Research Article
Detecting Falls Using a Wearable Accelerometer Motion Sensor
@INPROCEEDINGS{10.4108/eai.7-11-2017.2274974, author={Hoa Nguyen and Farhaan Mirza and M. Asif Naeem and Mirza Mansoor Baig}, title={Detecting Falls Using a Wearable Accelerometer Motion Sensor}, proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ACM}, proceedings_a={MOBIQUITOUS}, year={2018}, month={4}, keywords={acfda falls detection accelerometer algorithm threshold}, doi={10.4108/eai.7-11-2017.2274974} }- Hoa Nguyen
Farhaan Mirza
M. Asif Naeem
Mirza Mansoor Baig
Year: 2018
Detecting Falls Using a Wearable Accelerometer Motion Sensor
MOBIQUITOUS
ACM
DOI: 10.4108/eai.7-11-2017.2274974
Abstract
This research aims to early detect falls based on the rapid acceler- ation changes using the threshold based approach, using a single accelerometer. We propose the Acceleration Change-based Falls Detection Algorithm (ACFDA). The ACFDA observes and detects the rapid change of acceleration in vertical axis and the average value of signal magnitude vector of acceleration to di erentiate falls from other activities of daily life (ADL). Initial results demonstrates that our algorithm achieved 100% of sensitivity, 95.65% of speci city and 96.35% of accuracy when tested with a total of 44 intentional falls and 230 ADLs in 32 datasets. Future work will focus on devel- oping other strategies to reduce false alarms for improving both speci city and accuracy of the algorithm while still maintaining 100% of sensitivity.