身体的個人差を考慮した歩容フィードバック訓練システムのための深層学習を用いた歩容の判別と特徴の視覚化

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  • Classification and Features Visualization of Gait Using Convolutional Neural Network for Gait Feedback Training Optimized for Individuals

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<p>In this research, to develop a gait feedback training system optimized for individuals where trainees can efficiently train features that do not satisfy ideal walking using a deep learning, we examine classification and features visualization of gait using Convolutional Neural Network (CNN) and Grad-CAM. In the experiment, the thumb-floor distance of right foot was measured when young people walked normally and when they walked with a brace, to limit their movement. Further, these data were clustered to 3 clusters using k-shape method. And these data were learned and classified as input data and using these cluster as the label. As the result, the accuracy was 86.07%. In addition, the part where the feature in thumb-floor distance appears were visualized as heat map using Grad-CAM and it is confirmed that usefulness for gait training.</p>

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