A Determination Method for Gait Event Based on Acceleration Sensors

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.

Non-Linear Template-Based Approach for the Study of Locomotion

“…Reported scores for ∆Start range from 0.012 to 0.072 s depending on the method and from 0.012 to 0.112 s for ∆End. These values are in accordance with several recent publications on the topic (≥ 2019): Caramia et al [74] (∆Start = 0.022 s, ∆End = 0.024 s), Kidzinski et al [35] (∆Start = 0.010 s, ∆End = 0.013 s), Gadaleta et al [34] (∆Start ≈ ∆End ≈ 0.040 s) and Mei et al [75] (∆Start ≈ ∆End ≈ 0.020 s). These values (summarised in Table 2) are to be compared with the results presented in this section.…”

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

“…For interactive information fusion, common fusion algorithms include Kalman filter [ 95 ], particle filter [ 96 ], complementary filter [ 97 ], and artificial neural network [ 98 ]. Generally, a single Kalman filter is not ideal, so the extended Kalman filter method or combined with other methods is a good choice [ 99 ].…”

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

“…Gait refers to the movement and balance of the human body when walking upright, which is the basic movement mode of the lower limbs [ 1 , 2 ]. Under the control of the nervous system, gait is completed by the joint action of muscles, joints, and bones.…”