Interaction Detection in Egocentric Video: Toward a Novel Outcome Measure for Upper Extremity Function.

In order to develop effective interventions for restoring upper extremity function after cervical spinal cord injury, tools are needed to accurately measure hand function throughout the rehabilitation process. However, there is currently no suitable method to collect information about hand function in the community, when patients are not under direct observation of a clinician. We propose a wearable system that can monitor functional hand use using computer vision techniques applied to egocentric camera videos. To this end, in this study we demonstrate the feasibility of detecting interactions of the hand with objects in the environment from egocentric video. The system consists of a preprocessing step where the hand is segmented out from the background. The algorithm then extracts features associated with hand-object interactions. This includes comparing motion cues in the region near the hand (i.e., where the object is most likely to be located) to the motion of the hand itself, as well as to the motion of the background. Features representing hand shape are also extracted. The features serve as inputs to a random forest classifier, which was tested with a dataset of 14 activities of daily living as well as noninteractive tasks in five environment (total video duration of 44.16 min). The average F-score for the classifier was 0.85 for leave-one-activity out in our dataset set and 0.91 for a publicly available set (1.72 min) when filtered with a moving average. These results suggest that using egocentric video to monitor functional hand use at home is feasible.