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Contextualizing remote fall risk: Video data capture and implementing ethical AI.
- Source :
- NPJ Digital Medicine; 3/6/2024, Vol. 7 Issue 1, p1-7, 7p
- Publication Year :
- 2024
-
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]
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 7
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 175896024
- Full Text :
- https://doi.org/10.1038/s41746-024-01050-7