Gait classification of twins and non-twins siblings

2013 ◽  
Author(s):  
Wan-Noorshahida Mohd-Isa ◽  
Junaidi Abdullah ◽  
Chikkanan Eswaran
Keyword(s):  
Gait Classification

scholarly journals Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6253
Author(s):  
Unang Sunarya ◽  
Yuli Sun Hariyani ◽  
Taeheum Cho ◽  
Jongryun Roh ◽  
Joonho Hyeong ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Human Gait ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Gait Patterns ◽  
Gait Classification

Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.

scholarly journals Simulation study of deep-learning-based gait classification of young/elderly adults using Doppler radar

2021 ◽  
Vol 18 ◽  
pp. 100103
Author(s):  
Toshiyuki Hoshiga ◽  
Kenshi Saho ◽  
Keitaro Shioiri ◽  
Masahiro Fujimoto ◽  
Yoshiyuki Kobayashi
Keyword(s):  
Deep Learning ◽  
Simulation Study ◽  
Doppler Radar ◽  
Elderly Adults ◽  
Gait Classification

Movement Pattern Recognition Using Neural Networks

2011 ◽  
pp. 217-237 ◽  
Author(s):  
Rezaul Begg ◽  
Joarder Kamruzzaman
Keyword(s):  
Neural Networks ◽  
Movement Pattern ◽  
Lower Limbs ◽  
Gait Patterns ◽  
Automated Recognition ◽  
Network Applications ◽  
Gait Classification ◽  
Functional Gait ◽  
Neural Network Applications

This chapter provides an overview of artificial neural network applications for the detection and classification of various gaits based on their typical characteristics. Gait analysis is routinely used for detecting abnormality in the lower limbs and also for evaluating the progress of various treatments. Neural networks have been shown to perform better compared to statistical techniques in some gait classification tasks. Various studies undertaken in this area are discussed with a particular focus on neural network’s potential in gait diagnostics. Examples are presented to demonstrate the suitability of neural networks for automated recognition of gait changes due to aging from their respective gait patterns and their potential for identification of at-risk or non-functional gait.

Anomaly gait classification of Parkinson disease based on ANN

2011 ◽  
Author(s):  
Hany Hazfiza Manap ◽  
Nooritawati Md Tahir ◽  
Ahmad Ihsan Mohamed Yassin ◽  
Ramli Abdullah
Keyword(s):  
Parkinson Disease ◽  
Gait Classification

scholarly journals Feature selection in gait classification of leg length and distal mass

2019 ◽  
Vol 15 ◽  
pp. 100163 ◽  
Author(s):  
Millicent Schlafly ◽  
Yasin Yilmaz ◽  
Kyle B. Reed
Keyword(s):  
Feature Selection ◽  
Leg Length ◽  
Gait Classification
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