No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A Single-Ply and Knit-Only Textile Sensing Matrix for Mapping Body Surface Pressure
Abstract
Wearable sensing fabrics have great potential for applications such as human-computer interaction, motion monitoring, and human shape reconstruction. However, existing fabric sensors tend to sacrifice wearing comfort for sensing functionality, which restricts their application scenarios and hampers long-term usability due to poor wearability. To address this challenge, a novel fabric structure was designed to work with a flat knitting machine to realize a single-ply and knit-only textile pressure-sensing matrix. The sensing fabric has a sensing range of 0.255 to 35.65 kPa, with a maximum sensitivity of 0.72 kPa
−1
. Although its sensing performance fluctuates under fatigue testing, washing and drying, folding, and stretching operations, it still supports use. It also has thermal comfort performance comparable to a regular T-shirt. We produced a pair of sensing shorts containing 256 sensing units and collected a total of 224,483 frames of data containing 18 postures from 6 participants. Posture classification using ResNet-18 achieved 88.2% accuracy.
... Flexible pressure sensors have attracted significant attention as emerging sensing devices in recent years. Their exceptional wearability [1][2][3][4][5], biocompatibility [6,7], and adaptability to multi-scenario applications [8,9] demonstrate unique value across fields including human motion monitoring, human-machine interfaces, and bioinspired electronic skin [10][11][12][13][14]. Among prevalent sensor types (piezoresistive [15][16][17][18], capacitive [19][20][21], and piezoelectric [22][23][24]), piezoresistive pressure sensors stand out due to their high sensitivity (typically reaching several kPa −1 level), rapid response (<100 ms), and costeffective manufacturing, establishing them as a focal point in flexible sensing research. ...
Flexible pressure sensors face the dual challenges of weak signal extraction and environmental noise suppression in wearable electronics and human-machine interfaces. This research proposes an intelligent pressure sensor utilizing chitosan/carbon nanotube/melamine sponge (CS/CNT/MS) composites, achieving high-performance sensing through a dual-stage noise reduction architecture that combines mechanical pre-filtration and electrical synergistic regulation. An innovative compressed-stitching encapsulation technique creates pressure sensors with equivalent mechanical low-pass filtering characteristics, actively eliminating interference signals below 3 kPa while maintaining linear response within the 3–20 kPa effective loading range (sensitivity: 0.053 kPa⁻¹). The synergistic effects of CS molecular cross-linking and CNTs’ three-dimensional conductive network endow the device with a 72 ms response time, 24 ms recovery speed, and over 3500-cycle compression stability. Successful applications in smart sport monitoring and tactile interactive interfaces demonstrate a material-structure-circuit co-design paradigm for mechanical perception in complex environments.
In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure maps to a representation closer to the expected input space of common purpose pose estimation modules. Accordingly, we used a fully convolutional network with multiple scales to provide the pose-specific characteristics of the pressure maps to the pre-trained pose estimation module. Our complete analysis of different approaches shows that the combination of learnable pre-processing module along with re-training pre-existing image-based pose estimators on the pressure data is able to overcome issues such as highly vague pressure points to achieve very high pose estimation accuracy.
Background
Vertical jump height is widely used in health care and sports fields to assess muscle strength and power from lower limb muscle groups. Different approaches have been proposed for vertical jump height measurement. Some commonly used approaches need no sensor at all; however, these methods tend to overestimate the height reached by the subjects. There are also novel systems using different kind of sensors like force-sensitive resistors, capacitive sensors, and inertial measurement units, among others, to achieve more accurate measurements.
Objective
The objective of this study is twofold. The first objective is to validate the functioning of a developed low-cost system able to measure vertical jump height. The second objective is to assess the effects on obtained measurements when the sampling frequency of the system is modified.
Methods
The system developed in this study consists of a matrix of force-sensitive resistor sensors embedded in a mat with electronics that allow a full scan of the mat. This mat detects pressure exerted on it. The system calculates the jump height by using the flight-time formula, and the result is sent through Bluetooth to any mobile device or PC. Two different experiments were performed. In the first experiment, a total of 38 volunteers participated with the objective of validating the performance of the system against a high-speed camera used as reference (120 fps). In the second experiment, a total of 15 volunteers participated. Raw data were obtained in order to assess the effects of different sampling frequencies on the performance of the system with the same reference device. Different sampling frequencies were obtained by performing offline downsampling of the raw data. In both experiments, countermovement jump and countermovement jump with arm swing techniques were performed.
