Human Gait Feature Detection Using Inertial Sensors Wavelets

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.

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Sample Entropy of Human Gait Center of Pressure Displacement: A Systematic Methodological Analysis

Entropy ◽

10.3390/e20080579 ◽

2018 ◽

Vol 20 (8) ◽

pp. 579 ◽

Cited By ~ 9

Author(s):

Samira Ahmadi ◽

Nariman Sepehri ◽

Christine Wu ◽

Tony Szturm

Keyword(s):

Time Series Data ◽

Inertial Sensors ◽

Center Of Pressure ◽

Sampling Rate ◽

Sample Entropy ◽

Human Gait ◽

Series Data ◽

Low Pass ◽

Data Points ◽

Parameter Values

Sample entropy (SampEn) has been used to quantify the regularity or predictability of human gait signals. There are studies on the appropriate use of this measure for inter-stride spatio-temporal gait variables. However, the sensitivity of this measure to preprocessing of the signal and to variant values of template size (m), tolerance size (r), and sampling rate has not been studied when applied to “whole” gait signals. Whole gait signals are the entire time series data obtained from force or inertial sensors. This study systematically investigates the sensitivity of SampEn of the center of pressure displacement in the mediolateral direction (ML COP-D) to variant parameter values and two pre-processing methods. These two methods are filtering the high-frequency components and resampling the signals to have the same average number of data points per stride. The discriminatory ability of SampEn is studied by comparing treadmill walk only (WO) to dual-task (DT) condition. The results suggest that SampEn maintains the directional difference between two walking conditions across variant parameter values, showing a significant increase from WO to DT condition, especially when signals are low-pass filtered. Moreover, when gait speed is different between test conditions, signals should be low-pass filtered and resampled to have the same average number of data points per stride.

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3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic Modeling

IEEE Sensors Journal ◽

10.1109/jsen.2016.2593011 ◽

2016 ◽

Vol 16 (24) ◽

pp. 8823-8831 ◽

Cited By ~ 21

Author(s):

Amin Ahmadi ◽

Francois Destelle ◽

Luis Unzueta ◽

David S. Monaghan ◽

Maria Teresa Linaza ◽

...

Keyword(s):

Inertial Sensors ◽

Human Gait ◽

Kinematic Modeling

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Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

Journal of Healthcare Engineering ◽

10.1155/2020/7914649 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Abeer A. Badawi ◽

Ahmad Al-Kabbany ◽

Heba A. Shaban

Keyword(s):

Feature Selection ◽

Inertial Sensors ◽

Inertial Sensor ◽

Real Life ◽

Human Gait ◽

Body Sensor Networks ◽

Sequential Feature Selection ◽

Wearable Inertial Sensors ◽

Robust Systems ◽

Remote Health

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

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