scholarly journals Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

2021 ◽  
Vol 185 ◽  
pp. 282-291
Author(s):  
Nizam U. Ahamed ◽  
Kellen T. Krajewski ◽  
Camille C. Johnson ◽  
Adam J. Sterczala ◽  
Julie P. Greeves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Sensor ◽  
Load Carriage ◽  
Sensor Data ◽  
Gait Patterns

scholarly journals Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 75 ◽  
Author(s):  
Ole Rindal ◽  
Trine Seeberg ◽  
Johannes Tjønnås ◽  
Pål Haugnes ◽  
Øyvind Sandbakk
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Automatic Classification ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Micro Sensor ◽  
Cross Country Skiing ◽  
Cross Country ◽  
Classical Cross

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 Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

2017 ◽  
Vol 5 (8) ◽  
pp. e115 ◽  
Author(s):  
Jose Juan Dominguez Veiga ◽  
Martin O'Reilly ◽  
Darragh Whelan ◽  
Brian Caulfield ◽  
Tomas E Ward
Keyword(s):  
Machine Vision ◽  
Method Development ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Activity Classification ◽  
Free Activity

scholarly journals Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0155984 ◽  
Author(s):  
Danique Vervoort ◽  
Nicolas Vuillerme ◽  
Nienke Kosse ◽  
Tibor Hortobágyi ◽  
Claudine J. C. Lamoth
Keyword(s):  
Multivariate Analyses ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Aging Effects ◽  
Timed Up And Go
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