Gait analysis for early Parkinson’s disease detection based on deep learning
Better handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2019-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2019-0003 |
| _version_ | 1811194148803313664 |
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| author | Kondragunta Jyothsna Wiede Christian Hirtz Gangolf |
| author_facet | Kondragunta Jyothsna Wiede Christian Hirtz Gangolf |
| author_sort | Kondragunta Jyothsna |
| collection | DOAJ |
| description | Better handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined. |
| first_indexed | 2024-04-12T00:21:04Z |
| format | Article |
| id | doaj.art-4862892b0c4341399e7afec0c3ff7060 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-12T00:21:04Z |
| publishDate | 2019-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-4862892b0c4341399e7afec0c3ff70602022-12-22T03:55:43ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042019-09-015191210.1515/cdbme-2019-0003cdbme-2019-0003Gait analysis for early Parkinson’s disease detection based on deep learningKondragunta Jyothsna0Wiede Christian1Hirtz Gangolf2Faculty of Electrical Engineering and Information Technology, Technische Universität Chemnitz,Chemnitz, GermanyFaculty of Electrical Engineering and Information Technology, Technische Universität Chemnitz,Chemnitz, GermanyFaculty of Electrical Engineering and Information Technology, Technische Universität Chemnitz,Chemnitz, GermanyBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.https://doi.org/10.1515/cdbme-2019-0003gait analysisdeep learningparkinson’s disease 3d pose estimation |
| spellingShingle | Kondragunta Jyothsna Wiede Christian Hirtz Gangolf Gait analysis for early Parkinson’s disease detection based on deep learning Current Directions in Biomedical Engineering gait analysis deep learning parkinson’s disease 3d pose estimation |
| title | Gait analysis for early Parkinson’s disease detection based on deep learning |
| title_full | Gait analysis for early Parkinson’s disease detection based on deep learning |
| title_fullStr | Gait analysis for early Parkinson’s disease detection based on deep learning |
| title_full_unstemmed | Gait analysis for early Parkinson’s disease detection based on deep learning |
| title_short | Gait analysis for early Parkinson’s disease detection based on deep learning |
| title_sort | gait analysis for early parkinson s disease detection based on deep learning |
| topic | gait analysis deep learning parkinson’s disease 3d pose estimation |
| url | https://doi.org/10.1515/cdbme-2019-0003 |
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