VisiSafe: Real-Time Fall Detection for Visually Impaired People Using RF Sensing

• Published in: IEEE sensors journal Vol. 25; no. 3; pp. 5654 - 5667
• Main Authors: Khan, Muhammad Zakir, Althobaiti, Turke, Almutiry, Muhannad, Rashid, Umar, Ramzan, Naeem
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Bandwidth; Blindness; Contacless fall detection; Fall detection; Indoor environments; machine learning (ML); Millimeter wave communication; Pattern recognition; People with disabilities; Radar; Radar detection; Radio frequency; Real time; real-time detection; Real-time systems; Sensors; Ultra wideband radar; Ultrawideband radar; visual impaired people; Visual impairment
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3509266

Falls are a leading cause of mortality among individuals aged 65 and above, making timely fall detection alarms essential for preventing fatalities. Contactless radio frequency (RF) technology for fall detection has gained traction due to its wide coverage and privacy-preserving features. However, existing RF-based systems often assume falls create predictable RF signal patterns, which can be problematic, especially among visually impaired people (VIP), who cannot detect environmental changes. To overcome this challenge, we propose an innovative approach that focuses on recognizing normal, repeatable human activities and detecting falls as deviations from these patterns. Our prototype, developed using commercial ultrawideband (UWB) XeThru radar, was tested on human subjects, including VIPs. The results demonstrated a classification accuracy of 98.8% within a 1.5-m range in indoor environments, proving our system's high reliability and adaptability for real-time fall detection. This approach provides a more dependable solution for protecting the elderly, especially those with visual impairments, from fall-related dangers.