Abstract
Safety practitioners widely use the lifting index (LI) to determine workers' lifting risk but are hampered by the difficulties of estimating the lifting load without intervention or intrusive sensors. This study proposes a computer vision method for estimating the LI across varying lifting loads. The proposed method can also predict the Brog rating of perceived exertion (RPE), a measure associated with the lifting load. A controlled lifting experiment was conducted to demonstrate the approach. Thirty participants performed 2176 lifting tasks at three LI levels. These levels were controlled by varying the lifting load and fixing other task variables (e.g., the lifting distance). The proposed method combined the pose estimation (OpenPose) and the optical flow estimation (SelFlow) techniques for extracting the participants' body motion and posture features; a facial expression recognition algorithm (OpenFace) built upon the facial action unit coding system (FACS) was used to extract the participants' facial features. The extracted features were combined and used to develop prediction models. The best-performing model was an integration of the 1-D convolutional neural network and the long short-term memory network. It achieved an area under curve of 0.890 in classifying the LI and a root mean square of 2.264 in predicting the participants' RPE. Critical indicators were identified by investigating the contribution of the features through interpretable machine learning techniques. In summary, this study demonstrates a nonintrusive method for lifting risk assessment and discovers behavioral indicators that predict changes in the LI and RPE due to varying loads.