Detecting Activities Exposing Construction Workers to the Risk of Developing Carpal Tunnel Syndrome

Abstract

Carpal tunnel syndrome (CTS) is a repetitive-motion injury that occurs when the median nerve is regularly compressed or squeezed. CTS causes pain, numbness, tingling, and weakness in the hand and wrist, which can affect workers' ability to safely perform their job tasks. The National Institute for Occupational Safety and Health (NIOSH) recognizes that construction workers are at high risk for developing CTS due to the repetitive motions, awkward working posture, and forceful exertions required in many tasks. Specifically, overhead working postures and sustained postures with the wrist bent are the major causal factors of CTS among construction workers. However, to date, there has been little discussion about assessing the risk of CTS among construction workers. To this end, this study explores an approach to detect activities exposing workers to the risk of developing CTS by assessing workers' hand movements. The inertial measurement unit sensors were attached to a participant's wrist. The convolutional neural network-based approach was adopted to classify workers' postures and activities. The result validates the feasibility of assessing the development of construction workers' CTS and provides a foundation for the implementation of ergonomic interventions to reduce the risk of CTS.