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Real-Time Estimation of Lower Limb Posture and Joint Angles Using Wearable IMUs: Reproduced Hemiparetic and Normal
Shun Kanega1,2, Yoshihiro Muraoka1,2
1Faculty of Human Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Summary
This study developed a real-time lower limb posture estimation method using wearable inertial measurement units (IMUs). The system accurately estimates joint angles for functional electrical stimulation (FES) devices, demonstrating practical feasibility for gait analysis.
Area of Science:
- Biomechanics
- Rehabilitation Engineering
- Wearable Technology
Background:
- Accurate real-time estimation of lower limb posture and joint angles is crucial for developing advanced rehabilitation devices.
- Functional electrical stimulation (FES) systems require precise gait phase and joint angle data for effective therapeutic intervention.
- Existing methods may face challenges with computational load and real-time processing, limiting their integration into portable devices.
Purpose of the Study:
- To develop and evaluate a low-computational-load posture estimation method for real-time application in FES devices.
- To estimate thigh and shank inclination angles and knee joint angle during gait using wearable inertial measurement units (IMUs).
- To assess the accuracy and processing time of the proposed method for practical feasibility in gait rehabilitation.
Main Methods:
- Utilized two IMUs placed on the thigh and shank to capture gait data from healthy adults performing normal and hemiparetic-like gait (circumduction).
- Implemented a low-computational-load Madgwick filter on a microcontroller for real-time posture estimation in both sagittal and frontal planes.
- Validated estimation accuracy against optical motion capture data using root mean square error (RMSE) and cross-correlation analysis, while recording processing time.
Main Results:
- The entire process, including sampling, posture estimation, and stimulation control, averaged 6.8 ms, confirming feasibility for 100 Hz real-time processing.
- Sagittal plane posture angle estimation achieved an RMSE of less than 4°, comparable to previous studies.
- Frontal plane accuracy was higher during reproduced circumduction gait, indicating effective capture of hemiparetic gait characteristics, though accuracy was lower during normal gait.
Conclusions:
- The Madgwick filter-based posture estimation method achieves high accuracy and real-time processing capabilities suitable for integration into FES devices.
- The system's ability to capture gait deviations like circumduction highlights its potential for personalized gait rehabilitation.
- The low processing time (within 10 ms) confirms the method's practical feasibility for real-time gait analysis and intervention.
