Automated Hand Prehension Assessment From Egocentric Video After Spinal Cord Injury.

Abstract

Hand function assessments in a clinical setting are critical for upper limb rehabilitation after spinal cord injury (SCI) but may not accurately reflect performance in an individual's home environment. When paired with computer vision models, egocentric videos from wearable cameras provide an opportunity for remote hand function assessment during real activities of daily living (ADLs). This study demonstrates the use of computer vision models to predict clinical hand function assessment scores from egocentric video. SlowFast, MViT, and MaskFeat models were trained and validated on a custom SCI dataset, which contained a variety of ADLs carried out in a simulated home environment. The dataset was annotated with clinical hand function assessment scores using an adapted scale applicable to a wide range of object interactions. An accuracy of 0.551±0.139, mean absolute error (MAE) of 0.517±0.184, and F1 score of 0.547±0.151 was achieved on the 5-class classification task. An accuracy of 0.724±0.135, MAE of 0.290±0.140, and F1 score of 0.733±0.144 was achieved on a consolidated 3-class classification task. This novel approach, for the first time, demonstrates the prediction of hand function assessment scores from egocentric video after SCI.