Wearable Pre-Impact Fall Detection System Based on 3D Accelerometer and Subject's Height

• Published in: IEEE sensors journal Vol. 22; no. 2; pp. 1738 - 1745
• Main Authors: Ferreira de Sousa, Felipe Augusto Sodre, Escriba, Christophe, Avina Bravo, Eli Gabriel, Brossa, Vincent, Fourniols, Jean-Yves, Rossi, Carole
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Accelerometers; Air bags; Algorithms; Biomedical monitoring; Classification algorithms; Customizable algorithm; Electronics; Engineering Sciences; Fall detection; fall detection system; False alarms; Injuries; Instrumentation and Detectors; Lead time; Physics; pre-impact detection; Senior citizens; Sensitivity; Signal and Image processing; threshold-based; wearable systems; Wearable technology; Threshold-based; Pre-impact detection; Wearable systems; Customizable algorithm; Fall Detection System
• DOI: 10.1109/JSEN.2021.3131037
• ISSN: 1530-437X

This study presents a low-power wearable system able to predict a fall by detecting a pre-impact condition, performed through a simple analysis of motion data (acceleration) and height of the subject. The system can detect a fall in all directions with an average consumption of 5.91 mA; i.e., it can monitor the activity of daily living (ADL), whether or not a fall occurs. The entire detection system uses a single wearable tri-axis accelerometer placed on the waist for the comfort of the wearer during a long-term application. The algorithm is based on the following hypothesis: "A region defined as balanced boundary circle, based on the user's height, is characterized by the fact the chance that an actual fall happening is minimal. When an activity is classified outside this circle, an acceleration analysis is performed to determine an impending fall condition". Our threshold-based algorithm was validated experimentally, first with 9 young healthy volunteers performing both normal ADL and fall activities and then using 10 ADL and 5 falls from public SisFall dataset. The results show that falls could be detected with an average lead-time of 259 ms before the impact occurs, with minimal false alarms (97.7% specificity) and a sensitivity of 92.6%. This is a good lead-time achieved thus far in pre-impact fall detection, permitting the integration of our detection system in a wearable inflatable airbag for hip protection.