Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Acquisition and Processing
2.1.1. Data Collection
2.1.2. Gait Phase Variable
2.1.3. Knee Angle and GRF value
2.2. Feature Distributions and Extractions
2.2.1. Thigh Phase Diagram Shape (TPDS)
2.2.2. Knee Angle Trajectory (KAT)
2.2.3. Center Position Offset (CPO)
2.2.4. Ground Reaction Force Peak Value (GRFPV)
3. Results
3.1. ST Classifier
3.2. ANN Classifier
4. Discussion
4.1. Network Hyperparameters
4.2. Ramp Slope
4.3. Time Window Length
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Abbreviations | Full Names | Abbreviations | Full Names |
---|---|---|---|
LW | level walking | SA | stair ascent |
SD | stair descent | RA | ramp ascent |
RD | ramp descent | ST | standing |
TPDS | thigh phase diagram shape | KAT | knee angle trajectory |
CPO | center position offset | GRFPV | ground reaction force peak value |
ANN | artificial neural network | WHO | world health organization |
LLA | lower-limb amputation | PR | pattern recognition |
ML | machine learning | sEMG | surface electromyogram |
EFRS | environmental feature recognition system | LDA | linear discriminant analysis |
QDA | quadratic discriminant analysis | GMM | Gaussian mixture model |
DBN | dynamic Bayesian network | IMU | inertial measurement unit |
GRF | ground reaction force | CNN | convolutional neural network |
DL-based | deep learning based | LG | level ground |
M | man | W | woman |
DT | dynamic trend |
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Symbol | Quantity |
---|---|
Total number of modes except for ST () | |
Thigh IMU angular velocity in the sagittal plane | |
Locomotion mode. | |
The sampling period and | |
The length of the time window. |
Testing Accuracy | Locomotion Modes | ||||||
---|---|---|---|---|---|---|---|
LW | SA | SD | RA | RD | Total | ||
Subjects M = Man W = Woman | M1 | 100.0 | 99.89 | 99.08 | 99.10 | 98.71 | 99.36 |
M2 | 100.0 | 99.67 | 99.90 | 97.55 | 97.59 | 98.94 | |
M3 | 100.0 | 99.71 | 99.11 | 98.88 | 98.65 | 99.27 | |
M4 | 100.0 | 99.15 | 99.19 | 97.73 | 97.32 | 98.68 | |
M5 | 99.99 | 99.89 | 99.39 | 97.36 | 96.90 | 98.71 | |
M6 | 100.0 | 100.0 | 99.59 | 99.33 | 99.58 | 99.70 | |
W1 | 100.0 | 98.69 | 99.53 | 100.0 | 99.63 | 99.57 | |
W2 | 99.94 | 98.48 | 98.85 | 99.16 | 98.75 | 99.04 |
Confusion Matrix | Predicted Class | ||||||
---|---|---|---|---|---|---|---|
LW | SA | SD | RA | RD | None | ||
Actual Class | LW | 99.99 | 0.01 | ||||
SA | 99.44 | 0.56 | |||||
SD | 0.67 | 99.33 | |||||
RA | 0.05 | 98.64 | 1.30 | 0.01 | |||
RD | 1.61 | 98.38 | 0.01 |
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Liu, Y.; An, H.; Ma, H.; Wei, Q. Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis. Machines 2023, 11, 235. https://doi.org/10.3390/machines11020235
Liu Y, An H, Ma H, Wei Q. Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis. Machines. 2023; 11(2):235. https://doi.org/10.3390/machines11020235
Chicago/Turabian StyleLiu, Yi, Honglei An, Hongxu Ma, and Qing Wei. 2023. "Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis" Machines 11, no. 2: 235. https://doi.org/10.3390/machines11020235