Results
In the first experiment an overall mean relative error (MRE) of 1.98% and a mean absolute error of 0.38 cm were obtained. Bland-Altman and correlation analyses were performed, obtaining a coefficient of determination equal to R2=.996. In the second experiment, sampling frequencies of 200 Hz, 100 Hz, and 66.6 Hz show similar performance with MRE below 3%. Slower sampling frequencies show an exponential increase in MRE. On both experiments, when dividing jump trials in different heights reached, a decrease in MRE with higher height trials suggests that the precision of the proposed system increases as height reached increases.
Conclusions
In the first experiment, we concluded that results between the proposed system and the reference are systematically the same. In the second experiment, the relevance of a sufficiently high sampling frequency is emphasized, especially for jump trials whose height is below 10 cm. For trials with heights above 30 cm, MRE decreases in general for all sampling frequencies, suggesting that at higher heights reached, the impact of high sampling frequencies is lesser.
Recording, modelling and understanding tactile interactions is important in the study of human behaviour and in the development of applications in healthcare and robotics. However, such studies remain challenging because existing wearable sensory interfaces are limited in terms of performance, flexibility, scalability and cost. Here, we report a textile-based tactile learning platform that can be used to record, monitor and learn human–environment interactions. The tactile textiles are created via digital machine knitting of inexpensive piezoresistive fibres, and can conform to arbitrary three-dimensional geometries. To ensure that our system is robust against variations in individual sensors, we use machine learning techniques for sensing correction and calibration. Using the platform, we capture diverse human–environment interactions (more than a million tactile frames) and show that the artificial-intelligence-powered sensing textiles can classify humans’ sitting poses, motions and other interactions with the environment. We also show that the platform can recover dynamic whole-body poses, reveal environmental spatial information and discover biomechanical signatures.
Background. Traditional monitoring of athletes during sports has long been hampered by bulky, complicated and tethered systems. In the past decade, this has changed due to the miniaturization of sensors and improvement of systems to store and transmit data. These systems have been integrated into textiles to create 'smart clothing' which has been so ubiquitous that a review of the recent literature is crucial for understanding its full potential and potential use in extreme sports. Methods. An electronic data base search was performed from 2003 to April 2019 for full length articles including "Smart" AND "Clothing" OR "Clothing" AND "Sport(s)" written in English with human subjects. Articles were evaluated according to the Newcas-tle-Ottawa Scale. Results. Twenty-four studies resulted in 18 systems comprised of 22 types of clothing with various capabilities, including: monitoring heart rate, electromyography, respiratory rate, steps, GPS, energy expenditure, posture, body temperature and identifying the activity. Conclusions. Many types of smart clothing from socks and gloves, to pants, shirts and bras are increasingly utilized to monitor sports activity worldwide and gather previously unavailable, yet highly valuable data. This provides a unique opportunity to study athletes during training and competition, potentially providing more effective training and better safety protocols.
Featured Application
The single-layer pressure sensor presented in this study serves as a stepping stone towards the development of cost-effective and scalable sensors for the industrial manufacturing process. The results show that the one-layer sensor has a high working performance and is thinner than multi-layer sensors. Thus, it is easy to apply it to robot control or human motion recognition fields.
Abstract
Today, e-textiles have become a fundamental trend in wearable devices. Fabric pressure sensors, as a part of e-textiles, have also received much interest from many researchers all over the world. However, most of the pressure sensors are made of electronic fibers and composed of many layers, including an intermediate layer for sensing the pressure. This paper proposes the model of a single layer pressure sensor with electrodes and conductive fibers intertwined. The plan dimensions of the fabricated sensors are 14 x 14 mm, and the thickness is 0.4 mm. The whole area of the sensor is the pressure-sensitive point. As expected, results demonstrate an electrical resistance change from 283 Ω at the unload pressure to 158 Ω at the load pressure. Besides, sensors have a fast response time (50 ms) and small hysteresis (5.5%). The hysteresis will increase according to the pressure and loading distance, but the change of sensor loading distance is very small. Moreover, the single-layer pressure sensors also show high durability under many working cycles (20,000 cycles) or washing times (50 times). The single-layer pressure sensor is very thin and more flexible than the multi-layer pressure sensor. The structure of this sensor is also expected to bring great benefits to wearable technology in the future.
Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material properties while applying the right amount of force—a challenging set of tasks for a modern robot1. Mechanoreceptor networks that provide sensory feedback and enable the dexterity of the human grasp2 remain difficult to replicate in robots. Whereas computer-vision-based robot grasping strategies3–5 have progressed substantially with the abundance of visual data and emerging machine-learning tools, there are as yet no equivalent sensing platforms and large-scale datasets with which to probe the use of the tactile information that humans rely on when grasping objects. Studying the mechanics of how humans grasp objects will complement vision-based robotic object handling. Importantly, the inability to record and analyse tactile signals currently limits our understanding of the role of tactile information in the human grasp itself—for example, how tactile maps are used to identify objects and infer their properties is unknown6. Here we use a scalable tactile glove and deep convolutional neural networks to show that sensors uniformly distributed over the hand can be used to identify individual objects, estimate their weight and explore the typical tactile patterns that emerge while grasping objects. The sensor array (548 sensors) is assembled on a knitted glove, and consists of a piezoresistive film connected by a network of conductive thread electrodes that are passively probed. Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135,000 frames, each covering the full hand, while interacting with 26 different objects. This set of interactions with different objects reveals the key correspondences between different regions of a human hand while it is manipulating objects. Insights from the tactile signatures of the human grasp—through the lens of an artificial analogue of the natural mechanoreceptor network—can thus aid the future design of prosthetics7, robot grasping tools and human–robot interactions1,8–10. Tactile patterns obtained from a scalable sensor-embedded glove and deep convolutional neural networks help to explain how the human hand can identify and grasp individual objects and estimate their weights.
We present RESi (Resistive tExtile Sensor Interfaces), a novel sensing approach enabling a new kind of yarn-based, resistive pressure sensing. The core of RESi builds on a newly designed yarn, which features conductive and resistive properties. We run a technical study to characterize the behaviour of the yarn and to determine the sensing principle. We demonstrate how the yarn can be used as a pressure sensor and discuss how specific issues, such as connecting the soft textile sensor with the rigid electronics can be solved. In addition, we present a platform-independent API that allows rapid prototyping. To show its versatility, we present applications developed with different textile manufacturing techniques, including hand sewing, machine sewing, and weaving. RESi is a novel technology, enabling textile pressure sensing to augment everyday objects with interactive capabilities.
Today, public displays are used to display general purpose information or advertisements in many public and urban spaces. In addition to that, research identified novel application scenarios for public displays. These scenarios, however, mainly include gesture- and posture-based interaction mainly relying on optical tracking. Deploying optical tracking systems in the real world is not always possible since real-world deployments have to tackle several challenges. These challenges include changing light conditions or privacy concerns. In this paper, we explore how smart fabric can detect the user's posture. We particularly focus on the user's arm posture and how this can be used for interacting with public displays. We conduct a preliminary study to record different arm postures, create a model to detect arm postures. Finally, we conduct an evaluation study using a simple game that uses the arm posture as input. We show that smart textiles are suitable to detect arm postures and feasible for this type of application scenarios.
Smartwatches provide quick and easy access to information. Due to their wearable nature, users can perceive the information while being stationary or on the go. The main drawback of smartwatches, however, is the limited input possibility. They use similar input methods as smartphones but thereby suffer from a smaller form factor. To extend the input space of smartwatches, we present GestureSleeve, a sleeve made out of touch enabled textile. It is capable of detecting different gestures such as stroke based gestures or taps. With these gestures, the user can control various smartwatch applications. Exploring the performance of the GestureSleeve approach, we conducted a user study with a running application as use case. In this study, we show that input using the GestureSleeve outperforms touch input on the smartwatch. In the future the GestureSleeve can be integrated into regular clothing and be used for controlling various smart devices.
