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Current upper extremity outcome measures for persons with cervical spinalcord injury (cSCI) lack the ability to directly collect quantitativeinformation in home and community environments. A wearable first-person(egocentric) camera system is presented that can monitor functional hand useoutside of clinical settings. The system is based on computer vision algorithmsthat detect the hand, segment the hand outline, distinguish the user's left orright hand, and detect functional interactions of the hand with objects duringactivities of daily living. The algorithm was evaluated using egocentric videorecordings from 9 participants with cSCI, obtained in a home simulationlaboratory. The system produces a binary hand-object interaction decision foreach video frame, based on features reflecting motion cues of the hand, handshape and colour characteristics of the scene. This output was compared with amanual labelling of the video, yielding F1-scores of 0.74 $\pm$ 0.15 for theleft hand and 0.73 $\pm$ 0.15 for the right hand. From the resultingframe-by-frame binary data, functional hand use measures were extracted: theamount of total interaction as a percentage of testing time, the averageduration of interactions in seconds, and the number of interactions per hour.Moderate and significant correlations were found when comparing these outputmeasures to the results of the manual labelling, with $\rho$ = 0.40, 0.54 and0.55 respectively. These results demonstrate the potential of a wearableegocentric camera for capturing quantitative measures of hand use at home.

Egocentric Video: A New Tool for Capturing Hand Use of Individuals with Spinal Cord Injury at Home