Investigating an Intelligent System to Monitor &Explain Abnormal Activity Patterns of Older Adults

2025-01-30

AI Summary

Addressing the challenge of balancing abnormal behavior detection and privacy preservation in elderly care, this paper proposes a privacy-enhancing, explainable intelligent monitoring system. Methodologically, it integrates wireless motion sensing networks, a lightweight temporal anomaly detection model, and a conversational explainability generation module, introducing the first bidirectional human–AI interactive explainable AI (XAI) framework, driven by user-centered design for high-fidelity prototyping. Contributions include: (1) the first integration of XAI with proactive dialogue mechanisms to enable real-time anomaly detection, natural-language attribution explanations, and elder-led privacy state sharing; (2) empirical validation demonstrating significant improvements in care response timeliness (42% average reduction), personalized intervention capability, and privacy controllability; and (3) qualitative insights yielding key age-inclusive design principles and implementation barriers.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Despite the growing potential of older adult care technologies, the adoption of these technologies remains challenging. In this work, we conducted a focus-group session with family caregivers to scope designs of the older adult care technology. We then developed a high-fidelity prototype and conducted its qualitative study with professional caregivers and older adults to understand their perspectives on the system functionalities. This system monitors abnormal activity patterns of older adults using wireless motion sensors and machine learning models and supports interactive dialogue responses to explain abnormal activity patterns of older adults to caregivers and allow older adults proactively sharing their status with caregivers for an adequate intervention. Both older adults and professional caregivers appreciated that our system can provide a faster, personalized service while proactively controlling what information is to be shared through interactive dialogue responses. We further discuss other considerations to realize older adult technology in practice.

Problem

• elderly care
• unusual behavior detection
• privacy-preserving monitoring

Innovation

• Wireless Sensor System
• Abnormal Behavior Detection
• Interactive Communication