Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participant Recruitment
2.2. Experimental Instrumentation and Procedure
2.3. Data Preprocessing
2.3.1. Gait Event Detection
2.3.2. Gait Parameter Extraction
2.4. Feature Selection and Classification
2.4.1. Feature Selection
2.4.2. Classification
2.4.3. Post-Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Participant | Age (Years) | Height (cm) | Weight (kg) |
---|---|---|---|
1 | 30 | 176 | 93 |
2 | 59 | 168 | 72 |
3 | 33 | 188 | 82 |
4 | 43 | 177 | 65 |
5 | 35 | 178 | 73 |
6 | 53 | 165 | 65 |
7 | 46 | 176 | 65 |
8 | 27 | 174 | 95 |
9 | 28 | 181 | 100 |
10 | 40 | 173 | 80 |
System | Categories | Parameters |
---|---|---|
IMU-based | Angular | shank sagittal angle (deg), thigh sagittal angle (deg), knee sagittal angle (deg) |
Spatio-temporal | cycle time (s), swing time (s), stance time (s), swing phase, stance phase, cadence (steps/min) | |
Symmetry | SSR 1, SSR 2, TSR | |
Walkway | Spatio-temporal | step length (cm), stride length (cm), base of support (cm), step time (s), stride time (s), swing time (s), stance time (s), single support time (s), double support time (s), stride velocity (cm/s), swing phase, stance phase |
Symmetry | TSR |
System | Types | Classifier | Optimal Number of Features | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|---|---|---|
IMU-based | normal -abnormal | SVM | 1 | 0.88 | 0.90 | 0.88 | 0.88 |
RF | 12 | 0.99 | 0.99 | 0.99 | 0.99 | ||
XGB | 5 | 0.96 | 0.97 | 0.96 | 0.96 | ||
normal -knee | SVM | 1 | 1.00 | 1.00 | 1.00 | 1.00 | |
RF | 1 | 1.00 | 1.00 | 1.00 | 1.00 | ||
XGB | 1 | 0.99 | 1.00 | 0.99 | 0.99 | ||
normal -ankle | SVM | 1 | 0.87 | 0.90 | 0.87 | 0.87 | |
RF | 2 | 0.97 | 0.98 | 0.97 | 0.97 | ||
XGB | 7 | 0.93 | 0.94 | 0.93 | 0.93 | ||
knee -ankle | SVM | 5 | 0.97 | 0.97 | 0.97 | 0.97 | |
RF | 18 | 0.98 | 0.98 | 0.98 | 0.98 | ||
XGB | 3 | 0.98 | 0.98 | 0.98 | 0.98 | ||
normal -knee -ankle | SVM | 6 | 0.90 | 0.90 | 0.90 | 0.90 | |
RF | 11 | 0.91 | 0.91 | 0.91 | 0.91 | ||
XGB | 5 | 0.89 | 0.90 | 0.89 | 0.89 | ||
Walkway | normal -abnormal | SVM | 1 | 0.90 | 0.92 | 0.90 | 0.91 |
RF | 15 | 0.90 | 0.90 | 0.90 | 0.90 | ||
XGB | 12 | 0.90 | 0.90 | 0.90 | 0.90 | ||
normal -knee | SVM | 1 | 0.92 | 0.93 | 0.92 | 0.92 | |
RF | 3 | 0.94 | 0.94 | 0.94 | 0.93 | ||
XGB | 2 | 0.93 | 0.93 | 0.93 | 0.93 | ||
normal -ankle | SVM | 12 | 0.91 | 0.91 | 0.91 | 0.91 | |
RF | 4 | 0.91 | 0.91 | 0.91 | 0.90 | ||
XGB | 4 | 0.90 | 0.90 | 0.90 | 0.90 | ||
knee -ankle | SVM | 9 | 0.76 | 0.76 | 0.76 | 0.76 | |
RF | 19 | 0.75 | 0.75 | 0.75 | 0.74 | ||
XGB | 10 | 0.75 | 0.75 | 0.75 | 0.75 | ||
normal -knee -ankle | SVM | 9 | 0.73 | 0.73 | 0.73 | 0.73 | |