There is a large class of routine physical exercises that are performed on the ground, often on dedicated "mats" (e.g. pushups, crunches, bridge). Such exercises involve coordinated motions of different body parts and are difficult to recognize with a single body worn motion sensors (like a step counter). Instead a network of sensors on different body parts would be needed, which is not always practicable. As an alternative we describe a cheap, simple textile pressure sensor matrix, that can be unobtrusively integrated into exercise mats to recognize and count such exercises. We evaluate the system on a set of 10 standard exercises. In an experiment with 7 subjects, each repeating each exercise 20 times, we achieve a user independent recognition rate of 82.5% and a user independent counting accuracy of 89.9%. The paper describes the sensor system, the recognition methods and the experimental results. Copyright
Sleep plays a pivotal role in the quality of life, and sleep posture is related to many medical conditions such as sleep apnea. In this paper, we design a dense pressure-sensitive bedsheet for sleep posture monitoring. In contrast to existing techniques, our bedsheet system offers a completely unobtrusive method using comfortable textile sensors. Based on high-resolution pressure distributions from the bedsheet, we develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. We run a pilot study and evaluate the performance of our methods with 14 subjects to analyze 6 common postures. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than state-of-the-art methods, achieving up to 83.0% precision and 83.2% recall on average.
For a clothing system, comfort is a fundamental necessity. In this paper, basic definitions and elements of clothing comfort and the general research trends were reviewed. In particular, understanding comfort of textile materials, its relevance to clothing choice and some assessment methods have been discussed. The impact of fabric and clothing attributes on clothing comfort was explored. Psychological, physical and physiological perceptions of clothing comfort were reviewed, including subjective and objective modes of assessment. A thorough discussion of handle comfort was presented, including assessment methods. Statistical presentations from selected comfort studies were also reviewed. Other sensory comfort properties particularly acoustic and appearance were also mentioned. From the aforementioned reviews, it was noted that the main focus for most researchers has been on sensorial and thermal comfort.
Practical Applications
To comfort researchers, this review gives resourceful insights relevant to subject selection and the general research trend on clothing comfort. For clothing manufacturers, the compiled consumer feedbacks by wearers of particular clothing are tools for product improvement to enhance utility. Finally, this review informs consumers about clothing comfort, which will contribute to informed decision making during purchasing.
The mechanical hand of the future will roll a screw between its fingers and sense, by touch, which end is which. This paper describes a step toward such a manipulator -- a robot finger that is used to recognize small objects by touch. The device incorporates a novel imaging tactile sensor -- an artificial skin with hundreds of pressure sensors in a space the size of a finger tip. The sensor is mounted on a tendon-actuated mechanical finger, similar in size and range of motion to a human index finger. A program controls the finger, using it to press and probe the object placed in front of it. Based on how the object feels, the program guesses its shape and orientation and then uses the finger to test and refine the hypothesis. The device is programmed to recognize commonly used fastening devices-nuts, bolts, flat washers, lock washers, dowel pins, cotter pins, and set screws.
Tactile sensation is important for human beings that equips the whole body surface. To understand what kind of force distribution our bodies might sense, we created a set of pressure sensing garment consisting of a sweater and trousers, that provides 1952 sensing points and covers 80 percent of the body surface evenly. As our skin works days and nights, an ideal pressure acquisition system for such purpose shall also feature both high temporal coverage and population coverage, casting simultaneous demands on wearability, durability, and affordability. Special cares were thus given to all design procedures, from material selection, sensor structure, and electronic driving architecture to garment design. The capability of this smart garment in obtaining rich information about both the wearer and the environment is then demonstrated, including and not limited to the recognition of postures, self-contacts, object contacts, and interactions.
We present an approach to develop seamless and scalable piezo-resistive matrix-based intelligent textile using digital flat-bed and circular knitting machines. By combining and customizing functional and common yarns, we can design the aesthetics and architecture and engineer both the electrical and mechanical properties of a sensing textile. By incorporating a melting fiber, we propose a method to shape and personalize three-dimensional piezo-resistive fabric structure that can conform to the human body through thermoforming principles. It results in a robust textile structure and intimate interfacing, suppressing sensor drifts and maximizing accuracy while ensuring comfortability. This paper describes our textile design, fabrication approach, wireless hardware system, deep-learning enabled recognition methods, experimental results, and application scenarios. The digital knitting approach enables the fabrication of 2D to 3D pressure-sensitive textile interiors and wearables, including a 45 x 45 cm intelligent mat with 256 pressure-sensing pixels, and a circularly-knitted, form-fitted shoe with 96 sensing pixels across its 3D surface both with linear piezo-resistive sensitivity of 39.4 for up to 500 N load. Our personalized convolutional neural network models are able to classify 7 basic activities and exercises and 7 yoga poses in-real time with 99.6% and 98.7% accuracy respectively. Further, we demonstrate our technology for a variety of applications ranging from rehabilitation and sport science, to wearables and gaming interfaces.
