Abstract
A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.
Highlights
Gait disorders are usually associated with the ageing population as well as falling, leading to both a reduced quality of life and an increased mortality rate
At the same time, when the heel position is minimized as the heel strike (HS) event occurs, the toe position gradually starts to rise as the toe off (TO) event occurs
The results show that the proposed method is easier, for the initial segmentation of the continuous acceleration signal, while the effect is very improved
Summary
Gait disorders are usually associated with the ageing population as well as falling, leading to both a reduced quality of life and an increased mortality rate. The detection of gait events is an important tool in clinics [1,2], including human activity recognition for healthcare [3,4] and motor recovery assessments for effective rehabilitation strategies [5,6]. It is essential to develop an effective algorithm for the accurate detection of gait events. The cost of Mo-Cap system equipment and the force measuring board have a high cost, and there are limitations of areas such as a laboratory environment for data acquisition, while it is unable to synchronously detect human daily activities. The time of a heel strike (HS) and toe off (TO) can be determined by Sensors 2019, 19, 5499; doi:10.3390/s19245499 www.mdpi.com/journal/sensors
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