  Current upper extremity outcome measures for persons with cervical spinal
cord injury (cSCI) lack the ability to directly collect quantitative
information in home and community environments. A wearable first-person
(egocentric) camera system is presented that can monitor functional hand use
outside of clinical settings. The system is based on computer vision algorithms
that detect the hand, segment the hand outline, distinguish the user's left or
right hand, and detect functional interactions of the hand with objects during
activities of daily living. The algorithm was evaluated using egocentric video
recordings from 9 participants with cSCI, obtained in a home simulation
laboratory. The system produces a binary hand-object interaction decision for
each video frame, based on features reflecting motion cues of the hand, hand
shape and colour characteristics of the scene. This output was compared with a
manual labelling of the video, yielding F1-scores of 0.74 $\pm$ 0.15 for the
left hand and 0.73 $\pm$ 0.15 for the right hand. From the resulting
frame-by-frame binary data, functional hand use measures were extracted: the
amount of total interaction as a percentage of testing time, the average
duration of interactions in seconds, and the number of interactions per hour.
Moderate and significant correlations were found when comparing these output
measures to the results of the manual labelling, with $\rho$ = 0.40, 0.54 and
0.55 respectively. These results demonstrate the potential of a wearable
egocentric camera for capturing quantitative measures of hand use at home.
