An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease
Abstract
:1. Introduction
2. Methods
2.1. Subjects and Data Collection
2.2. Signal Processing and Classification Schemes
- Linear discriminant analysis (LDA)
- Long-short-term memory (LSTM) recurrent neural network. LSTM network parameters were set as follows: batch size = 50, number of epochs = 70, number of layers = 100
- Subject independent I: The classifiers were trained on able-bodied data and evaluated on PD patient’s data.
- Subject independent II: The classifiers were trained on PD patients’ data, leave-one-subject-out was performed across the patients for model evaluation.
- Subject dependent: Training and testing were performed within trials of each PD patient’s data using cross-validation.
- Feet
- Trunk-pelvis
- Forearms
- Signal fusion (combination of feet, trunk-pelvis, and forearms data)
2.3. System Evaluation
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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Subject Independent I | Subject Independent Ⅱ | Subject Dependent | ||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Signal Source | LDA | LSTM | LDA | LSTM | LDA | LSTM | ||||||||||||||||||
Feet | Trunk-Pelvis | Forearms | Fusion | Feet | Trunk-Pelvis | Forearms | Fusion | Feet | Trunk-Pelvis | Forearms | Fusion | Feet | Trunk-Pelvis | Forearms | Fusion | Feet | Trunk-Pelvis | Forearms | Fusion | Feet | Trunk-Pelvis | Forearms | Fusion | |
RA | 0.56 (0.07) | 0.67 (0.05) | 0.44 (0.08) | 0.63 (0.07) | 0.87 (0.1) | 0.87 (0.18) | 0.85 (0.1) | 0.86 (0.11) | 0.61 (0.08) | 0.68 (0.1) | 0.46 (0.21) | 0.78 (0.11) | 0.89 (0.07) | 0.77 (0.31) | 0.73 (0.21) | 0.91 (0.07) | 0.78 (0.09) | 0.84 (0.09) | 0.79 (0.08) | 0.92 (0.06) | 0.94 (0.06) | 0.98 (0.03) | 0.97 (0.03) | 0.99 (0.02) |
RD | 0.69 (0.08) | 0.62 (0.15) | 0.42 (0.22) | 0.78 (0.1) | 0.94 (0.04) | 0.95 (0.05) | 0.68 (0.2) | 0.95 (0.03) | 0.70 (0.04) | 0.64 (0.17) | 0.50 (0.17) | 0.70 (0.12) | 0.85 (0.14) | 0.89 (0.16) | 0.76 (0.19) | 0.95 (0.03) | 0.81 (0.07) | 0.82 (0.06) | 0.82 (0.06) | 0.90 (0.06) | 1.00 (0.01) | 0.96 (0.05) | 0.97 (0.03) | 0.99 (0.01) |
SA | 0.79 (0.08) | 0.79 (0.08) | 0.57 (0.13) | 0.91 (0.08) | 0.92 (0.04) | 0.62 (0.35) | 0.84 (0.1) | 0.92 (0.01) | 0.79 (0.12) | 0.52 (0.32) | 0.40 (0.18) | 0.74 (0.25) | 0.86 (0.08) | 0.73 (0.34) | 0.67 (0.22) | 0.85 (0.08) | 0.94 (0.05) | 0.88 (0.06) | 0.86 (0.08) | 0.95 (0.06) | 0.98 (0.03) | 0.94 (0.08) | 0.95 (0.05) | 0.98 (0.03) |
SD | 0.81 (0.1) | 0.60 (0.33) | 0.41 (0.15) | 0.90 (0.06) | 0.96 (0.02) | 0.94 (0.07) | 0.74 (0.17) | 0.95 (0.03) | 0.85 (0.04) | 0.60 (0.27) | 0.64 (0.04) | 0.68 (0.35) | 0.92 (0.05) | 0.90 (0.05) | 0.64 (0.18) | 0.91 (0.1) | 0.91 (0.04) | 0.90 (0.04) | 0.85 (0.05) | 0.93 (0.04) | 1.00 (0.01) | 0.98 (0.02) | 0.99 (0.02) | 0.99 (0.01) |
