Detection of Occupational Physical Activities using a Smart Textile System

Abstract

High-risk occupational physical activities (OPAs) may adversely impact the physical health of workers (Valero, Sivanathan, Bosché, & Abdel-Wahab, 2016). For example, adverse OPA-related outcomes are linked to repetitive physical activities or postures that target certain muscle groups or anatomical areas, and a sedentary workstyle that appears to be contributing to work-related musculoskeletal disorders (WMSDs), including back pain, spinal disorders, and joint complaints (Smith et al., 2016). To offset the risk for the range of disorders noted above, there is a need to identify occupational tasks and to detect high-risk OPAs in the workplace. For these purposes, activity monitoring represents a potentially effective approach. Physical monitoring systems are potential assessment devices to detect undesired or high-risk occupational activities during the workday. A promising area of research, and a new alternative for detecting and monitoring a range of OPAs, is smart textile system (STS). STSs are comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronic boards, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. In this study, our goal was to assess the feasibility and accuracy of using an STS to monitor several occupational activities. A particular STS was examined, consisting of a commercially-available system (i.e., “smart” socks), using textile pressure sensors, and a custom smart shirt (Mokhlespour Esfahani & Nussbaum, (Under review)), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Nine manual material handling and lifting tasks were simulated in a lab, representing common occupational tasks (e.g., as performed by a delivery driver) that involve some variations of manual box lifting, carrying, pushing, pulling, and reaching: Activity 1 = Squat lifting of a box from mid-shank height to elbow height and lowering; Activity 2 = Stoop lifting a box from mid-shank height to elbow height and lowering; Activity 3 = Semi-squat lifting a box from mid-shank height to elbow height and lowering; Activity 4 = Lifting and turning a box from the table on the right side and inversely; Activity 5 = Carrying a box from one table to another (3 meters); Activity 6 = Turning and placing a box on the table on the left side and inversely; Activity 7 = Pushing a box away from the body over a distance = 70 cm and inversely; Activity 8 = Pulling a box toward the body over 70 cm and inversely; Activity 9 = Lifting a box from elbow height to overhead height and inversely. Eleven participants performed these tasks while wearing the STS. Task classification accuracy using data from the STS was determined using several methods. We implemented three of the more popular algorithms – k-nearest (k-NN), support vector machine (SVM), and artificial neural network (ANN) – and then compared their relative performance. The classification algorithms were implemented both for each participant (individual-level) and for all 11 participants (group-level). Classification models were also developed separate for the shirt only, socks only, and the complete STS. Global accuracy at the group level using k-NN, SVM, and ANN respectively ranged from ~ 95 to 99%, from ~ 29 to 97%, and from ~ 89 to 97%. However, all algorithms performed very well, and comparably, at the individual- level. According to the results, using data from either the shirt or socks yielded similar task classification performance, in terms of global accuracy, using K-NN and ANN, though using a SVM generated relatively poor performance with the data from the shirt. Nevertheless, other performance criteria indicated that the shirt had better performance than the smart socks (e.g., in terms of sensitivity, specificity, and precision). Both the smart shirt and socks, and their combination, could detect occupational tasks with better than 97% accuracy. Our results support the feasibility of using an STS for identifying occupational tasks among jobs that require lifting, carrying, reaching, pulling, and pushing. However, some limitations should be addressed. First, our participant sample involved healthy, young volunteers; the results obtained thus may not be generalizable to an older population or those with medical conditions. Second, the occupational activities simulated here only involved manual material handling, and did not cover all common workplace activities. Therefore, the extent to which results of this study can be generalized to a wider set of occupational activities is limited. Third, the current investigation relied on standardized activities in a laboratory setting; the performance of an STS in real workplaces may be less accurate. Fourth, the current investigation did not address the issue of data segmentation, or the need to identify the initiation and termination times of a given task. Based on our findings, however, we hope to facilitate future work that more effectively detects additional occupational activity types that may be help or hinder health and fitness. Such information will likely be of use to both workers and ergonomists. More specifically, results from future investigation may provide strategies for helping to more accurately identify occupational injury risk factors associated with human movement.

RibbedTee (Nevada, USA) kindly donated its products for developing the SUS. The first author was supported by a fellowship from the United Parcel Service (UPS); any opinions expressed here do not necessarily represent those of UPS.