A Deep Learning Approach to the Recognition of Rehabilitation Exercises
碩士 === 國立中央大學 === 資訊工程學系 === 105 === Many people suffer from inconveniences due to various kinds of diseases or bodily injury in modern society. In order to help these people, physical rehabilitation is indispensable. However, too many patients may cause a shortage of medical resource. Therefore,...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/5v3u5a |
| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 105 === Many people suffer from inconveniences due to various kinds of diseases or bodily injury in modern society. In order to help these people, physical rehabilitation is indispensable. However, too many patients may cause a shortage of medical resource. Therefore, if a combination of physical rehabilitation and modern technology could be made, patients can improve their health condition by performing proper rehabilitation exercises in their own place with a rehabilitation system.
A rehabilitation system should be able to recognize the motion performed by patient correctly. The accuracy of motion recognition plays a very important role in a rehabilitation system. This paper provides a motion recognition system which uses the Kinect 2 sensor with the deep learning techniques. This paper introduces a new method of feature extraction. The main concept of this method is to convert a motion into a motion trajectory image. The motion trajectory image is then used as the input of convolutional neural networks for motion recognition.
This paper uses 12 types of rehabilitation exercises in our experiment. We have tried different ways in each step of our method, and we finally choose one with a better result in our test. According to the result, our method has a good ability in motion recognition.
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