Advancements in Fall Detection Technologies: A Comprehensive Review and Innovative Solutions
Indranil Basu, Sattwiki Ray, Sayantan Sarkar, Shubhayan Saha, Sanchary Panda · 2023
The aging population is facing an increasing risk of accidental falls, making fall detection technologies crucial for public health and the well-being of elderly individuals. This paper provides a comprehensive research of sensor-based fall detection and rescue systems, categorizing them into single sensor-based and multiple sensor-based solutions. It explores the integration of Machine Learning (ML) and the Internet of Things (IoT) to address the challenges of real-time monitoring, high accuracy, and minimizing false alarms in fall detection. In the quest to enhance fall detection accuracy, innovative solutions are proposed, including the use of Convolutional Neural Networks (CNNs) for efficient event classification. CNNs, originally designed for image analysis, exhibit impressive performance in processing time-series data from various sensors, such as accelerometers, gyroscopes, and ambient environmental sensors.These sensors, part of an IoT network, collect real-time data and transmit it to the CNN model, enabling rapid detection and response to fall incidents. The integration of contextual data from video and audio sources further aids in distinguishing real falls from other events generating similar vibrations. This innovation demonstrates remarkable mean sensitivities and specificities, addressing issues of computational demands and resilience. An accompanying mobile app complements the wearable system, enabling automatic fall detection and immediate alarms, which is vital for timely medical intervention. Continuous calibration and online learning mechanisms are also emphasized to ensure adaptability to changing environmental conditions and performance improvement over time. This comprehensive review and innovative solutions pave the way for more advanced, proactive, and accurate fall detection systems, offering a practical and promising approach to mitigate the consequences of accidental falls among the elderly.