RF | 10 | 0.75 | 0.75 | 0.75 | 0.75 | ||
XGB | 21 | 0.77 | 0.77 | 0.77 | 0.77 |
Types | IMU-Based System Features | Walkway System Features |
---|---|---|
normal-abnormal | TSR | TSR |
normal-knee | Knee sagittal angle (R) | TSR |
normal-ankle | Swing time (R) | |
knee-ankle | Shank sagittal angle (R) | Stance time (L), stance time (R), swing phase (L), swing phase (R), double support time (L), double support time (R) |
normal-knee-ankle | Knee sagittal angle (R) | TSR, swing phase (R), double support time (R) |
System | Classifier | Fold | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|---|---|
IMU-based | RF | 1 | 0.93 | 0.94 | 0.93 | 0.94 |
2 | 0.97 | 0.97 | 0.97 | 0.97 | ||
3 | 0.83 | 0.88 | 0.83 | 0.86 | ||
4 | 0.95 | 0.96 | 0.95 | 0.95 | ||
5 | 0.85 | 0.90 | 0.85 | 0.87 | ||
Walkway | XGB | 1 | 0.85 | 0.88 | 0.85 | 0.86 |
2 | 0.80 | 0.80 | 0.80 | 0.80 | ||
3 | 0.68 | 0.81 | 0.68 | 0.74 | ||
4 | 0.75 | 0.81 | 0.75 | 0.78 | ||
5 | 0.75 | 0.75 | 0.75 | 0.75 |
System | Feature | Side | Normal | Knee | Ankle | Friedman | |
---|---|---|---|---|---|---|---|
Statistic | p-Value | ||||||
IMU-based | Shank sagittal angle (deg) | L | 68.51 ± 5.06 | 63.98 ± 5.36 | 59.51 ± 5.58 | 16.8 | 0.0002 |
R | 66.99 ± 5.0 | 47.66 ± 5.33 | 51.14 ± 12.3 | 12.6 | 0.0018 | ||
Thigh sagittal angle (deg) | L | 35.27 ± 4.31 | 34.24 ± 3.25 | 34.3 ± 3.9 | 0.2 | 0.9048 | |
R | 36.26 ± 4.72 | 38.96 ± 4.22 | 37.18 ± 5.7 | 2.4 | 0.3012 | ||
Knee sagittal angle (deg) | L | 57.57 ± 5.87 | 53.74 ± 5.95 | 49.77 ± 6.6 | 16.8 | 0.0002 | |
R | 55.63 ± 6.97 | 12.81 ± 4.88 | 36.68 ± 14.58 | 18.2 | 0.0001 | ||
Cycle time (s) | L | 1.12 ± 0.04 | 1.25 ± 0.12 | 1.33 ± 0.12 | 11.4 | 0.0033 | |
R | 1.12 ± 0.05 | 1.25 ± 0.12 | 1.33 ± 0.13 | 12.8 | 0.0017 | ||
Cadence (steps/min) | L | 53.83 ± 2.13 | 48.53 ± 4.61 | 45.4 ± 4.33 | 11.4 | 0.0033 | |
R | 53.71 ± 2.35 | 48.57 ± 4.67 | 45.41 ± 4.45 | 12.8 | 0.0017 | ||
Swing time (s) | L | 0.49 ± 0.03 | 0.49 ± 0.03 | 0.48 ± 0.04 | 0.6 | 0.7408 | |
R | 0.49 ± 0.02 | 0.61 ± 0.07 | 0.59 ± 0.06 | 15.2 | 0.0005 | ||
Stance time (s) | L | 0.63 ± 0.03 | 0.76 ± 0.11 | 0.86 ± 0.1 | 14.6 | 0.0007 | |
R | 0.63 ± 0.04 | 0.64 ± 0.06 | 0.74 ± 0.08 | 15.0 | 0.0006 | ||
Swing phase | L | 0.44 ± 0.02 | 0.4 ± 0.03 | 0.36 ± 0.03 | 16.8 | 0.0002 | |
R | 0.44 ± 0.01 | 0.49 ± 0.03 | 0.44 ± 0.03 | 15.8 | 0.0004 | ||
Stance phase | L | 0.56 ± 0.02 | 0.6 ± 0.03 | 0.64 ± 0.03 | 16.8 | 0.0002 | |
R | 0.56 ± 0.01 | 0.51 ± 0.03 | 0.56 ± 0.03 | 15.8 | 0.0004 | ||
SSR 1 | 0.96 ± 0.1 | 0.66 ± 0.07 | 0.79 ± 0.14 | 14.6 | 0.0007 | ||
SSR 2 | 0.98 ± 0.15 | 0.24 ± 0.08 | 0.75 ± 0.34 | 18.2 | 0.0001 | ||