While sports activity recognition is a well studied subject in mobile, wearable and ubiquitous computing, work to date mostly focuses on recognition and counting of specific exercise types. Quality assessment is a much more difficult problem with significantly less published results. In this work, we present Quali-Mat: a method for evaluating the quality of execution (QoE) in exercises using a smart sports mat that can measure the dynamic pressure profiles during full-body, body-weight exercises. As an example, our system not only recognizes that the user is doing push-ups, but also distinguishes 5 subtly different types of push-ups, each of which (according to sports science literature and professional trainers) has a different effect on different muscle groups. We have investigated various machine learning algorithms targeting the specific type of spatio-temporal data produced by the pressure mat system. We demonstrate that computationally efficient, yet effective Conv3D model outperforms more complex state-of-the-art options such as transfer learning from the image domain. The approach is validated through an experiment designed to cover 47 quantifiable variants of 9 basic exercises with 12 participants. Overall, the model can categorize 9 exercises with 98.6% accuracy / 98.6% F1 score, and 47 QoE variants with 67.3% accuracy / 68.1% F1 score. Through extensive discussions with both the experiment results and practical sports considerations, our approach can be used for not only precisely recognizing the type of exercises, but also quantifying the workout quality of execution on a fine time granularity. We also make the Quali-Mat data set available to the community to encourage further research in the area.
Considering the fast development of wearable electronics and soft robotics, pressure sensors with high sensitivity, durability, and washability are of great importance. However, the surface modification of fabrics with high-sensitivity active materials requires that issues associated with poor interface adhesion and stability are resolved. In this study, we explored the key factors for firmly bonding MXene to fabric substrates to fabricate wearable and washable pressure sensing fabric. The interactions between MXene and various fabrics were elucidated by investigating the adsorption and binding capacities. The natural rough surface of cotton fibres promoted the firm adsorption of MXene. As a result, MXene was difficult to detach, even using mechanical washing and ultrasonic treatment. Further, the abundant functional groups on the MXene surface were conducive to interfacial interactions with cotton fibres. An increase in the amount of fluorine-containing functional groups also improved the hydrophobicity of the fabric surface. The good force-sensitive resistance of MXene-coated cotton allowed this pressure-sensing fabric to function as a flexible pressure sensor, which showed a high gauge factor (7.67 kPa-1), a rapid response and relaxation speed (<35 ms), excellent stability (>2000 cycles), and good washing durability. Further, the as-fabricated flexible pressure sensor was demonstrated as a wearable human-machine interface that supported multi-touch interactions and exhibited a rapid response. Thus, this work provides a new approach for developing next-generation high-sensitivity wearable pressure sensors.
Tactile sensing is essential for skilled manipulation and object perception. Existing sensing devices cannot capture the full range of tactile information in the naturally behaving hand, and are unable to match human abilities of perception and action. Human touch sensing is mediated via contact-generated mechanical signals in the skin. Time varying contacts elicit propagating mechanical waves that are captured via numerous vibration-sensitive neurons distributed throughout the hand, yielding a wealth of sensory information. Little engineering attention has been given to important biological sensing system. Inspired by human sensing abilities, we present a wearable system based on a 126 channel sensor array capable of capturing high resolution tactile signals throughout the hand during natural manual activities. It employs a network of miniature threeaxis sensors mounted on a flexible circuit whose geometry is adapted to the anatomy of the hand, allowing tactile data to be captured during natural manual interactions. Each sensor possesses a frequency bandwidth overlapping the entire human tactile frequency range. Data is acquired in real time via a custom FPGA and an I2C network. We also present physiologically informed signal processing methods for reconstructing whole hand tactile signals. We report experiments that demonstrate the utility of this system for collecting rich tactile signals during manual interactions.
