Development of a gait assessment device using deep convolutional neural network and pressure distribution insole / (Record no. 23482)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01797nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240919141852.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240906b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MMSU |
| Transcribing agency | ULS |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ay-ay, Ismael S...et.al. |
| 245 ## - TITLE STATEMENT | |
| Title | Development of a gait assessment device using deep convolutional neural network and pressure distribution insole / |
| Statement of responsibility, etc. | Ismael S. Ay-ay, John Isa O. Palacio, Dale M. Panganiban, Selwyne Christian E. Ponce, Lanz Brent Salmo |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | City of Batac : |
| Name of publisher, distributor, etc. | MMSU, |
| Date of publication, distribution, etc. | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxii, 167 leaves : |
| Dimensions | 29 cm |
| 500 ## - GENERAL NOTE | |
| General note | UTHESIS (Bachelor of Science in Computer Engineering) |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: leaves 85-89 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis/Dissertation |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Inventory number | Barcode | Date last seen | Price effective from | Koha item type | Public note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MMSU Main Library | MMSU Main Library | Theses and Dissertation Section | 09/06/2024 | 6928 | UTHESIS-6928 | 09/06/2024 | 09/06/2024 | Thesis/Dissertation | Room Use Only |