000			01797nam a22001817a 4500
003			OSt
005			20240919141852.0
008			240906b |||||||| |||| 00| 0 eng d
040			_aMMSU _cULS
100			_aAy-ay, Ismael S...et.al.
245			_aDevelopment of a gait assessment device using deep convolutional neural network and pressure distribution insole / _cIsmael S. Ay-ay, John Isa O. Palacio, Dale M. Panganiban, Selwyne Christian E. Ponce, Lanz Brent Salmo
260			_aCity of Batac : _bMMSU, _c2024.
300			_axxii, 167 leaves : _c29 cm
500			_aUTHESIS (Bachelor of Science in Computer Engineering)
504			_aBibliography: leaves 85-89
520			_aThis 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.
942			_2lcc _cTHEDIS
999			_c23482 _d23482