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Project # 126
Health Monitor IoT-Based Fall Detection System
| Team Lead: | Trung Nguyen |
| Other Team Members: | Ayman Azahar | Tanthony Caotran | Oscar Guerrero | Mahib Nasif | Jose Lopez |
| Faculty Advisor(s): | Chenxi Wang |
| Department: | CSE |
| Project Type: | Senior Design |
| Title: | Health Monitor IoT-Based Fall Detection System |
| Abstract: | The basis of this project was to develop a wearable device designed to help with rapid response for fall-prone individuals. The system architecture centers around an ESP32 Microcontroller and an Inertial Measurement Unit (IMU). By processing the tri-axial accelerometer and gyroscope data, the device processes and identifies events characteristic to a fall. The fall-detection logic utilizes a multi-stage verification process: it first monitors for rapid rotational and inertial changes, followed by a post-fall timer to detect user stillness. If no recovery movement is sensed within the designated window, the system triggers a "fall detected" flag. To minimize false positives - such as the transition from standing to sitting - the algorithm is tuned to account for this differentiation, complemented by a manual override button for the user. Without a change of detection logic, the system's parameters that control the differentiation are tuned through several trials. To maximize battery efficiency, the device utilizes Bluetooth Low Energy (BLE) to sync with a mobile-web application. The smartphone acts as a gateway, transmitting the flag and raw data to a cloud-based platform for more intensive computation and caregiver notification. |
| Poster: | View Poster #126 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |