Since threshold-based fall detection has been widely studied by many research groups, accuracy is still a main limitation affected by personal factors. To this end, a personalized threshold extraction approach being adapted for the fall detection usage for different individual is proposed to increase the fall detection accuracy. Moreover, we also implement a fall detection algorithm called ALARM to verify the feasibility of the proposed threshold extraction approach. Results of comprehensive evaluation show it has high accuracy of 96.76% for fall detection, while the sensitivity and the specificity are 92.01% and 99.13%, respectively, based on the data collected from 8 volunteers.
['Lingmei Ren', 'Weisong Shi', 'Zhifeng Yu', 'Jie Cao']
ALARM: A novel fall detection algorithm based on personalized threshold