Highly sensitive wearable textile pressure sensors represent the key components of smart textiles and personalized electronics, with potential applications in biomedical monitoring, electronic skin, and human-machine interfacing. Here, we present a simple and low-cost strategy to fabricate highly sensitive wearable textile pressure sensors for non-invasive human motion and physiological signal monitoring and the detection of dynamic tactile stimuli. The wearable textile sensor was woven using a one-dimensional (1D) weavable core-sheath nanofiber yarn, which was obtained by coating a Ni-coated cotton yarn electrode with carbon nanotube (CNT)-embedded polyurethane (PU) nanofibers using a simple electrospinning technique. In our design, the three-dimensional elastic porous nanofiber structure of the force-sensing layer and hierarchical fiber-bundled structure of the conductive Ni-coated electrode provide the sensor with a relatively large surface area, and a sufficient surface roughness and elasticity. This leads to rapid and sharp increases in the contact area under stimuli with low external pressure. As a result, the textile pressure sensor exhibits the advantages of a high sensitivity (16.52 N-1), wide sensing range (0.003-5 N), and short response time (~0.03 s). Owing to these merits, our textile-based sensor can be directly attached to the skin as usual and conformally fit the shape deformations of the body's complex flexible curved surfaces. This contributes to the reliable real-time monitoring of human movements, ranging from subtle physiological signals to vigorous movements. Moreover, a large-area textile sensing matrix is successfully fabricated for tactile mapping of spatial pressure by being worn on the surface of wrist, highlighting the tremendous potential for applications in smart textiles and wearable electronics.
This work presents a novel interactive system for simple garment composition and surface patterning. Our approach makes it easier for casual users to customize machine-knitted garments, while enabling more advanced users to design their own composable templates. Our tool combines ideas from CAD software and image editing: it allows the composition of (1) parametric knitted primitives, and (2) stitch pattern layers with different resampling behaviours. By leveraging the regularity of our primitives, our tool enables interactive customization with automated layout and real-time patterning feedback. We show a variety of garments and patterns created with our tool, and highlight our ability to transfer shape and pattern customizations between users.
With recent interest in shape-changing interfaces, material-driven design, wearable technologies, and soft robotics, digital fabrication of soft actuatable material is increasingly in demand. Much of this research focuses on elastomers or non-stretchy air bladders. Computationally-controlled machine knitting offers an alternative fabrication technology which can rapidly produce soft textile objects that have a very different character: breathable, lightweight, and pleasant to the touch. These machines are well established and optimized for the mass production of garments, but compared to other digital fabrication techniques such as CNC machining or 3D printing, they have received much less attention as general purpose fabrication devices. In this work, we explore new ways to employ machine knitting for the creation of actuated soft objects. We describe the basic operation of this type of machine, then show new techniques for knitting tendon-based actuation into objects. We explore a series of design strategies for integrating tendons with shaping and anisotropic texture design. Finally, we investigate different knit material properties, including considerations for motor control and sensing.
Pressure senor array is widely used in gait analysis and robotics. To meet the demand on different sizes and reduce the manufacture costs, in this paper, we proposed a design method for customizable pressure sensor array which could be trimmed into different sizes. The customizable pressure sensor array was made of piezo-resistive fabric and flexible circuits, which made it suitable for the sensor array to be trimmed into different sizes. Pressure sensors were uniformly distributed to maintain the resolution of the pressure sensor array when trimmed into different sizes. Wires on the flexible circuits were designed to ensure that all the full/partial sensors remained on the sensor array could be used after trimming the sensor array into a smaller size. To evaluate the design method, we designed insole shaped customizable pressure sensor arrays to analyze gait parameters such as gait cycle and cadence. Spatial and temporal pressure distribution patterns acquired with trimmed and nontrimmed sensor arrays were used for analyzing. Experiment results indicated that the customizable pressure sensor array designed with the method proposed in this paper could be used to acquire spatial and temporal pressure distribution patterns without being influenced by trimming.
Over the last decades, there have been numerous efforts in wearable computing research to enable interactive textiles. Most work focus, however, on integrating sensors for planar touch gestures, and thus do not fully take advantage of the flexible, deformable and tangible material properties of textile. In this work, we introduce SmartSleeve, a deformable textile sensor, which can sense both surface and deformation gestures in real-time. It expands the gesture vocabulary with a range of expressive interaction techniques, and we explore new opportunities using advanced deformation gestures, such as, Twirl, Twist, Fold, Push and Stretch. We describe our sensor design, hardware implementation and its novel non-rigid connector architecture. We provide a detailed description of our hybrid gesture detection pipeline that uses learning-based algorithms and heuristics to enable real-time gesture detection and tracking. Its modular architecture allows us to derive new gestures through the combination with continuous properties like pressure, location, and direction. Finally, we report on the promising results from our evaluations which demonstrate real-time classification.
