Recognition of Falls and Activities of Daily Living using Tri-axial Accelerometer and Bi-axial Gyroscope

Abstract

Abstract This paper proposes a threshold-based fall recognition algorithm to discriminate between falls and activities of daily living(ADL) using a tri-axial accelerometer and a bi-axial gyroscope sensor mounted on the upper sternum. The experiment wasexecuted ten times according to the proposed experimental protocol. The output signals of the tri-axial accelerometer andthe bi-axial gyroscope were measured during eight falls and eleven ADL action sequences. The threshold values of the signalvector magnitude (SVM_Acc), angular velocity (ω res ), and angular variation (θ res ) parameter were calculated using MATLAB.From the preliminary study, three thresholds (TH1, TH2, and TH3) were set so that the falls could be distinguished fromADL. When the parameter SVM_Acc is greater than 2.5 g (TH1), ω res is greater than 1.75 rad/s (TH2), and θ res is greaterthan 0.385 rad (TH3), these action sequences are recognized as falls. If at least one or more of these conditions is not sat-isfied, the sequence is classified as ADL.Keywords: Falls-recognition, Tri-axial accelerometer, Bi-axial gyroscope, Activities of Daily Living (ADL), ADL actionsequence