Review paper on gait analysis using soft computing techniques (Record no. 22870)

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fixed length control field a
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control field OSt
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control field 20250515142820.0
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fixed length control field 250515b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 26218
Author Moumi Pandit, Ritu
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Title Review paper on gait analysis using soft computing techniques
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Volume, Issue number Vol.105(6), Dec
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Place of publication, distribution, etc. Mumbai
Name of publisher, distributor, etc. Springer
Year 2024
300 ## - PHYSICAL DESCRIPTION
Pagination 1817-1833p.
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Summary, etc. Applications of Artificial intelligence (AI) and soft computing techniques are rapidly growing biomedical research fields. This review article summarizes the most recent advances in detecting and classifying human gait parameters developed over the last forty years. The study explains various initiatives that can help increase the role of soft computing techniques in the health sector in diagnosing and treating diseases much faster. The proposed overview describes the conventional to recent advancements in the subject, including detecting neurodegenerative diseases by applying multimodal medical picture fusion, transfer learning, and deep learning. The techniques in the current paper have been validated with the Gait in Neurodegenerative Disease Database for raw sensor force data collected for left and right foot gait signals for neurodegenerative disease and healthy subjects, which are available online, i.e., PhysioNet.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4619
Topical term or geographic name entry element EXTC Engineering
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9 (RLIN) 26219
Co-Author Bhoi, Akash Kumar
773 0# - HOST ITEM ENTRY
International Standard Serial Number 2250-2106
Title Journal of the institution of engineers (India): Series B
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URL https://link.springer.com/article/10.1007/s40031-024-01130-y
Link text Click here
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    Dewey Decimal Classification     School of Engineering & Technology School of Engineering & Technology Archieval Section 15/05/2025   2025-0842 15/05/2025 15/05/2025 Articles Abstract Database
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