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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Main Authors: Pang-Ti Tai, 戴邦地
Other Authors: 蘇木春
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/5v3u5a
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spelling ndltd-TW-105NCU053920892019-05-15T23:39:52Z http://ndltd.ncl.edu.tw/handle/5v3u5a A Deep Learning Approach to the Recognition of Rehabilitation Exercises 基於深度學習之復健動作辨識系統 Pang-Ti Tai 戴邦地 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. 蘇木春 2017 學位論文 ; thesis 79 zh-TW
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description 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.
author2 蘇木春
author_facet 蘇木春
Pang-Ti Tai
戴邦地
author Pang-Ti Tai
戴邦地
spellingShingle Pang-Ti Tai
戴邦地
A Deep Learning Approach to the Recognition of Rehabilitation Exercises
author_sort Pang-Ti Tai
title A Deep Learning Approach to the Recognition of Rehabilitation Exercises
title_short A Deep Learning Approach to the Recognition of Rehabilitation Exercises
title_full A Deep Learning Approach to the Recognition of Rehabilitation Exercises
title_fullStr A Deep Learning Approach to the Recognition of Rehabilitation Exercises
title_full_unstemmed A Deep Learning Approach to the Recognition of Rehabilitation Exercises
title_sort deep learning approach to the recognition of rehabilitation exercises
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/5v3u5a
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