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http://hdl.handle.net/10603/427558
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DC Field | Value | Language |
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dc.coverage.spatial | Certain performance analysis on GAIT parameters using machine learning techniques for ceaseless monitoring | |
dc.date.accessioned | 2022-12-18T09:40:00Z | - |
dc.date.available | 2022-12-18T09:40:00Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/427558 | - |
dc.description.abstract | A walking pattern or gait is the common individual activity that a human being tends to do for locomotion in their daily commute life. It is the maximum fundamental technique of transportation, however, an incapability to walk or to be mobile can significantly alternate someone s life. It can affect independence and create massive fitness issues throughout an individual life. The act of walking happens without accessing lots of knowledge into thoughts when there are no disorders or abnormalities in the lower extremities. Post-surgery patients or patients subjected to paraplegics will face difficulties in regaining their proper gait for a period which will lead to complications like posture problems, stabilities issues, falls, and restoration functions. Gait analysis is the most properly used method in characterizing the kinetics and kinematics of human locomotion parameters concerning individual statistics. The clinical gait analysis techniques are engaged in finding the normal pattern of walking on a plain surface where it doesn t meet out the real-life standards. But for people under ambulation, the necessity of continuous monitoring is in high demand. The researchers concluded that gait-related abnormalities will make alternate and issues in mobilities and problems related to postures while standing or sitting affecting the pathological and physiological nature of the human being. The pathological gait pattern is subjected to ataxic gait, neurological disorders, hemiplegic, paraplegic, and Parkinson s diseases will have a huge modification in human gait. Quantification of gait characteristics can be offered as diagnostic acuity concerning gait rehabilitation for the medical practitioner in the field of medical treatment or sports for enhancing the abilities. The parameters from evaluated patterns will provide the measures to set up the maximum efficacy. With this, the change of gait will be measured and analyzed by the proposed method. newline | |
dc.format.extent | xix,147p. | |
dc.language | English | |
dc.relation | p.136-146 | |
dc.rights | university | |
dc.title | Certain performance analysis on GAIT parameters using machine learning techniques for ceaseless monitoring | |
dc.title.alternative | ||
dc.creator.researcher | Arunkumar, P | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Information Systems | |
dc.subject.keyword | Locomotion | |
dc.subject.keyword | Massive fitness | |
dc.subject.keyword | Paraplegics | |
dc.description.note | ||
dc.contributor.guide | Shanthini, J | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Information and Communication Engineering | |
dc.date.registered | ||
dc.date.completed | 2022 | |
dc.date.awarded | 2022 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 27.18 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.53 MB | Adobe PDF | View/Open | |
03_content.pdf | 32.46 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 97.86 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 341.82 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 194.76 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.31 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 1.5 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.75 MB | Adobe PDF | View/Open | |
10_annexures.pdf | 134.68 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 96.4 kB | Adobe PDF | View/Open |
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