earticle

영어

A lot of construction works are done by manually and often involves heavy materials handling, which increases the risk of musculoskeletal disorders. However, monitoring physical load levels applied to workers during construction work is difficult due to the large size of the site and a huge number of workforce. Under this circumstance, this study developed an approach to evaluate the lifting workload of construction workers using a smart insole sensor for the purpose of preventing musculoskeletal disorders. In the experiment, different level of risks were set by changing the lifting load according to the NIOSH Lifting Index. Participants wore a smart insole and performed repetitive lifting tasks. A analysis was conducted by applying the Bi-LSTM model, a deep learning algorithm based on a recurrent neural network. As a result of the analysis, an accuracy of up to 84.1% was confirmed when using data collected from the nearest foot to the lifting object. The approach introduced in this study utilizes foot-pressure data which is easier to acquire than other biometric data and would have a higher field applicability. The approach would help to manage the level of physical load during a heavy material handling tasks at construction sites and prevent musculoskeletal disorders of construction workers.