| Project period | 2022-2024 |
| Project Duration | 2 year |
| Funding Source | Simula Research Laboratory, Oslo, Norway (DL-SIM 2022) |
| Principle Applicant | Design Lab, DoME, Kathmandu University |
Objectives
1. To develop a system for Human Gait Recognition and Analysis using computer vision and sensor based wearables.
2. To Classify Gait Deviation using Machine Learning.
Current Status and Outputs
Knee flexion/extension angle is measured using Pose Estimation Machine Learning(ML) Model called “MediaPipe Pose” (Marker-less Approach) and its efficacy is tested by comparing the measured knee angles with output from motion analysis software “Kinovea® ” (Marker-Based Approach)