Industrial knitting machines can produce finely detailed, seamless, 3D surfaces quickly and without human intervention. However, the tools used to program them require detailed manipulation and understanding of low-level knitting operations. We present a compiler that can automatically turn assemblies of high-level shape primitives (tubes, sheets) into low-level machine instructions. These high-level shape primitives allow knit objects to be scheduled, scaled, and otherwise shaped in ways that require thousands of edits to low-level instructions. At the core of our compiler is a heuristic transfer planning algorithm for knit cycles, which we prove is both sound and complete. This algorithm enables the translation of high-level shaping and scheduling operations into needle-level operations. We show a wide range of examples produced with our compiler and demonstrate a basic visual design interface that uses our compiler as a backend.
Thermophysiological wear comfort concerns with the heat and moisture transport properties of clothing and the way it helps the clothing to maintain the heat balance of the body during various level of activity. Heat and moisture flow through clothing is a complex phenomenon. So, heat and moisture transfer analysis for clothing is an important issue for researchers. This article delves into the processes which are involved in heat and moisture transmission along with mathematical models of heat, liquid and vapour transport through clothing to understand the exact phenomena of heat and moisture transmission. The reported testing methods and parameters used for determining heat and moisture are also summarized in this article. This article also describes the need of heat and moisture transmission in clothing, desired attributes for heat and moisture management and parameters affecting heat and moisture transmission in clothing.
Project Jacquard presents manufacturing technologies that enable deploying invisible ubiquitous interactivity at scale. We propose novel interactive textile materials that can be manufactured inexpensively using existing textile weaving technology and equipment.
The development of touch-sensitive textiles begins with the design and engineering of a new highly conductive yarn. The yarns and textiles can be produced by standard textile manufacturing processes and can be dyed to any color, made with a number of materials, and designed to a variety of thicknesses and textures to be consistent with garment designers' needs.
We describe the development of yarn, textiles, garments, and user interactivity; we present the opportunities and challenges of creating a manufacturable interactive textile for wearable computing.
We report on the fabrication and characterization of a large area (handkerchief-size, i.e., >16 cm × 16 cm) of pressure sensor fabric. The sensor is constructed by weaving the fibers, which are coated with the organic conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and a dielectric film of perfluoropolymer (Cytop), and the capacitances at the points of the crossed fibers vary with the applied force. The functional films are continuously coated with die-coating system with the thickness ranging from hundreds nm to several μm. The resultant fibers are loomed, forming the sensor fabric with an area of 16 cm × 16 cm. Its sensitivity ranged from 0.98 to 9.8 N/cm2, which is within the range of human touch. Therefore, our fabric touch sensors could lead to applications from wearable keyboards to sensors embedded in beds for health care purposes.
A lying person's motion tracking system by using a pressure distribution image and a full body model is proposed. The full body model consists of a skeleton and a surface model to cope with a variety of body shapes. BVH files are used as the skeleton model that describes a hierarchy of joints and links. Wavefront object files are used as the surface model that describes geometry of the surface. The bed has 210 pressure sensors that are under the mattress. It can measure a pressure distribution image of a lying person. The lying person's motion is tracked by considering potential energy, momentum and a difference between the measured pressure distribution image and a pressure distribution image that is calculated by the full body model. Experimental results reveal that the realized system can track not only horizontal motions such as opening and closing legs but also vertical motions such as raising the upper body.
A new type of tactile sensor using pressure-conductive rubber with stitched electrical wires is presented. The sensor is thin and flexible and can cover three-dimensional objects. Since the sensor adopts a single-layer composite structure, the sensor is durable with respect to external force. In order to verify the effectiveness of this tactile sensor, we performed an experiment in which a four-fingered robot hand equipped with tactile sensors grasped sphere and column. The sensor structure, electrical circuit, and characteristics are described. The sensor control system and experimental results are also described.