Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/427558
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dc.coverage.spatialCertain performance analysis on GAIT parameters using machine learning techniques for ceaseless monitoring
dc.date.accessioned2022-12-18T09:40:00Z-
dc.date.available2022-12-18T09:40:00Z-
dc.identifier.urihttp://hdl.handle.net/10603/427558-
dc.description.abstractA 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.extentxix,147p.
dc.languageEnglish
dc.relationp.136-146
dc.rightsuniversity
dc.titleCertain performance analysis on GAIT parameters using machine learning techniques for ceaseless monitoring
dc.title.alternative
dc.creator.researcherArunkumar, P
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordLocomotion
dc.subject.keywordMassive fitness
dc.subject.keywordParaplegics
dc.description.note
dc.contributor.guideShanthini, J
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2022
dc.date.awarded2022
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File27.18 kBAdobe PDFView/Open
02_prelim pages.pdf1.53 MBAdobe PDFView/Open
03_content.pdf32.46 kBAdobe PDFView/Open
04_abstract.pdf97.86 kBAdobe PDFView/Open
05_chapter 1.pdf341.82 kBAdobe PDFView/Open
06_chapter 2.pdf194.76 kBAdobe PDFView/Open
07_chapter 3.pdf1.31 MBAdobe PDFView/Open
08_chapter 4.pdf1.5 MBAdobe PDFView/Open
09_chapter 5.pdf1.75 MBAdobe PDFView/Open
10_annexures.pdf134.68 kBAdobe PDFView/Open
80_recommendation.pdf96.4 kBAdobe PDFView/Open


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