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

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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE sensors journal Ročník 22; číslo 2; s. 1738 - 1745
Hlavní autori: Ferreira de Sousa, Felipe Augusto Sodre, Escriba, Christophe, Avina Bravo, Eli Gabriel, Brossa, Vincent, Fourniols, Jean-Yves, Rossi, Carole
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 15.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
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
ISSN:1530-437X, 1558-1748
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3131037