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Method Article
A protocol is proposed to capture natural hand function of individuals with hand impairments during their daily routines using an egocentric camera. The goal of the protocol is to ensure that the recordings are representative of an individual's typical hand use during activities of daily living at home.
Impaired hand function after neurological injuries can have a major impact on independence and quality of life. Most existing upper limb assessments are carried out in person, which is not always indicative of hand use in the community. Novel approaches to capture hand function in daily life are required to measure the true impact of rehabilitation interventions. Egocentric video combined with computer vision for automated analysis has been proposed to evaluate hand use at home. However, there are limitations to the duration of continuous recordings. We present a protocol designed to ensure that the videos obtained are representative of daily routines while respecting participant privacy.
A representative recording schedule is selected through a collaborative process between the researchers and participants, to ensure that the videos capture natural tasks and performance, while being useful for hand assessment. Use of the equipment and procedures is demonstrated to the participants. A total of 3 h of video recordings are scheduled over two weeks. To reduce privacy concerns, participants have full control to start and stop recordings, and the opportunity to edit the videos before returning them to the research team. Reminders are provided, as well as help calls and home visits if necessary.
The protocol was tested with 9 stroke survivors and 14 individuals with cervical spinal cord injury. The videos obtained contained a variety of activities, such as meal preparation, dishwashing, and knitting. An average of 3.11 ± 0.98 h of video were obtained. The recording periods varied from 12-69 d, due to illness or unexpected events in some cases. Data was successfully obtained from twenty-two out of 23 participants, with 6 participants requiring assistance from the investigators during the home recording period. The protocol was effective for collecting videos that contained valuable information about hand function at home after neurological injuries.
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.
The study was approved by the Research Ethics Board of the University Health Network. Signed informed consent was obtained from each participant before enrollment in the study. Signed informed consent was also obtained from any caregivers or household members appearing in video recordings.
1. Verification of the protocol applicability to the individual
NOTE: This protocol is intended to be applied to individuals with impaired but not completely absent hand function (specific criteria can be adapted to the population and/or question of interest).
2. Determination of the daily routine of participants
3. Agreement on recording schedules and target video content with participants
4. Emphasis of the importance of performing ADLs naturally
5. Notification of potential privacy issues during recordings at home
6. Camera and tablet instruction
NOTE: If participants indicate during the initial contact that they require caregiver assistance for many of their daily needs, the caregiver is encouraged to also attend the study visit and be trained on the use of the equipment, so that they can later assist the participant.
7. Giving the equipment
8. Experimental troubleshooting and followup
9. Retrieval of equipment and videos
Participant demographics and inclusion criteria
A sample of 23 participants was recruited for these studies: 9 stroke survivors (6 men, 3 women) and 14 individuals with cSCI (12 men, 2 women). Summary demographic and clinical information for the recruited sample are reported in Table 1.
Age (years) |
We presented a protocol for recording videos of ADLs at home using wearable cameras in individuals with upper limb impairments, such as cSCI and stroke. The protocol is flexible and can be goal-directed to capture hand function performance in specific ADLs or to track the progress of rehabilitation remotely in people living at home. The egocentric vision paradigm has great potential for remote monitoring of hand function in individuals living in the community, and for optimizing rehabilitation once people are discharged ...
The authors have nothing to disclose.
The studies using this protocol were funded by the Heart and Stroke Foundation (G-18-0020952), the Craig H. Neilsen Foundation (542675), the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-05498), and Ministry of Research, Innovation and Science, Ontario (ER16-12-013).
Name | Company | Catalog Number | Comments |
Egocentric camera | GoPro Inc., CA, USA | GoPro Hero 4 and 5 | A camera that records from a first-person angle. |
Battery chager and batteries | GoPro Inc., CA, USA | MAX Dual Battery Charger + Battery | Extra batteries for the camera and battery charger |
Camera charger | GoPro Inc., CA, USA | Supercharger | This charger is connected to the camera directly without disassembling the camera frame. |
Camera frame | GoPro Inc., CA, USA | The Frame | The hinge of the camera frame can be used to adjust the angle of view of the camera. |
Headband for the camera | GoPro Inc., CA, USA | Head Strap + QuickClip | |
SD card | SanDisk, CA, USA | 32GB microSD | |
Tablet | ASUSTeK Computer Inc., Taiwan | ZenPad 8.0 Z380M | The tablet is installed with the GoPro App in order to connect with the camera. |
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