Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots
Abstract
:1. Introduction
- This paper proposes the use of a non-parametric modeling approach (long short-term memory (LSTM) and gated recurrent unit (GRU) network) to learn the inverse dynamics model of a robot-like human lower limb.
- Comparing the learning effects of the two neural networks, we can obtain that, under the same number of iterations, the GRU network requires a shorter training time than the LSTM network, and the learned model works better.
- The motion data of the lower limbs of a healthy person were captured by a 3D motion capture system, and the captured data were processed to obtain data that can be used for model learning.
2. Methodology
2.1. Framework
2.2. Problem Formulation
2.3. Inverse Dynamics Model Learning
2.3.1. Long Short Term Memory
2.3.2. Gated Recurrent Unit
2.3.3. Proposed Learning Architecture
3. Experiment
3.1. Experimental Subject
3.2. Data Collection
3.3. Preprocessing of Collected Data
- Filtering and fitting:To reduce the noise disturbance of the raw signal, we must filter the data using the acquisition system’s software Seeker. If the data frame is lost during processing, the cubic spline difference needs to be performed to ensure the accuracy of the data. If the data frame is lost too much, this set of data is directly discarded.
- Removing invalid data:Data can be lost or misplaced if the marker point is obscured by a swinging arm or if the foot is off the force measuring platform when collecting data. Additionally, there are four force plates, with a space between each couple. If the subject walks between the force plates, the gait data and plantar force data acquired will be erroneous, particularly the sole force data. Therefore, not all gait and plantar force data are valid and we need to delete these invalid data and remove them manually.
- Data processing of human lower limb joint angles, angular velocities, and angular accelerations:In this paper, an inverse dynamics analysis was used to model the human lower limb as a two-linked rod (Figure 9) using data on the position of the ends of individual joints at different moments in time, captured by a 3D motion capture system during human movement. Equations (16) and (17) calculate the joint angles of the hip and knee at different moments (Figure 10). To ensure that the derived trajectory data can be used directly in LLRRs, the derived joint angles were fitted by Fourier function curves (Equation (18)). The fitted Fourier function was derived to obtain the joint angular velocities of the hip and knee joints, and the quadratic derivative was used to acquire the corresponding accelerations (Figure 11).
- Human gait force data processing:The force data and of the joint end position of the subject’s lower limb were measured by the 3D force measurement platform. The moment components and of the two joints end positions were calculated (Equation (19)). Finally, the two joint moments (Figure 12) were derived from the Jacobian matrix (Equation (20)).
3.4. Experimental Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Input Value | Data Bulk | Target Value | Data Bulk |
---|---|---|---|
Hip joint angle | 3451 | Moment of hip joint | 3451 |
Hip joint angular velocity | 3451 | ||
Angular acceleration of hip joint | 3451 | ||
Knee joint angle | 3451 | Moment of knee joint | 3451 |
Knee joint angular velocity | 3451 | ||
Angular acceleration of Knee joint | 3451 |
Train Ratio | Method | Epoch | Time (s) | RMSE | |
---|---|---|---|---|---|
27,608 samples | 0.9 | LSTM | 100 | 3.12 s | 0.19334 |
GRU | 100 | 1.54 s | 0.16905 | ||
0.8 | LSTM | 100 | 4.23 s | 0.17525 | |
GRU | 100 | 1.59 s | 0.1571 |
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Song, L.; Wang, A.; Zhong, J. Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots. Electronics 2022, 11, 3848. https://doi.org/10.3390/electronics11233848
Song L, Wang A, Zhong J. Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots. Electronics. 2022; 11(23):3848. https://doi.org/10.3390/electronics11233848
Chicago/Turabian StyleSong, Lulu, Aihui Wang, and Junpei Zhong. 2022. "Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots" Electronics 11, no. 23: 3848. https://doi.org/10.3390/electronics11233848
APA StyleSong, L., Wang, A., & Zhong, J. (2022). Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots. Electronics, 11(23), 3848. https://doi.org/10.3390/electronics11233848