Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions

Conventional snap fasteners used in clothing are often used as electrical connectors in e-textile and wearable applications for signal transmission due to their wide availability and ease of use. Nonetheless, limited research exists on the validation of these fasteners, regarding the impact of contact-induced high-amplitude artefacts, especially under motion conditions. In this work, three types of fasteners were used as electromechanical connectors, establishing the interface between a regular sock and an acquisition device. The tested fasteners have different shapes and sizes, as well as have different mechanisms of attachment between the plug and receptacle counterparts. Experimental evaluation was performed under static conditions, slow walking, and rope jumping at a high cadence. The tests were also performed with a test mass of 140 g. Magnetic fasteners presented excellent electromechanical robustness under highly dynamic human movement with and without the additional mass. On the other hand, it was demonstrated that the Spring snap buttons (with a spring-based engaging mechanism) presented a sub-optimal performance under high motion and load conditions, followed by the Prong snap fasteners (without spring), which revealed a high susceptibility to artefacts. Overall, this work provides further evidence on the importance and reliability of clothing fasteners as electrical connectors in wearable systems.

Evaluating Lumbar Shape Deformation With Fabric Strain Sensors

OBJECTIVE

To better study human motion inside the space suit and suit-related contact, a multifactor statistical model was developed to predict torso body shape changes and lumbar motion during suited movement by using fabric strain sensors that are placed on the body.

BACKGROUND

Physical interactions within pressurized space suits can pose an injury risk for astronauts during extravehicular activity (EVA). In particular, poor suit fit can result in an injury due to reduced performance capabilities and excessive body contact within the suit during movement. A wearable solution is needed to measure body motion inside the space suit.

METHODS

An array of flexible strain sensors was attached to the body of 12 male study participants. The participants performed specific static lumbar postures while 3D body scans and sensor measurements were collected. A model was created to predict the body shape as a function of sensor signal and the accuracy was evaluated using holdout cross-validation.

RESULTS

Predictions from the torso shape model had an average root mean square error (RMSE) of 2.02 cm. Subtle soft tissue deformations such as skin folding and bulges were accurately replicated in the shape prediction. Differences in posture type did not affect the prediction error.

CONCLUSION

This method provides a useful tool for suited testing and the information gained will drive the development of injury countermeasures and improve suit fit assessments.

APPLICATION

In addition to space suit design applications, this technique can provide a lightweight and wearable system to perform ergonomic evaluations in field assessments.

Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning

The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.

The KF-SVM-based fusion method for multi sensor uncertain system with correlated noise

For multi-sensor target tracking system, the accurate state estimation is obtained under the condition that the system model is unbiased and the noise disturbance satisfies the characteristics of independent Gaussian white noise. However, in engineering practice, it is almost impossible to get the accurate system model and also the system noise is non-independent Gaussian white noise. So the traditional state estimation methods are not suitable for uncertainty system with non Gaussian white noise. In this paper, the Kalman Filter-Support Vector Machine (KF-SVM) based data fusion algorithm is proposed for system with model uncertainty and correlated noise. Firstly, the state pre-estimates are calculated by the proposed improved Kalman Filter for single sensor system. Then, the state estimation is obtained via proposed KF-SVM data fusion algorithm for multi-sensor system. Finally, compared with the traditional algorithms, the simulation results show that the proposed fusion algorithm based on KF-SVM does not need to calculate the sensor cross-covariance matrix and has better estimation accuracy.

Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.

Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device

Textile sensors have gained attention for wearable devices, in which the most popular are the resistive textile sensor. However, these sensors present high hysteresis and a drift when stretched for long periods of time. Inductive textile sensors have been commonly used as antennas and plethysmographs, and their applications have been extended to measure heartbeat, wireless data transmission, and motion and gesture capturing systems. Inductive textile sensors have shown high reliability, stable readings, low production cost, and an easy manufacturing process. This paper presents the design and validation of an inductive strain textile sensor. The anthropometric dimensions of a healthy participant were used to define the maximum dimensions of the inductive textile sensor. The design of the inductive sensor was studied through theoretical calculations and simulations. Parameters such as height, width, area, perimeter, and number of complete loops were considered to calculate and evaluate the inductance value.

Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods.

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features-i.e., spatio-temporal features, energy-based features, shape based angular and geometric features-and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man-machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

Lumbar posture assessment with fabric strain sensors

Astronauts are at risk for low back pain and injury during extravehicular activity because of the deconditioning of the lumbar region and biomechanical demands associated with wearing a spacesuit. To understand and mitigate injury risks, it is necessary to study the lumbar kinematics of astronauts inside their spacesuit. To expand on previous efforts, the purpose of this study was to develop and test a generalizable method to assess complex lumbar motion using 10 fabric strain sensors placed on the torso. Anatomical landmark positions and corresponding sensor measurements were collected from 12 male study participants performing 16 static lumbar postures. A multilayer principal component and regression-based model was constructed to estimate lumbar joint angles from the sensor measurements. Good lumbar joint angle estimation was observed (<9° mean error) from flexion and lateral bending joint angles, and lower accuracy (13.7° mean error) was observed from axial rotation joint angles. With continued development, this method can become a useful technique for measuring suited lumbar motion and could potentially be extrapolated to civilian work applications.

Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor

Low back pain (LBP) is the most common work-related musculoskeletal disorder among healthcare workers and is directly related to long hours of working in twisted/bent postures or with awkward trunk movements. It has already been established that providing relevant feedback helps individuals to maintain better body posture during the activities of daily living. With the goal of preventing LBP through objective monitoring of back posture, this paper proposes a wireless, comfortable, and compact textile-based wearable platform to track trunk movements when the user bends forward. The smart garment developed for this purpose was prototyped with an inductive sensor formed by sewing a copper wire into an elastic fabric in a zigzag pattern. The results of an extensive simulation study showed that this unique design increases the inductance value of the sensor, and, consequently, improves its resolution. Furthermore, experimental evaluation on a healthy participant confirmed that the proposed wearable system with the suggested sensor design can easily detect forward bending movements. The evaluation scenario was then extended to also include twisting and lateral bending of the trunk, and it was observed that the proposed design can successfully discriminate such movements from forward bending of the trunk. Results of the magnetic interference test showed that, most notably, moving a cellphone towards the unworn prototype affects sensor readings, however, manipulating a cellphone, when wearing the prototype, did not affect the capability of the sensor in detecting forward bends. The proposed platform is a promising step toward developing wearable systems to monitor back posture in order to prevent or treat LBP.

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Application-Based Production and Testing of a Core-Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles

Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core–sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor’s piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities.

Classifying Diverse Physical Activities Using "Smart Garments"

Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.

Using smart garments to differentiate among normal and simulated abnormal gaits

Detecting and assessing an individual's gait can be important for medical diagnostic purposes and for developing and guiding follow-on rehabilitation protocols. Thus, an accurate, objective gait classification system has the potential to facilitate earlier diagnosis and improved clinical decision-making. Systems using smart garments represent an emerging technology for physical activity assessment and that may be relevant for gait classification. The objective of this study was to assess the accuracy of one such system - comprised of commercial instrumented socks and a custom instrument shirt - for differentiating among normal gait and four distinct simulated gait abnormalities. Eleven participants completed an experiment in which they completed several gait trails on a single day. Gait types were classified using diverse modeling approaches (K-nearest neighbors, linear discriminant analyses, support vector machines, and artificial neural networks). High classification accuracy could be obtained, both when classification models were developed and tested using data from each participant separately and grouped together, particularly using the k-nearest neighbor method (>98% accuracy). Some gaits were more often "confused" with other gaits, especially when they shared underlying kinematic aspects. These results support the potential of using "smart" garments for detecting and identifying abnormal gaits, and for future implementation in diagnosis and rehabilitation.

Using a smart textile system for classifying occupational manual material handling tasks: evidence from lab-based simulations

Physical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile systems (STSs). Our goal in this exploratory study was to assess the feasibility and accuracy of using two STSs to classify several manual material handling (MMH) tasks. Specifically, commercially-available 'smart' socks and a custom 'smart' shirt were used individually and in combination. Eleven participants simulated nine separate MMH tasks while wearing the STSs, and task classification accuracy was quantified subsequently using several common models. The shirt and socks, both individually and in combination, could classify the simulated tasks with greater than 97% accuracy. Thus, using STSs appears to have potential utility for discriminating occupational physical tasks in the work environment. Practitioner summary: A smart textile system could classify diverse MMH tasks with high accuracy. This technology may help in developing future ergonomic exposure assessment systems, with the goal of preventing occupational injuries.

Sharif-Human movement instrumentation system (SHARIF-HMIS): Development and validation

The interest in wearable systems among the biomedical engineering and clinical community continues to escalate as technical refinements enhance their potential use for both indoor and outdoor applications. For example, an important wearable technology known as a microelectromechanical system (MEMS) is demonstrating promising applications in the area of biomedical engineering. Accordingly, this study was designed to investigate the Sharif-Human Movement Instrumentation System (SHARIF-HMIS), consisting of inertial measurement units (IMUs), stretchable clothing, and a data logger-all of which can be used outside the controlled environment of a laboratory, thus enhancing its overall utility. This system is lightweight, portable, able to be deliver data for almost 10 h, and features a new data-fusion algorithm using the Kalman filter with an adaptive approach. In specific terms, the data from the system's gyroscope, accelerometer, and magnetometer sensors can be combined to estimate total-body orientation; additionally, the noise level of these sensors can be changed to accommodate faster motions as well as magnetic disturbances. These variations can be incorporated within the extended Kalman filter by changing the parameters of the filter adaptively. In specific terms, the system's interface was developed to acquire data from eighteen IMUs located on the body to collect kinematic data associated with human motion. Meanwhile, a validation test involving one subject performing different shoulder motions was designed to compare data captured by SHARIF-HMIS and the VICON motion-capture system. This validation test demonstrated correlation values of >0.9. Results also confirmed that the output accuracy of the new system's sensor was <0.55, 1.5 and 3.5° for roll, pitch, and yaw directions, respectively. In summary, SHARIF-HMIS successfully collected kinematic data for specific human movements, which has promising implications for a range of sporting, biomedical, and healthcare-related applications.

Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring

Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.