Gait analysis is crucial across various fields, yet traditional methods often struggle outside controlled environments. Smart Insoles, measuring Ground Reaction Forces (GRF), offer a promising avenue for real-world gait assessment. This study focuses solely on comparing stride length data obtained from Smart Insoles with that processed using MATLAB normalization and Convolutional Neural Network (CNN) analysis. Emphasizing accuracy, consistency, and feasibility in everyday settings, our comprehensive analysis aims to validate Smart Insoles' efficacy and foster their integration into daily life, augmenting gait analysis and its applications.
Authors
First Name
Last Name
Hao
Wang
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Submission Details
Conference URC
Event Interdisciplinary Science and Engineering (ISE)
Department Electrical and Computer Engineering (ISE)