A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset

Balance ability is one of the important factors in measuring human physical fitness and a common index for evaluating sports performance. Its quality directly affects the coordination ability of human movements and plays an important role in human productive activities. In the field of sports, balan...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-12, Vol.23 (1), p.33
Main Authors: Wang, Fei, Dong, Anqi, Zhang, Kaiyu, Qian, Dexing, Tian, Yinsheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Athletic Performance
balance performance
Business metrics
Comparative analysis
Data collection
Datasets
Equilibrium (Physiology)
Experiments
Human motion
Humans
Kinematics
Lower Extremity
lower limb dataset
Machine learning
Measurement
Methods
Motion capture
Movement disorders
Physical fitness
quantitative assessment model
RUS Boost
Sensors
Software
Sports
Sports injuries
Sports sciences
Support Vector Machine
training
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Summary:Balance ability is one of the important factors in measuring human physical fitness and a common index for evaluating sports performance. Its quality directly affects the coordination ability of human movements and plays an important role in human productive activities. In the field of sports, balance ability is an important indicator of athletes' selection and training. How to objectively analyze balance performance becomes a problem for every non-professional sports enthusiast. Therefore, in this paper, we used a dataset of lower limb collected by inertial sensors to extract the feature parameters, then designed a RUS Boost classifier for unbalanced data whose basic classifier was SVM model to predict three classifications of balance degree, and, finally, evaluated the performance of the new classifier by comparing it with two basic classifiers (KNN, SVM). The result showed that the new classifier could be used to evaluate the balanced ability of lower limb, and performed higher than basic ones (RUS Boost: 72%; KNN: 60%; SVM: 44%). The results meant the established classification model could be used for and quantitative assessment of balance ability in initial screening and targeted training.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23010033