[Design of Fall Detection System for Elderly People Based on MPU6050 Sensor]

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Sensor Systems

Background:

• Falls pose a significant risk to the elderly, necessitating advanced detection systems.
• Existing fall detection methods often struggle with accuracy and distinguishing falls from daily activities.

Purpose of the Study:

• To develop and validate a robust fall detection system using the MPU6050 sensor.
• To create an algorithm capable of differentiating falls from normal activities with high accuracy.

Main Methods:

• Utilized an MPU6050 sensor and STM32 microcontroller for data acquisition.
• Implemented a new judging algorithm based on three-axis acceleration and body angle deviation thresholds.
• Integrated Bluetooth 4.0 for data transfer to a smartphone for alerting family and medical services.

Main Results:

• The proposed system achieved a fall detection accuracy exceeding 95%.
• The algorithm effectively distinguished between actual falls and normal daily activities.
• Demonstrated the system's robustness and reliability in real-world scenarios.

Conclusions:

• The MPU6050-based fall detection system offers a reliable solution for elderly monitoring.
• The developed algorithm enhances accuracy, reducing false alarms.
• This technology has the potential to improve emergency response times and patient outcomes.