A privacy-aware fall detection system for aging-in-place environments
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Date
2025-08
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University of New Brunswick
Abstract
Falls are a major threat to the health and independence of older adults, making effective fall detection critical in smart healthcare systems. Traditional approaches face challenges like limited labeled data and privacy concerns from centralized data collection. This thesis introduces a privacy-preserving fall detection framework that integrates three key systems: (1) a semi-supervised federated learning model for wearable-based fall detection without requiring labeled data; (2) an adaptive indoor localization technique using a SLAM-enabled robot for autonomous WiFi and BLE fingerprinting; and (3) a multi-stage response system combining wearable alerts, robotic navigation, and vision-based verification. The Semi-supervised Federated Fall Detection (SF2D) model enables devices to learn collaboratively while safeguarding privacy. The robotic system builds a detailed radio map for precise localization, and the integrated system confirms falls through visual validation. Experimental results show improved detection accuracy, fewer false alarms, and enhanced privacy and resource consumption. This work presents a scalable, ethical solution to support aging-in-place through intelligent fall detection.