Efficient fall detection for elderly with integrated machine learning and sensor networks
Munish Bhargav, R. Reddy Anirudh, K Karthik, G. Pavan Rahul, G. Harini Hari, B V Abhinav · 2025
This review paper develops a personalized fall detection system for elderly individuals. By leveraging accelerometer, gyroscope, and temperature sensor data, the system dynamically adjusts thresholds based on body mass index to enhance accuracy and reliability, ensuring prompt and effective fall detection and comprehensive health monitoring. As the global population ages, the incidence of falls and related injuries is projected to increase, posing significant health and economic challenges. Traditional fall detection methods, often relying on manual intervention or basic threshold-based systems, suffer from limitations such as high false-positive rates and delayed response times. To address these challenges, we propose an adaptive fall detection system that harnesses the power of machine learning and sensor fusion. By integrating data from accelerometers and gyroscopes, our system can differentiate between fall events and normal activities with high accuracy. Machine learning algorithms are employed to analyse the sensor data, enabling the system to adapt to individual movement patterns and environmental factors. The primary objectives of this project are: To develop a robust and reliable fall detection algorithm that minimizes false alarms. To ensure the system’s adaptability to different users and conditions. To provide timely alerts to caregivers or emergency services in the event of a fall. Our approach combines advanced data processing techniques with real-time analysis, ensuring prompt and accurate fall detection. The implementation of this system has the potential to significantly enhance the quality of life for elderly individuals, providing them with greater autonomy and safety.