trajectory for the control systems of the devices to track. One valuable class of methods used to find an optimal reference trajectory is direct collocation, but even after selecting a method like direct collocation, several optimization design decisions remain. In order to determine the most desirable optimization settings, 600 optimizations were performed for the trajectory of a two degree-of-freedom (DOF) compass gait walker, and 200 optimizations were performed for a five-DOF link walker. These runs evaluated various combinations of optimization settings, including: numerical vs. symbolic vs. automatic differentiation; trapezoidal vs. Hermite Simpson collocation; numerical vs. symbolic calculation of joint accelerations; and inclusion or exclusion of joint accelerations in the decision variables. The different generated gaits were then compared in terms of computational efficiency and accuracy. The results showed that including joint accelerations as decision variables eliminated alternative gaits but increased computational complexity and variability. Symbolic acceleration evaluation was preferable when automatic differentiation was excluded. Automatic differentiation was shown to be significantly faster than the other two differentiation methods for both walking models. In addition, Hermite-Simpson collocation, although slower than trapezoidal, was the more accurate of the two approaches. These results can be applied to the derivation of optimal reference joint trajectories in future robotics applications.
... This is the author's version which has not been fully edited and calculated considering both the vehicle and the rider masses and their individual CoG positions, as approached by [27]. The inertial properties of the human body are presented in [28] and [29], being applied here to determine the individual CoG position and posture of the rider. It was considered that the standard rider has a nominal mass of 95kg. ...
This paper presents two case studies on the application of Sliding Mode Control, a technique selected for its well-known robustness against noise, disturbances and model uncertainties. By introducing a clear and systematic methodology for designing and implementing Sliding Mode Controllers in two distinct nonlinear mechanical systems, the authors conduct an assessment of their performance and robustness. Initially, a simplified mathematical model is adopted to design a Sliding Mode Controller for a didactic cart and pendulum test rig. Subsequently, the approach is extended to develop a Sliding Mode Controller for a multibody tricycle model, also from a simplified mathematical framework, based on a laboratory prototype of the tricycle. The effectiveness of the proposed method is shown through successful experimental validation for pendulum control, together with simulated analysis for the tricycle application. These results highlight the achieved performance and robustness, reinforcing the expectation of good results in future real-world road tests of the tricycle.
... • Inertial properties of the humerus, radius, and capitate were set using anthropomorphic estimation for the average male height and weight [53]. Principal moments of inertia for the body segments are taken from [54]. • The MTU model of the original model [55], is replaced by the more recent [56] MTU model. ...
This study aims to evaluate a grip strength model designed to elucidate the relationship between measured grip force and muscular activity and assess its impact on an upper limb musculoskeletal model. This work aims to develop the grip strength model by utilizing a piecewise linear function based on the Woods and Bigland-Ritchie EMG-force model, which derives its parameters from 10 adult participants performing isometric and dynamic gripping tasks. Experimental results demonstrate the model's efficacy in estimating surface electromyography (sEMG) readings from force measurements, with a mean root mean square error (RMSE) of 0.2035 and a standard deviation of 0.1207. Moreover, incorporating sEMG readings associated with grip force does not significantly affect the optimization of muscle activation in the upper arm, as evidenced by kinematic data analysis from dynamic tasks. This validation underscores the model's potential to enhance musculoskeletal model-based motion analysis pipelines without distorting results. Consequently, this research emphasizes the prospect of integrating external models into existing human motion analysis frameworks, presenting promising implications for physical Human-Robot Interactions (pHRI).
... Table. 1 shows the mass ratio of each part [3]. Using the internal division ratio [4] shown in Fig. 2, we obtained the position of the center of gravity of each part of the link model from the position obtained by pose estimation. The red dots in Fig. 2 are the positions obtained by BlazePose, and the blue dots are the center of gravity positions of each part of the link model. ...
We present the sense of effort through vibration to help the video viewer understand how the person in the video moves the body. We suppose sense of effort is related to force, so we generate vibration based on force and present the sense of effort through the vibration. We use perceived intensity to make sense of effort proportional to vibration. In our demonstration, you can experience vibration while watching a video. We can create vibration on the spot, so you can experience vibration made from a video taken on the spot.
... Anthropometric properties were determined for a standard male with a height of 1.8 m and a mass of 75 kg. The masses, lengths, and the location of the center of mass from the proximal end of the segment were adapted from [34] and the moments of inertia were adapted from [35] (Table 1). Fig. 4 shows the coordinate values of the "d", "COM" and "p" points (see Fig. 3) in the link-segment model (except the hand). ...
This study focuses on developing a simulation model using MATLAB Multibody tools to analyze the biomechanical effects of different strength training exercises on the musculoskeletal system. Unlike previous models that typically analyze single exercises or specific body regions, this model includes 45 segments and 44 joints when considering the fingers, with a total of 51 degrees of freedom. Basic exercises like dumbbell curl, dumbbell fly, pull up, push up, dumbbell bench press, and dumbbell back squat were simulated using this model. By analyzing motion data, the study calculated joint moments in shoulder, elbow, hip, knee, and ankle joints, along with muscle forces in the biceps brachii. Comparisons with existing literature confirmed the utility of the model. Its flexible structure allows for dynamic analysis of various exercise movements and daily activities such as sit-to-stand, climbing steps, and walking. The model also holds potential for assessing rehabilitation processes by examining how recommended programs affect joint biomechanics, and for distinguishing biomechanical differences between patients and healthy individuals. Overall, it provides a robust framework for studying the impact of exercises on joint biomechanics and has wide-ranging applications in both research and practical settings.
An impressive growth of theoretical, methodological, and experimental advancements of robotic components and systems in healthcare applications has characterized the last three decades. Robots are able to support prevention, diagnosis and treatment of a wide range of human diseases. Several industrial medical robots exist, whose performance, reliability and affordability have made them true success stories and turned—in several cases—research concepts into gold standards in clinical care.
This chapter will consider four representative domains of medical robotics: surgery, rehabilitation, limb substitution and assistive technology. In each of these domains, robots have reached a rather high level of maturity, so that very interesting and inspiring stories can be told on various aspects of their evolution.
Based on these assumptions and objectives, the remainder of this chapter will address the following topics: (i) motivations for developing medical robots, (ii) robots for surgery, (iii) robots for rehabilitation, (iv) robots for limb substitution and (v) robots for assistive services.
Thesis (Ph. D.)--Southern Methodist University, 1974. Bibliography: leaves 227-230. Microfilm. s
This paper is concerned with the statistical estimation of the inertial property distribution of the human torso, based on an analysis of the data obtained from segmented cadaveric trunks. This estimation is based on a sample of eight cadavers: seven embalmed and one unembalmed. The implications of these biomechanical data on mathematical simulation and manikin (dummy) models of the human body are briefly discussed, especially with respect to dynamic situations.
By segmenting the body into 14 idealized masses, a mathematical model is developed to approximate the mass distribution, center of mass, moments of inertia, and degrees of freedom of a human being. An analysis of the model reveals that the segment moments of inertia about the mass centers of the hands, feet, and forearms are negligible when compared to the total body moments of inertia, although the torso moment of inertia is not negligible. Some selected problems in thrust misalignment, freebody dynamics, stability of rotation, and torque application are solved analytically to predict man's dynamic response characteristics in space. Preliminary experiments indicate that the torque which weightless man can exert by applying a sudden twist to a fixed handle varies as a halfsine wave, and is approximately 67% of his maximum torque under normal gravity conditions.