LWp | 0.39 (0.23) | 0.19 (0.22) | 0.32 (0.19) | 0.39 (0.25) | 0.90 (0.04) | 0.84 (0.21) | 0.84 (0.09) | 0.90 (0.06) | 0.55 (0.22) | 0.59 (0.27) | 0.58 (0.19) | 0.60 (0.37) | 0.83 (0.12) | 0.77 (0.33) | 0.76 (0.1) | 0.91 (0.05) | 0.65 (0.17) | 0.66 (0.17) | 0.60 (0.13) | 0.91 (0.05) | 0.90 (0.1) | 0.92 (0.11) | 0.82 (0.25) | 0.95 (0.07) |
LWf | 0.62 (0.12) | 0.89 (0.03) | 0.57 (0.09) | 0.80 (0.07) | 0.92 (0.05) | 0.92 (0.03) | 0.82 (0.1) | 0.92 (0.05) | 0.59 (0.22) | 0.74 (0.15) | 0.59 (0.2) | 0.74 (0.16) | 0.89 (0.07) | 0.88 (0.08) | 0.77 (0.13) | 0.91 (0.06) | 0.78 (0.09) | 0.87 (0.04) | 0.83 (0.05) | 0.92 (0.04) | 1.00 (0.0) | 0.98 (0.02) | 0.99 (0.01) | 0.99 (0.01) |
Subject Independent I | Subject Independent Ⅱ | Subject Dependent | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RA | RD | SA | SD | LW | RA | RD | SA | SD | LW | RA | RD | SA | SD | LW | |||
Feet | LDA | RA | 284 | 0 | 73 | 0 | 16 | 301 | 0 | 26 | 0 | 46 | 365 | 0 | 0 | 0 | 8 |
RD | 10 | 289 | 0 | 68 | 40 | 15 | 333 | 0 | 40 | 19 | 3 | 391 | 2 | 4 | 7 | ||
SA | 24 | 0 | 308 | 0 | 1 | 45 | 0 | 269 | 1 | 18 | 14 | 1 | 316 | 0 | 2 | ||
SD | 0 | 9 | 0 | 275 | 0 | 0 | 11 | 0 | 272 | 1 | 0 | 6 | 0 | 275 | 3 | ||
LWp | 69 | 92 | 12 | 4 | 88 | 29 | 100 | 4 | 3 | 129 | 57 | 73 | 11 | 1 | 123 | ||
LWf | 261 | 40 | 49 | 60 | 408 | 210 | 105 | 33 | 45 | 425 | 135 | 93 | 12 | 44 | 534 | ||
LSTM | RA | 301 | 0 | 13 | 0 | 59 | 316 | 0 | 0 | 0 | 57 | 368 | 0 | 5 | 0 | 0 | |
RD | 0 | 373 | 0 | 6 | 28 | 0 | 340 | 1 | 3 | 63 | 0 | 404 | 0 | 2 | 1 | ||
SA | 5 | 0 | 300 | 0 | 28 | 7 | 0 | 266 | 0 | 60 | 2 | 0 | 330 | 0 | 1 | ||
SD | 0 | 0 | 0 | 273 | 11 | 0 | 1 | 1 | 256 | 26 | 0 | 0 | 0 | 284 | 0 | ||
LWp | 3 | 4 | 14 | 0 | 244 | 13 | 18 | 1 | 1 | 232 | 36 | 1 | 1 | 0 | 227 | ||
LWf | 11 | 4 | 0 | 10 | 793 | 1 | 6 | 4 | 4 | 803 | 0 | 0 | 0 | 0 | 818 | ||
Trunk-pelvis | LDA | RA | 319 | 0 | 41 | 0 | 13 | 307 | 0 | 21 | 0 | 45 | 352 | 0 | 7 | 0 | 14 |
RD | 5 | 277 | 0 | 90 | 35 | 6 | 301 | 0 | 65 | 35 | 8 | 357 | 0 | 15 | 27 | ||
SA | 75 | 1 | 255 | 0 | 2 | 103 | 1 | 158 | 0 | 71 | 18 | 1 | 312 | 0 | 2 | ||
SD | 0 | 108 | 0 | 176 | 0 | 0 | 104 | 0 | 180 | 0 | 0 | 16 | 0 | 267 | 1 | ||
LWp | 152 | 55 | 10 | 6 | 42 | 16 | 55 | 28 | 5 | 161 | 34 | 29 | 65 | 2 | 135 | ||
LWf | 27 | 64 | 4 | 18 | 705 | 87 | 90 | 20 | 43 | 578 | 57 | 69 | 0 | 31 | 661 | ||
LSTM | RA | 349 | 0 | 0 | 0 | 24 | 272 | 2 | 40 | 0 | 59 | 364 | 3 | 0 | 0 | 6 | |
RD | 0 | 380 | 0 | 2 | 25 | 0 | 382 | 1 | 2 | 22 | 0 | 398 | 0 | 2 | 7 | ||
zSA | 102 | 0 | 153 | 0 | 78 | 81 | 0 | 194 | 0 | 58 | 0 | 0 | 316 | 0 | 17 | ||
SD | 0 | 8 | 0 | 272 | 4 | 0 | 64 | 0 | 203 | 17 | 0 | 0 | 0 | 275 | 9 | ||
LWp | 25 | 4 | 0 | 0 | 236 | 26 | 9 | 6 | 0 | 224 | 7 | 15 | 26 | 0 | 217 | ||
