Biomechanical Analysis of Human Gait with Inertial Sensors Using Neural Networks

2019 ◽  
pp. 213-221
Author(s):  
Bogart Yail Márquez ◽  
José Sergio Magdaleno-Palencia ◽  
Arnulfo Alanís-Garza ◽  
Karina Romero-Alvarado ◽  
Rosana Gutiérrez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Inertial Sensors ◽  
Biomechanical Analysis ◽  
Human Gait

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

scholarly journals Attention-based deep learning networks for identification of human gait using radar micro-Doppler spectrograms

2021 ◽  
pp. 1-6
Author(s):  
Hannah Garcia Doherty ◽  
Roberto Arnaiz Burgueño ◽  
Roeland P. Trommel ◽  
Vasileios Papanastasiou ◽  
Ronny I. A. Harmanny
Keyword(s):  
Neural Networks ◽  
Feature Vector ◽  
Classification Performance ◽  
Input Image ◽  
Human Gait ◽  
Learning Networks ◽  
Class Label ◽  
Deep Convolutional Neural Networks ◽  
Network Layers ◽  
Feature Dimension

Abstract Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.

scholarly journals Biomechanical parameters characterising the foot during normal gait

2021 ◽  
Vol 21 (2) ◽  
pp. 87-104
Author(s):  
Arina SEUL ◽  
Aura MIHAI ◽  
Antonela CURTEZA ◽  
Mariana COSTEA ◽  
Bogdan SÂRGHIE
Keyword(s):  
Neuromuscular Control ◽  
Gait Pattern ◽  
Biomechanical Analysis ◽  
Human Gait ◽  
Foot Health ◽  
Mean Values ◽  
Study Results ◽  
Size Groups ◽  
Biomechanical Parameters ◽  
Extended Group

The biomechanical analysis allows to understand the normal and pathological gait, the mechanics of neuromuscular control, and last but not least, allows the visualisation of the effects of footwear on human gait or feet. Biomechanical analyses are very important for the footwear development process, as they can identify the incorrect loading of the foot or the incorrect gait pattern, thus avoiding the occurrence of deformations. This paper aims to create an average representative model of barefoot loading based on an extended group of participants by applying an optimal procedure for measuring biomechanical parameters. The variation of four basic biomechanical parameters, namely force, pressure, contact time and contact area, was measured using a pressure platform and a specialised software system. The data was collected from 32 healthy females, without particularities regarding foot health and the practice of performance sports, aged between 18 and 30 years, divided into three size groups – 36, 37 and 38. The T-Student test was applied to verify if there are significant differences between the left and right foot. Statistical indicators for each parameter were calculated, in order to characterize and establish the degree of variation of the obtained values, as follows: mean, standard deviation, minimum and maximum values, the amplitude of variation and coefficient of variation (CV). The study results confirm that the obtained mean values can be used as input data to load the foot and perform virtual simulations of footwear products.

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 Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks

2020 ◽  
Vol 10 (16) ◽  
pp. 5405
Author(s):  
Cornelia Herbert ◽  
Michael Munz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Treadmill Walking ◽  
Human Gait ◽  
Brain Potentials ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Artificial Neural ◽  
Technical Solutions

The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) for synchronous recording of gait and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module, and a data-merging module, allowing the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG system (Brain Products GmbH). The usability and validity of the developed solution was investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis.

Human Gait Analysis and Classification Based on Neural Networks and Fuzzy Logic

2009 ◽  
Vol 147-149 ◽  
pp. 600-605
Author(s):  
Jolanta Pauk ◽  
Marcin Derlatka ◽  
Rafał Długosz ◽  
Marta Kolasa
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Gait Analysis ◽  
Kinetic Data ◽  
Treatment Decision ◽  
Human Gait ◽  
Decision Making Process ◽  
Treatment Decision Making ◽  
Human Gait Analysis ◽  
Stride Parameters

Human gait analysis and classification is the process of identifying individuals by their walking manners. Computerized gait analysis using neural networks and fuzzy logic has become an integral part of the treatment decision-making process. Authors proposed the integration of kinetic data, more specifically power joints in combination with neural networks and fuzzy logic. It is a relatively new addition to other types of data including temporal and stride parameters. The performance of our approach was verified in laboratory for motion analysis. The obtained results are satisfying.

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