RiskWatch: Wearable Fall Detection and Healthcare Accountability Device

Falls are a leading cause of hospitalization among seniors, with delayed medical response and underreporting being the most critical factors. RiskWatch is a wearable fall detection and health-monitoring device built to address this critical gap in senior healthcare. Our solution utilizes an ESP32-based wearable equipped with an inertial measurement unit (IMU), a pulse sensor for vital sign tracking, and a lightweight Machine Learning model that classifies incoming sensor data to distinguish fall events from normal activities. When the system detects a fall, the device instantly transmits data to a companion Android application via Bluetooth Low Energy (BLE), which in turn updates a secure, cloud-based database. In preliminary tests involving simulated fall scenarios, the prototype demonstrated reliable, real-time detection and seamless caregiver notification, thereby significantly reducing medical response latency. Furthermore, the device captures contextual data-such as heart rate and incident timestamp-to enable post-fall analysis and help identify patterns that can inform preventative measures. By integrating immediate alerts, data storage, and caregiver access in a single system, RiskWatch offers a proactive solution to reduce fall-related risks and enhance accountability in healthcare settings.

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Publication Details

Published
2025-05-29
DOI
https://doi.org/10.1109/eit64391.2025.11103628
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
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article

RiskWatch: Wearable Fall Detection and Healthcare Accountability Device

Jafar Saniie, Nathan E. Cook, M. Gromov, Samuel Karson et al.
Context-Aware Activity Recognition Systems
article

RiskWatch: Wearable Fall Detection and Healthcare Accountability Device

Jafar Saniie, Nathan E. Cook, M. Gromov, Samuel Karson, Deimantas Gilys, Daniil Skvyrski
article en

Abstract

Falls are a leading cause of hospitalization among seniors, with delayed medical response and underreporting being the most critical factors. RiskWatch is a wearable fall detection and health-monitoring device built to address this critical gap in senior healthcare. Our solution utilizes an ESP32-based wearable equipped with an inertial measurement unit (IMU), a pulse sensor for vital sign tracking, and a lightweight Machine Learning model that classifies incoming sensor data to distinguish fall events from normal activities. When the system detects a fall, the device instantly transmits data to a companion Android application via Bluetooth Low Energy (BLE), which in turn updates a secure, cloud-based database. In preliminary tests involving simulated fall scenarios, the prototype demonstrated reliable, real-time detection and seamless caregiver notification, thereby significantly reducing medical response latency. Furthermore, the device captures contextual data-such as heart rate and incident timestamp-to enable post-fall analysis and help identify patterns that can inform preventative measures. By integrating immediate alerts, data storage, and caregiver access in a single system, RiskWatch offers a proactive solution to reduce fall-related risks and enhance accountability in healthcare settings.

Embedded Systems (United States) (US)
Openalex Percentile: Top 81%
Context-Aware Activity Recognition Systems
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RiskWatch: Wearable Fall Detection and Healthcare Accountability Device — Jafar Saniie, Nathan E. Cook, et al. · (2025) | TGRS Research Map | TGRS