Detecting Falls Using a Wearable Accelerometer Motion Sensor
Hoa Hong Nguyen, Farhaan Mirza, M. Asif Naeem, Mirza Mansoor Baig · 2017
This research aims to early detect falls based on the rapid acceleration 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 differentiate falls from other activities of daily life (ADL). Initial results demonstrates that our algorithm achieved 100% of sensitivity, 95.65% of specificity 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 developing other strategies to reduce false alarms for improving both specificity and accuracy of the algorithm while still maintaining 100% of sensitivity.