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.
Authors
- Jafar Saniie (ORCID: https://orcid.org/0000-0002-2655-6950)
- Nathan E. Cook (ORCID: https://orcid.org/0000-0002-1946-9023)
- M. Gromov
- Samuel Karson
- Deimantas Gilys
- Daniil Skvyrski
Institutions
- Embedded Systems (United States) (US)
Publication Details
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1109/eit64391.2025.11103628
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00