| Abstrak/Abstract |
This study introduces a cutting-edge real-time
gait deviation detection system that integrates biomechanics
sensory acquisition through gyroscopic sensors and advanced
deep learning via the combination of MobileNetV2 and a bi-
directional long-term memory model. The system evaluates
angular misalignments in critical joints (hip, knee, ankle) along
with speed and gait pattern analysis, offering a comprehensive
dual-approach solution. In clinical trials involving 30 patients,
the system achieved a 12% accuracy in detecting hemiplegic,
diplegic, and standard gait patterns, highlighting challenges in
model optimization. While the system's performance currently
lags behind traditional diagnostic methods, it shows promise for
further refinement. The system offers a low-cost, scalable, and
potentially highly accurate solution that addresses critical gaps
in primary healthcare, particularly in resource-limited
environments. Integrating biomechanics data with deep
learning represents a significant advancement in gait analysis,
and continued development enhances diagnostic precision and
enables continuous monitoring of rehabilitation progress.
Implementing a bi-directional LSTM and MobileNetV2 into
gyroscopic sensors revolutionizes the detection and treatment of
gait disorders, offering significant benefits for clinical practice
and research. The findings underscore the importance of
individualized, joint-specific interventions for effective gait
rehabilitation and highlight the system’s role in advancing gait
diagnostics |