LWf | 1 | 7 | 0 | 27 | 783 | 1 | 37 | 10 | 17 | 753 | 0 | 0 | 0 | 1 | 817 | ||
Forearms | LDA | RA | 228 | 4 | 27 | 0 | 114 | 228 | 0 | 77 | 0 | 68 | 344 | 0 | 12 | 0 | 17 |
RD | 1 | 195 | 0 | 145 | 66 | 17 | 218 | 2 | 113 | 57 | 7 | 373 | 0 | 15 | 12 | ||
SA | 161 | 0 | 166 | 0 | 6 | 143 | 1 | 158 | 0 | 31 | 21 | 2 | 307 | 0 | 3 | ||
SD | 0 | 99 | 0 | 174 | 11 | 0 | 55 | 0 | 225 | 4 | 0 | 16 | 0 | 264 | 4 | ||
LWp | 96 | 100 | 2 | 10 | 57 | 14 | 62 | 20 | 16 | 153 | 42 | 59 | 56 | 1 | 107 | ||
LWf | 173 | 6 | 31 | 203 | 405 | 129 | 71 | 126 | 57 | 435 | 80 | 59 | 8 | 59 | 612 | ||
LSTM | RA | 327 | 0 | 14 | 0 | 32 | 258 | 16 | 12 | 0 | 87 | 369 | 0 | 1 | 0 | 3 | |
RD | 0 | 212 | 0 | 56 | 139 | 4 | 317 | 0 | 10 | 76 | 6 | 397 | 0 | 2 | 2 | ||
SA | 7 | 0 | 265 | 0 | 61 | 53 | 0 | 203 | 1 | 76 | 2 | 0 | 325 | 0 | 6 | ||
SD | 0 | 0 | 0 | 211 | 73 | 0 | 57 | 0 | 173 | 54 | 0 | 0 | 0 | 277 | 7 | ||
LWp | 6 | 4 | 11 | 0 | 244 | 12 | 9 | 35 | 1 | 208 | 14 | 6 | 22 | 0 | 223 | ||
LWf | 62 | 1 | 4 | 14 | 737 | 32 | 46 | 4 | 84 | 652 | 0 | 2 | 0 | 0 | 816 | ||
Fusion | LDA | RA | 324 | 0 | 25 | 0 | 24 | 330 | 0 | 5 | 0 | 38 | 370 | 0 | 0 | 0 | 3 |
RD | 28 | 277 | 0 | 25 | 77 | 6 | 332 | 0 | 19 | 50 | 2 | 378 | 1 | 7 | 19 | ||
SA | 12 | 0 | 316 | 0 | 5 | 46 | 1 | 232 | 0 | 54 | 10 | 0 | 317 | 0 | 6 | ||
SD | 0 | 2 | 0 | 281 | 1 | 0 | 73 | 0 | 206 | 5 | 0 | 5 | 0 | 277 | 2 | ||
LWp | 150 | 14 | 8 | 1 | 92 | 24 | 54 | 7 | 1 | 179 | 21 | 10 | 6 | 1 | 227 | ||
LWf | 139 | 11 | 10 | 38 | 620 | 89 | 91 | 12 | 40 | 586 | 28 | 40 | 8 | 33 | 709 | ||
LSTM | RA | 304 | 0 | 6 | 0 | 63 | 341 | 0 | 1 | 0 | 31 | 373 | 0 | 0 | 0 | 0 | |
RD | 0 | 389 | 0 | 2 | 16 | 0 | 384 | 0 | 2 | 21 | 0 | 403 | 0 | 2 | 2 | ||
SA | 6 | 0 | 298 | 0 | 29 | 23 | 0 | 254 | 0 | 56 | 0 | 0 | 330 | 0 | 3 | ||
SD | 0 | 0 | 0 | 274 | 10 | 0 | 0 | 0 | 262 | 22 | 0 | 0 | 0 | 284 | 0 | ||
LWp | 11 | 4 | 5 | 0 | 245 | 4 | 5 | 3 | 0 | 253 | 11 | 12 | 8 | 0 | 234 | ||
LWf | 1 | 15 | 2 | 15 | 785 | 9 | 8 | 4 | 17 | 780 | 1 | 1 | 0 | 0 | 816 |
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Kazemimoghadam, M.; Fey, N.P. An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease. Appl. Sci. 2022, 12, 4682. https://doi.org/10.3390/app12094682
Kazemimoghadam M, Fey NP. An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease. Applied Sciences. 2022; 12(9):4682. https://doi.org/10.3390/app12094682
Chicago/Turabian StyleKazemimoghadam, Mahdieh, and Nicholas P. Fey. 2022. "An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease" Applied Sciences 12, no. 9: 4682. https://doi.org/10.3390/app12094682
APA StyleKazemimoghadam, M., & Fey, N. P. (2022). An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease. Applied Sciences, 12(9), 4682. https://doi.org/10.3390/app12094682