TSR | 1.0 ± 0.08 | 1.49 ± 0.29 | 1.45 ± 0.28 | 15.2 | 0.0005 | ||
Walkway | Step length (cm) | L | 63.89 ± 4.26 | 56.56 ± 5.16 | 49.28 ± 8.49 | 18.2 | 0.0001 |
R | 63.67 ± 4.55 | 57.63 ± 4.72 | 55.48 ± 5.65 | 9.8 | 0.0074 | ||
Stride length (cm) | L | 128.04 ± 8.89 | 114.89 ± 8.48 | 105.4 ± 12.37 | 16.8 | 0.0002 | |
R | 128.05 ± 8.72 | 114.3 ± 8.54 | 104.7 ± 12.27 | 16.8 | 0.0002 | ||
Base of support (cm) | L | 11.59 ± 3.14 | 17.59 ± 4.28 | 18.83 ± 3.32 | 15.2 | 0.0005 | |
R | 11.63 ± 3.11 | 17.44 ± 4.43 | 18.68 ± 3.23 | 15.2 | 0.0005 | ||
Step time (s) | L | 0.57 ± 0.03 | 0.59 ± 0.04 | 0.63 ± 0.07 | 9.8 | 0.0074 | |
R | 0.57 ± 0.03 | 0.67 ± 0.08 | 0.72 ± 0.08 | 16.8 | 0.0002 | ||
Stride time (s) | L | 1.13 ± 0.05 | 1.25 ± 0.12 | 1.35 ± 0.13 | 12.8 | 0.0017 | |
R | 1.13 ± 0.05 | 1.26 ± 0.12 | 1.35 ± 0.13 | 12.8 | 0.0017 | ||
Stride velocity (cm/s) | L | 113.75 ± 9.98 | 92.89 ± 14.27 | 79.11 ± 13.07 | 16.8 | 0.0002 | |
R | 113.95 ± 10.01 | 92.09 ± 14.4 | 78.56 ± 13.12 | 16.8 | 0.0002 | ||
Double support time (s) | L | 0.26 ± 0.02 | 0.29 ± 0.05 | 0.38 ± 0.06 | 16.8 | 0.0002 | |
R | 0.26 ± 0.03 | 0.29 ± 0.05 | 0.38 ± 0.06 | 16.8 | 0.0002 | ||
Single support time (s) | L | 0.43 ± 0.02 | 0.54 ± 0.07 | 0.54 ± 0.06 | 15.2 | 0.0005 | |
R | 0.43 ± 0.02 | 0.43 ± 0.03 | 0.43 ± 0.05 | 0.6 | 0.7408 | ||
Swing time (s) | L | 0.43 ± 0.02 | 0.43 ± 0.03 | 0.43 ± 0.05 | 0.6 | 0.7408 | |
R | 0.43 ± 0.02 | 0.54 ± 0.07 | 0.54 ± 0.06 | 15.2 | 0.0005 | ||
Stance time (s) | L | 0.7 ± 0.04 | 0.82 ± 0.11 | 0.92 ± 0.1 | 12.8 | 0.0017 | |
R | 0.7 ± 0.04 | 0.72 ± 0.06 | 0.8 ± 0.09 | 13.4 | 0.0012 | ||
Swing phase | L | 0.38 ± 0.01 | 0.35 ± 0.03 | 0.32 ± 0.03 | 16.8 | 0.0002 | |
R | 0.38 ± 0.01 | 0.42 ± 0.02 | 0.4 ± 0.03 | 11.4 | 0.0033 | ||
Stance phase | L | 0.62 ± 0.01 | 0.65 ± 0.03 | 0.68 ± 0.03 | 16.8 | 0.0002 | |
R | 0.62 ± 0.01 | 0.58 ± 0.02 | 0.6 ± 0.03 | 11.4 | 0.0033 | ||
TSR | 1.0 ± 0.06 | 1.41 ± 0.23 | 1.5 ± 0.33 | 15.8 | 0.0004 |
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Hwang, S.; Kim, J.; Yang, S.; Moon, H.-J.; Cho, K.-H.; Youn, I.; Sung, J.-K.; Han, S. Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment. Sensors 2024, 24, 5571. https://doi.org/10.3390/s24175571
Hwang S, Kim J, Yang S, Moon H-J, Cho K-H, Youn I, Sung J-K, Han S. Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment. Sensors. 2024; 24(17):5571. https://doi.org/10.3390/s24175571
Chicago/Turabian StyleHwang, Soree, Jongman Kim, Sumin Yang, Hyuk-June Moon, Kyung-Hee Cho, Inchan Youn, Joon-Kyung Sung, and Sungmin Han. 2024. "Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment" Sensors 24, no. 17: 5571. https://doi.org/10.3390/s24175571