A contactless computer vision system for underwater walking and jogging gait analysis using YOLO-pose and Multi-CNN BiLSTM architecture
Buoyancy-assisted hydrotherapy exercise has been shown to reduce joint loading and accelerate functional recovery. However, conventional marker or sensor-based approaches are costly and impractical for underwater use due to water interference and setup constraints when monitoring recovery progress m...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Universiti Teknologi MARA, Perak
2025
|
| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/128990/1/128990.pdf https://doi.org/10.24191/mij.v6i2.9665 https://ir.uitm.edu.my/id/eprint/128990/ https://mijuitm.com.my/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Buoyancy-assisted hydrotherapy exercise has been shown to reduce joint loading and accelerate functional recovery. However, conventional marker or sensor-based approaches are costly and impractical for underwater use due to water interference and setup constraints when monitoring recovery progress monitoring. To overcome these challenges, a computer vision-based gait analysis model was trained for jogging sessions in hydrotherapy pools. In this study, 2D coordinates extracted using You Only Look Once (YOLO) 11m-pose served as the model input without noise filtration to validate their robustness. A comparison of hyperparameter optimization algorithms was conducted, with the combination of multivariate tree-structured Parzen estimators (MultiTPE) and Hyperband identified as the optimal approach. Two convolutional bidirectional long short-term memory architectures, i.e., single vs. multiple convolutional layers (CNNs) per pooling were applied and compared in multi-head and single-head regression settings. Result indicated that multi-CNNs per pooling with multi-task learning best exploit inter-parameter correlations. On a 45-sample test set, the model achieved an intraclass correlation coefficient (ICC) with two-way random effects, absolute agreement, single rater model of 0.8999, Pearson’s correlation coefficient (PCC) of 0.9066, mean absolute error (MAE) of 0.0954 s for swing, stance, and stride time, while 3.5141 steps/min for cadence. The developed system thus achieves precise analysis for underwater leg movements. |
|---|