Optimization of novel feature extraction for foot strike pattern recognition
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Abstract
Foot strike pattern has a massive effect on the knee joint of the runner. An incorrect pattern while running can hurt the runner and significantly decrease running performance. The strike index is the most popular approach used to detect the strike pattern of a runner. However, this method requires expensive equipment in a laboratory environment, which creates difficulty for the experiment and significant costs. The purpose of this study paper was to develop a system to detect foot strike patterns during running using an inexpensive wireless wearable sensor system using hybrid center of pressure and principal component analysis for feature generation and machine learning for pattern classification. Furthermore, different classifiers were compared, to determine the optimal classifier. As a result, the proposed method improved performance in machine learning for foot strike pattern classification; the best classifier was support vector machine (radial basis function), which offered accuracy of 98.68%. This recognition system was thus established and able to successfully detect foot strike patterns. With this system, runners can adjust their foot strike pattern to achieve optimal results.
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