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

Abstract

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.

Keywords:

• Smart shirt
• smart socks
• wearable sensor
• exposure assessment
• manual material handling

Acknowledgements

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.

Disclosure statement

No potential conflict of interest was reported by the authors.