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

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review

The emergence of physiological monitoring technologies has produced exceptional opportunities for real-time collection and analysis of workers' physiological information. To benefit from these safety and health prognostic opportunities, research efforts have explored the applicability of these devices to control workers' wellbeing levels during occupational activities. A systematic review is proposed to summarise up-to-date progress in applying physiological monitoring systems for occupational groups. Adhering with the PRISMA Statement, five databases were searched from 2014 to 2021, and 12 keywords were combined, concluding with the selection of 38 articles. Sources of risk of bias were assessed regarding randomisation procedures, selective outcome reporting and generalisability of results. Assessment procedures involving non-invasive methods applied with health and safety-related goals were filtered. Working-age participants from homogeneous occupational groups were selected, with these groups primarily including firefighters and construction workers. Research objectives were mainly directed to assess heat stress and physiological workload demands. Heart rate related variables, thermal responses and motion tracking through accelerometry were the most common approaches. Overall, wearable sensors proved to be valid tools for assessing physiological status in working environments. Future research should focus on conducting sensor fusion assessments, engaging wearables in real-time evaluation methods and giving continuous feedback to workers and practitioners.

Combining Ergonomic Risk Assessment (RULA) with Inertial Motion Capture Technology in Dentistry-Using the Benefits from Two Worlds

Traditional ergonomic risk assessment tools such as the Rapid Upper Limb Assessment (RULA) are often not sensitive enough to evaluate well-optimized work routines. An implementation of kinematic data captured by inertial sensors is applied to compare two work routines in dentistry. The surgical dental treatment was performed in two different conditions, which were recorded by means of inertial sensors (Xsens MVN Link). For this purpose, 15 (12 males/3 females) oral and maxillofacial surgeons took part in the study. Data were post processed with costume written MATLAB^® routines, including a full implementation of RULA (slightly adjusted to dentistry). For an in-depth comparison, five newly introduced levels of complexity of the RULA analysis were applied, i.e., from lowest complexity to highest: (1) RULA score, (2) relative RULA score distribution, (3) RULA steps score, (4) relative RULA steps score occurrence, and (5) relative angle distribution. With increasing complexity, the number of variables times (the number of resolvable units per variable) increased. In our example, only significant differences between the treatment concepts were observed at levels that are more complex: the relative RULA step score occurrence and the relative angle distribution (level 4 + 5). With the presented approach, an objective and detailed ergonomic analysis is possible. The data-driven approach adds significant additional context to the RULA score evaluation. The presented method captures data, evaluates the full task cycle, and allows different levels of analysis. These points are a clear benefit to a standard, manual assessment of one main body position during a working task.

Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.

A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

Work-related musculoskeletal disorders are a major concern globally affecting societies, companies, and individuals. To address this, a new sensor-based system is presented: the Smart Workwear System, aimed at facilitating preventive measures by supporting risk assessments, work design, and work technique training. The system has a module-based platform that enables flexibility of sensor-type utilization, depending on the specific application. A module of the Smart Workwear System that utilizes haptic feedback for work technique training is further presented and evaluated in simulated mail sorting on sixteen novice participants for its potential to reduce adverse arm movements and postures in repetitive manual handling. Upper-arm postures were recorded, using an inertial measurement unit (IMU), perceived pain/discomfort with the Borg CR10-scale, and user experience with a semi-structured interview. This study shows that the use of haptic feedback for work technique training has the potential to significantly reduce the time in adverse upper-arm postures after short periods of training. The haptic feedback was experienced positive and usable by the participants and was effective in supporting learning of how to improve postures and movements. It is concluded that this type of sensorized system, using haptic feedback training, is promising for the future, especially when organizations are introducing newly employed staff, when teaching ergonomics to employees in physically demanding jobs, and when performing ergonomics interventions.

A computer vision approach for classifying isometric grip force exertion levels

Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited by subjectivity. In this work, computer vision techniques are developed to detect isometric grip exertions using facial videos and wearable photoplethysmogram. Eighteen participants (19-24 years) performed isometric grip exertions at varying levels of maximum voluntary contraction. Novel features that predict forces were identified and extracted from video and photoplethysmogram data. Two experiments with two (High/Low) and three (0%MVC/50%MVC/100%MVC) labels were performed to classify exertions. The Deep Neural Network classifier performed the best with 96% and 87% accuracy for two- and three-level classifications, respectively. This approach was robust to leave subjects out during cross-validation (86% accuracy when 3-subjects were left out) and robust to noise (i.e. 89% accuracy for correctly classifying talking activities as low force exertions). Practitioner summary: Forceful exertions are contributing factors to musculoskeletal injuries, yet it remains difficult to measure in work environments. This paper presents an approach to estimate force exertion levels, which is less distracting to workers, easier to implement by practitioners, and could potentially be used in a wide variety of workplaces. Abbreviations: MSD: musculoskeletal disorders; ACGIH: American Conference of Governmental Industrial Hygienists; HAL: hand activity level; MVC: maximum voluntary contraction; PPG: photoplethysmogram; DNN: deep neural networks; LOSO: leave-one-subject-out; ROC: receiver operating characteristic; AUC: area under curve.

Modelling performance during repetitive precision tasks using wearable sensors: a data-driven approach

In modern manufacturing systems, especially assembly lines, human input is a critical resource to provide dexterity and flexibility. However, the repetitive precision tasks common in assembly lines can have adverse effects on workers and overall system performance. We present a data-driven approach to evaluating task performance using wearable sensor data (kinematics, electromyography and heart rate). Eighteen participants (gender-balanced) completed repeated cycles of maze tracking and assembly/disassembly. Various combinations of input data types and classification algorithms were used to model task performance. The use of the linear discriminant analysis (LDA) algorithm and kinematic data provided the most promising classification performance. The highest model accuracy was found using the LDA algorithm and all data types, with respective levels of 62.4, 88.6, 85.8 and 94.1% for predicting maze errors, maze speed, assembly/disassembly errors and assembly/disassembly speed. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly-lines or similar industries. Practitioner summary: This paper proposed models the repetitive precision task performance using data collected from wearable sensors. The use of the LDA algorithm and kinematic data provided the most promising classification performance. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly lines or similar industries. Abbreviations: AD: anterior deltoid; BB: biceps brachii; ECR: extensor carpi radialis; ECU: extensor carpi ulnaris; FCR: flexor carpi radialis; FCU: flexor carpi ulnaris; FN: false negatives; FP: false positives; HR: heart rate; HRR: heart rate reserve; IMUs: inertial measurement units; kNN: k-nearest neighbors; LDA: linear discriminant analysis; MD: medial deltoid; MF: median power frequency; MNF: mean power frequency; MVIC: maximum voluntary isometric contraction; nRMS: normalized root-mean-square amplitudes; PD: posterior deltoid; RandFor: random forests; RHR: resting heart rate; RMS: root-mean-square amplitudes; sEMG: surface electromyographic; SVM: support vector machines; TB: triceps brachii medial; TN: true negatives; TP: true positives; t-SNE: t-distributed Stochastic Neighbor Embedding; UT: upper trapezius.

Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People

Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors

Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient's pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.

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.