Development and Assessment of Smart Textile Systems for Human Activity Classification

Wearable sensors and systems have become increasingly popular for diverse applications. An emerging technology for physical activity assessment is Smart Textile Systems (STSs), comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronics, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. Thus, STSs have clear potential utility for measuring health-relevant aspects of human activity, and to do so passively and continuously in diverse environments. For these reasons, STSs have received increasing interest in recent studies. Despite this, however, limited evidence exists to support the implementation of STSs during diverse applications.

Our long-term goal was to assess the feasibility and accuracy of using an STS to monitor human activities. Our immediate objective was to investigate the accuracy of an STS in three representative applications with respect to occupational scenarios, healthcare, and activities of daily living. A particular STS was examined, consisting of a smart socks (SSs), using textile pressure sensors, and smart undershirt (SUS), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Thus, five studies were completed to design and evaluate the usability of the smart undershirt, and investigate the accuracy of implementing an STS in the noted applications. Input from the SUS led to planar angle estimations with errors on the order of 1.3 and 9.4 degrees for the low-back and shoulder, respectively. Overall, individuals preferred wearing a smart textile system over an IMU system and indicated the former as superior in several aspects of usability. In particular, the short-sleeved T-shirt was the most preferred garments for an STS. Results also indicated that the smart shirt and smart socks, both individually and in combination, could detect occupational tasks, abnormal and normal gaits, and activities of daily living with greater than 97% accuracy.

Based on our findings, we hope to facilitate future work that more effectively quantifies sedentary periods that may be deleterious to human health, as well as detect activity types that may be help or hinder health and fitness. Such information may be of use to individuals and workers, healthcare providers, and ergonomists. More specifically, further analyses from this investigation could provide strategies for: (a) modifying a sedentary lifestyle or work scenario to a more active one, and (b) helping to more accurately identify occupational injury risk factors associated with human movement.