Development and Evaluation of a Prior-to-Impact Fall Event Detection Algorithm

Abstract

Automatic fall event detection has attracted research attention recently for its potential application in fall alarming system and wearable fall injury prevention system. Nevertheless, existing fall detection research is facing various limitations. The current study aimed to develop and validate a new fall detection algorithm using 2-D information (i.e., trunk angular velocity and trunk angle). Ten healthy elderly were involved in a laboratory study. Sagittal trunk angular kinematics was measured using inertial measurement unit during slip-induced backward falls and a variety of daily activities. The new algorithm was, on average, able to detect backward falls prior to impact, with 100% sensitivity, 95.65% specificity, and 255 ms response time. Therefore, it was concluded that the new fall detection algorithm was able to effectively detect falls during motion for the elderly population.

An Image-Based Fall Detection System for the Elderly

“…Similarly, Tong et al [11], Aberyuwan et al [12], and Pannurat et al [13] used machine learning technologies to analyze signals that were received from triaxial accelerometers that were distributed over a body. In addition, Liu et al [14] detected falls by using not only acceleration information but also angular velocity information. The accuracy of wearable-device-based methods can be improved by obtaining Appl.…”

Pre-impact fall detection

“…Data from wearable IMUs was used as the input of this function to search the optimal thresholds that can distinguish falls from activities of daily living (ADLs). The evaluation results showed that the fall detection performance was enhanced with such an integrative system [ 22 ].…”

“…Head acceleration [ 13 , 21 ] and upper arm velocity [ 13 ] were also used to define fall detection indicators. Fall detection indicators defined by rotational measures included angular rate of the sternum [ 33 ], angular rate of the waist [ 32 , 41 , 43 ] and trunk [ 22 , 23 ], and segment orientation of the trunk [ 22 , 23 ] and thigh [ 31 , 32 ]. Most existing studies used a single kinematic measure to define the fall detection indicator.…”

“…Instead of simulated falls, some researchers used involuntary falls for fall detection evaluation [ 1 , 12 , 13 , 22 , 23 , 26 ]. For example, realistic slip-induced falls were used in Hu and Qu [ 12 , 13 ].…”

“…The rest studies can achieve sensitivity above 90 %. A few studies reported 100 % sensitivities, including Lindemann et al [ 21 ], Nyan et al [ 33 ], Bourke et al [ 8 ], Wu and Xue [ 48 ], Shi et al [ 41 ], Shan and Yuan [ 40 ], Tong et al [ 44 ] and Liu and Lockhart [ 22 , 23 ]. The specificities reported in the reviewed articles were from 85.6–100 %.…”