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Background: Egocentric video has recently emerged as a potential solution formonitoring hand function in individuals living with tetraplegia in thecommunity, especially for its ability to detect functional use in the homeenvironment. Objective: To develop and validate a wearable vision-based systemfor measuring hand use in the home among individuals living with tetraplegia.Methods: Several deep learning algorithms for detecting functional hand-objectinteractions were developed and compared. The most accurate algorithm was usedto extract measures of hand function from 65 hours of unscripted video recordedat home by 20 participants with tetraplegia. These measures were: thepercentage of interaction time over total recording time (Perc); the averageduration of individual interactions (Dur); the number of interactions per hour(Num). To demonstrate the clinical validity of the technology, egocentricmeasures were correlated with validated clinical assessments of hand functionand independence (Graded Redefined Assessment of Strength, Sensibility andPrehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal CordIndependent Measure - SCIM). Results: Hand-object interactions wereautomatically detected with a median F1-score of 0.80 (0.67-0.87). Our resultsdemonstrated that higher UEMS and better prehension were related to greatertime spent interacting, whereas higher SCIM and better hand sensation resultedin a higher number of interactions performed during the egocentric videorecordings. Conclusions: For the first time, measures of hand functionautomatically estimated in an unconstrained environment in individuals withtetraplegia have been validated against internationally accepted measures ofhand function. Future work will necessitate a formal evaluation of thereliability and responsiveness of the egocentric-based performance measures forhand use.

Measuring hand use in the home after cervical spinal cord injury using egocentric video