Privacy-Preserving Fall Detection Using an Ultra-Wideband Sensor With Continuous Human State Classification

Detecting falls in elderly individuals is crucial for their safety and well-being, especially in high-risk areas such as bathrooms. To enhance human activity recognition (HAR) while ensuring user privacy, we developed an advanced fall detection system using ultra-wideband (UWB) radar technology. The UWB radar transmits high-frequency pulse signals that are used to reflect human activities without compromising user privacy. Our innovative continuous human state classification approach analyzes the sequences of the activities identified using a single-shot recognition algorithm. This method interprets these activities as cohesive events, leveraging the preceding activities to predict and identify falls. Our algorithm meticulously organized these activities into a comprehensive chronicle of human behavior, significantly enhancing the model's ability to accurately identify falls. The historical context provided by this chronicle boosted the model's performance. Our research revealed that the continuous human state classification model increased the accuracy by 26 % compared with that of the single-shot approach. Furthermore, we incorporated a novel transformer-based model that surpasses previous methods employing convolutional neural networks (CNN) and long short-term memory (LSTM) models. This study provides an exhaustive account of the design, implementation, and evaluation of our approach, demonstrating its effectiveness through real-world testing. Our findings highlight the superior accuracy and reliability of the continuous human state classification model for fall detection, thereby setting a new standard in elderly care technology.