Privacy-Aware Computer Vision for Ambient Assisted Living: Balancing Older Adults’ Autonomy, Safety, and Trust
Keywords:
ambient assisted living, computer vision, privacy, older adults, autonomy, trust, edge computing, data governanceAbstract
Ambient assisted living systems increasingly rely on computer vision to support older adults in maintaining independence while enabling timely detection of falls, health deterioration, and unsafe conditions. However, camera-based monitoring introduces substantial privacy risks that can undermine autonomy, dignity, and trust. This paper examines privacy-aware computer vision for ambient assisted living as a systems problem spanning architecture, data governance, edge intelligence, robustness, fairness, and policy. It argues that meaningful privacy protection cannot be achieved solely through algorithmic modifications or encryption, but requires a layered sociotechnical design in which raw visual data are interpreted close to the source, sensitive information is abstracted before transmission, and residents retain control over how monitoring outputs are used. The discussion considers structural trade-offs between utility and privacy, the role of edge processing in reducing data leakage, the importance of dynamic consent infrastructures, and the challenges of ensuring equitable performance across diverse older populations. The paper also addresses deployment sustainability, regulatory alignment, and the need for continuous auditing of vision systems in domestic settings. By integrating technical and governance perspectives, the paper provides a system-level framework for balancing safety, autonomy, and trust in real-world ambient assisted living deployments.
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