1. Contextualizing remote fall risk: Video data capture and implementing ethical AI.
- Author
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Moore, Jason, McMeekin, Peter, Parkes, Thomas, Walker, Richard, Morris, Rosie, Stuart, Samuel, Hetherington, Victoria, and Godfrey, Alan
- Subjects
RISK assessment ,COMPUTERS ,PREDICTION models ,RESEARCH funding ,ARTIFICIAL intelligence ,PRIVACY ,PRODUCT design ,PILOT projects ,WEARABLE technology ,DESCRIPTIVE statistics ,DIAGNOSIS ,GAIT in humans ,HOME environment ,TELEMEDICINE ,SOUND recordings ,NEUROLOGICAL disorders ,ACQUISITION of data ,ARTIFICIAL neural networks ,MEDICAL needs assessment ,DATA analysis software ,COMPARATIVE studies ,INDIVIDUALIZED medicine ,ACCIDENTAL falls ,VIDEO recording ,MEDICAL ethics ,SENSITIVITY & specificity (Statistics) ,ALGORITHMS ,OPTICAL head-mounted displays ,EVALUATION - Abstract
Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual's habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual's free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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