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Hand function is a determinant of independence and quality of life across clinical populations with upper limb impairments1,2. Capturing the hand function of individuals with hand impairments at home is vital to evaluating the progress of their ability to carry out activities of daily living (ADLs) during and after rehabilitation. Most clinical hand function assessments are conducted in a clinical or laboratory environment, rather than at home3,4. Existing clinical hand function assessments that seek to capture the impact on ADLs at home are questionnaires and rely on subjective self-reported ratings5,6,7. An objective evaluation to assess the ultimate impact of rehabilitation on hand function at home is still unavailable.
In recent years, many wearable technologies have been developed and implemented to capture upper limb function in real-world environments. Wearable sensors such as accelerometers and inertial measurement units (IMUs) have been commonly used to measure upper limb movements in daily life. However, these devices typically do not distinguish whether the detected epochs belong to functional upper limb movements8,9, defined as purposeful movements intended to complete a desired task. For example, some wearable sensors are sensitive to the presence of upper limb swings during walking, which is not a functional movement of the upper limb. Furthermore, although wrist-worn accelerometers capture upper limb movements, they cannot capture the details of hand function in real-world environments. Sensorized gloves allow capturing more detailed information about hand manipulations10, but they may be cumbersome for people whose hand function and sensation are already impaired. Wearable approaches have also been proposed to capture finger movements through magnetometry or finger-worn accelerometers11,12,13, but the functional interpretation of those movements remains challenging14. Thus, although previously proposed wearable devices are small and convenient to use, they are insufficient to describe the details and functional context of hand use.
Wearable cameras have been proposed to fill these gaps and capture details of hand function during ADLs at home for neurorehabilitation applications15,16,17,18,19. Automated analysis of egocentric videos using computer vision has considerable potential to quantify hand function in context, by providing information both about the hands themselves and about the tasks carried out in real ADLs20. On the other hand, the duration of continuous recordings is typically limited to approximately 1 to 1.5 h by battery, storage, and comfort considerations. Here, within these constraints, we present an egocentric video collection protocol intended to obtain data that is both representative of an individual's daily life as well as informative for hand function evaluation.