Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes

TL;DR

This paper introduces a bed-integrated vibration-based fall detection system using machine learning, aiming to improve patient safety in nursing homes without infringing on privacy.

Contribution

It develops a novel convolutional neural network approach to classify fall events from bed vibrations, addressing data limitations and emphasizing real-world applicability.

Findings

Effective classification of fall patterns from bed vibrations

Potential for rapid, privacy-preserving fall detection

Promising lab results with plans for real-world testing

Abstract

The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of an automated fall detection system integrated into care beds, aimed at enhancing patient safety without compromising privacy through wearables or video monitoring. Mechanical vibrations transmitted through the bed frame are processed using a short-time Fourier transform, enabling robust classification of distinct human fall patterns with a convolutional neural network. Challenges pertaining to the quantity and diversity of the data are addressed, proposing the generation of additional data with a specific emphasis on enhancing variation. While the model shows promising results in distinguishing fall events from noise using lab data, further testing in real-world environments is recommended for validation and improvement. Despite limited available data, the proposed system shows the potential for an accurate and rapid response to falls, mitigating health implications, and addressing the needs of an aging population. This case study was performed as part of the ZIM Project. Further research on sensors enhanced by artificial intelligence will be continued in the ShapeFuture Project.