Video-Based Automatic Wrist Flexion and Extension Classification

A computer vision method was developed to automatically measure wrist flexion and extension from a 2-D video for occupational health and safety research. Marker-less tracked skeletal joints of the elbow, wrist, and hand estimated the wrist flexion/extension angle between the hand and forearm. Based on the estimated angles, wrist posture was classified as flexion (palmar bending), neutral (no bending), or extension (dorsal bending) for each cycle of hand movement. Applying to a set of laboratory videos of a simulated repetitive motion task, we demonstrated the feasibility of using this algorithm for assessing the state of hand activities during manual work. Tested on 1464 frames from 61 recorded videos for 16 participants, the algorithm achieved an average performance of 72.40% correct, per-class accuracy. The sensitivity and specificity for flexion were 66.16% and 91.47%, respectively. The sensitivity and specificity for extension were 77.12% and 89.72%, respectively. This compared favorably against a previously reported consistency rate of 57% between human analyst estimates and wrist electrogoniometer measured wrist flexion/extension angles. We also applied this technique to 262 video frames of hand flexion instances selected from industrial field video data. For these videos, the average correct per-class accuracy was 76.03% in comparison to human observers. The sensitivity and specificity for flexion were 69.23% and 94.17%, respectively, and the sensitivity and specificity for extension were 91.95% and 80.57%, respectively.