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

2016 ◽  
Vol 16 (24) ◽  
pp. 8823-8831 ◽  
Author(s):  
Amin Ahmadi ◽  
Francois Destelle ◽  
Luis Unzueta ◽  
David S. Monaghan ◽  
Maria Teresa Linaza ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Human Gait ◽  
Kinematic Modeling

SMARTphone inertial sensors based STEP detection driven by human gait learning

2021 ◽  
Author(s):  
Nahime Al Abiad ◽  
Yacouba Kone ◽  
Valerie Renaudin ◽  
Thomas Robert
Keyword(s):  
Inertial Sensors ◽  
Human Gait ◽  
Step Detection ◽  
Gait Learning

scholarly journals A Determination Method for Gait Event Based on Acceleration Sensors

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5499 ◽  
Author(s):  
Chang Mei ◽  
Farong Gao ◽  
Ying Li
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Gait Recognition ◽  
Three Dimensional ◽  
High Standard ◽  
Vertical Displacement ◽  
Human Gait ◽  
Vertical Acceleration ◽  
Acceleration Signal ◽  
Acceleration Sensors

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.

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

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 579 ◽  
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.

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

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.

scholarly journals Method of synchronization and data processing from differents inertial sensors kits sources for the human gait analysis

2018 ◽  
Vol 9 ◽  
pp. 345-349
Author(s):  
Aleksandra Goźdź ◽  
Maciej Kalinowski ◽  
Piotr Kopniak
Keyword(s):  
Data Processing ◽  
Human Body ◽  
Inertial Sensors ◽  
Frame Of Reference ◽  
Data Synchronization ◽  
Human Gait ◽  
File Format ◽  
Sensor Measurement ◽  
Human Gait Analysis ◽  
Common Frame Of Reference

The article talks about results of data synchronization measurements sourced from two recording gait systems for human gait analyses. Two systems are Xsens sensor kits: MT Awinda, Xbus Kit. The article cover file format used to save data and synchronization method for sensor measurement from above mentioned kits. On the basis of the studies carried out, sensor measurement from different places on human body are unify to a common frame of reference. The discussed method provides also progressive data processing for angles range from -180° to 180° conversion to the absolute angle value from initial sensor settings.

Human gait monitoring using body-worn inertial sensors and kinematic modelling

2015 ◽  
Author(s):  
Amin Ahmadi ◽  
Francois Destelle ◽  
David Monaghan ◽  
Kieran Moran ◽  
Noel E. O'Connor ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Human Gait ◽  
Kinematic Modelling ◽  
Gait Monitoring

Non-rigid alignment pipeline applied to human gait signals acquired with optical motion capture systems and inertial sensors

2020 ◽  
Vol 98 ◽  
pp. 109429 ◽  
Author(s):  
Rubén Soussé ◽  
Jorge Verdú ◽  
Ricardo Jauregui ◽  
Ventura Ferrer-Roca ◽  
Simone Balocco
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Human Gait ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Motion Capture Systems

scholarly journals Supernumerary Robotic Limbs to Assist Human Walking With Load Carriage

2020 ◽  
Vol 12 (6) ◽  
Author(s):  
Ming Hao ◽  
Jiwen Zhang ◽  
Ken Chen ◽  
Harry Asada ◽  
Chenglong Fu
Keyword(s):  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Hybrid Control ◽  
High Energy ◽  
Efficient Solutions ◽  
Load Carriage ◽  
Human Walking ◽  
Human Gait ◽  
Finite State ◽  
Load Weight

Abstract Walking with load carriage is a common requirement for individuals in many situations. Legged exoskeletons can transfer the load weight to the ground with rigid-leg structures, thus reducing the load weight borne by the human user. However, the inertia of paralleled structures and the mechanical joint tend to disturb natural motions of human limbs, leading to high-energy consumption. Different from exoskeletons, Supernumerary Robotic Limbs (SuperLimbs) are kinematically independent of the human limbs, thus avoiding the physical interference with the human limbs. In this paper, a SuperLimb system is proposed to assist the human walking with load carriage. The system has two rigid robotic limbs, and each robotic limb has four degrees-of-freedom (DOFs). The SuperLimbs can transfer the load weight to the ground through the rigid structures, thus reducing the weight borne by the human user. A hybrid control strategy is presented to assist the human as well as avoid disturbing user’s natural motions. Motions of the SuperLimb system are generated autonomously to follow the gait of the human user. The gait synchronization is controlled by a finite state machine, which uses inertial sensors to detect the human gait. Human walking experiments are conducted to verify this concept. Experiments indicate that the SuperLimbs can follow the human gait as well as distribute the load weight. Results show that our SuperLimb system can reduce 85.7% of load weight borne by the human when both robotic limbs support and 55.8% load weight on average. This study may inspire the design of other wearable robots and may provide efficient solutions for human loaded walking.

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