TY  - BOOK
AU  - Ay-ay, Ismael S...et.al.
TI  - Development of a gait assessment device using deep convolutional neural network and pressure distribution insole / 
PY  - 2024///
CY  - City of Batac
PB  - MMSU
N1  - UTHESIS (Bachelor of Science in Computer Engineering); Bibliography: leaves 85-89
N2  - This study addresses the limitations of traditional gait assessment methods by  developing a Raspberry Pi-driven device that employs computer vision and deep  learning to provide objective gait cycle analysis. Integrating a Deep Convolutional  Neural Network (DCNN) model for gait phase classification and MediaPipe for  detecting hip, knee, and ankle joints to calculate their range of motion, the device also  includes pressure distribution insoles for enhanced analysis. Evaluations revealed high  user satisfaction (average scores of 4.49 for software and 4.69 for hardware) and  impressive accuracy of 96% for the DCNN model in gait phase classification. Despite  some delays in pressure distribution synchronization, the device demonstrated  significant potential in reducing subjectivity in gait analysis, offering a sophisticated  tool for clinical diagnostics and rehabilitation. Future recommendations include  developing a custom pose estimation model, upgrading hardware, incorporating frontal  view analysis, and integrating additional sensors for comprehensive biomechanical  data
ER  -