Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
- Age 14.9 ± 4.4 (10.2–21.4) years;
- Weight: 37.3 ± 17.2 (21.7–76.9) kg;
- Height: 145.41 ± 23.5 (116–190) cm;
- 4 females/8 males;
- Gross Motor Function Classification System (GMFCS): II (n= 2), IV (n = 5) or V (n = 5);
- Manual Ability Classification System (MACS): II (n = 1), III (n = 3), IV (n = 2), V (n = 6).
2.2. Measurements
2.2.1. Materials
- (1)
- Mobile phone: Samsung A71 (Samsung Electronics, Daegu, South-Korea), with;
- (2)
- MODYS@home app (developed by Rutgers Engineering, Norg, The Netherlands): a custom mobile application for Android, using the Xsens DOT Software Development Kit (SDK). The app automatically links recorded videos with corresponding time stamps in the sensor data;
- (3)
- Four IMUs (Xsens DOT, Xsens Technologies B.V., Enschede, The Netherlands). Xsens DOT is a wearable sensor incorporating 3D accelerometers, gyroscopes and magnetometers to provide acceleration, angular velocity, and the Earth’s magnetic field. Combined with Xsens, sensor fusion algorithms, 3D orientation and free acceleration are provided [10]. Inertial and orientation data outputs of the Xsens DOT sensor are presented in Table 1. The Xsens DOT sensors were set to measure with a sampling frequency of 60 Hz with an accelerometer range of ±16 g and a gyroscope range of ±2000 dps;
- (4)
- Fixation material (Xsens DOT Adhesive patches (Xsens DOT, Xsens Technologies B.V., Enschede, The Netherlands), FabriFoam NuStim Wrap (Fabrifoam, Exton, PA, USA), 3m Coban self-adherent wrap (3M, St. Paul, MN, USA).
2.2.2. Procedure
2.3. Software
2.4. Clinical Scoring
2.5. Data Pre-Processing
2.6. Feature Selection and Extraction
2.7. Machine Learning and Algorithms
2.8. Training, Validating and Testing
3. Results
3.1. Datasets
3.2. Individual Clinical Scores Classification
3.3. Generalized Clinical Scores Classification
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Output | Unit |
---|---|
Free acceleration | m/s2 |
Angular velocity | degree/s |
Euler angles | degree. Roll, pitch, yaw (ZYX Euler Sequence. Earth fixed type, also known as Cardan or aerospace sequence) |
Nr | Feature Class |
---|---|
1 | Absolute harmonic mean |
2 | Absolute maximum |
3 | Bandpower |
4 | Geometric mean |
5 | Maximum |
6 | Median |
7 | Minimum |
8 | Root-mean-square |
9 | Root-sum-of-squares |
10 | Shannon entropy |
Model | Features | Hyperparameter Tuning |
---|---|---|
ML model (ALL) | All features | no |
ML model (ALL + HYP) | All features | yes |
ML model (SFS) | Selected features with SFS | no |
ML model (SFS + HYP) | Selected features with SFS | yes |
Subject | Dataset | Samples | Best Algorithm | Model | F1 Score Validation | F1 Score Test | Precision Test | Recall Test |
---|---|---|---|---|---|---|---|---|
Subject 1 | dys lower | 720 | KNN | ALL + HYP | 1 | 0.50 | 0.98 | 0.33 |
dys upper | 726 | KNN | SFS | 0.92 | 0.74 | 0.74 | 0.75 | |
Subject 2 | dys lower | 189 | KNN | ALL | 0.94 | 0.93 | 0.93 | 0.93 |
dys upper | 186 | KNN | SFS + HYP | 0.88 | 0.75 | 0.73 | 0.77 | |
Subject 4 | dys lower | 120 | KNN | ALL | 1 | 0.74 | 0.87 | 0.64 |
dys upper | 125 | KNN | SFS | 0.97 | 0.70 | 0.85 | 0.60 | |
Subject 5 | dys lower | 338 | KNN | ALL | 1 | 0.66 | 0.96 | 0.50 |
dys upper | 441 | KNN | SFS | 0.98 | 0.96 | 0.95 | 0.98 | |
Subject 6 | dys lower | 66 | n/a | n/a | n/a | n/a | n/a | n/a |
dys upper | 66 | KNN | ALL + HYP | 0.96 | 0.60 | 0.65 | 0.73 | |
Subject 7 | dys lower | 334 | KNN | ALL | 0.95 | 0.82 | 0.81 | 0.83 |
dys upper | 336 | NB | ALL + HYP | 0.97 | 0.59 | 0.73 | 0.50 | |
Subject 8 | dys lower | 336 | NB | ALL + HYP | 1 | 0.62 | 0.81 | 0.50 |
dys upper | 298 | KNN | SFS | 0.93 | 0.64 | 0.73 | 0.58 | |
Subject 9 | dys lower | 588 | KNN | ALL + HYP | 0.93 | 0.85 | 0.84 | 0.85 |
dys upper | 583 | KNN | ALL | 0.97 | 0.75 | 0.86 | 0.66 | |
Subject 10 | dys lower | 514 | n/a | n/a | n/a | n/a | 1 | 1 |
dys upper | 510 | KNN | SFS | 0.97 | 0.53 | 0.53 | 0.54 | |
Subject 11 | dys lower | 478 | KNN | ALL + HYP | 0.97 | 0.37 | 0.43 | 0.33 |
dys upper | 444 | ENS | ALL | 0.84 | 0.76 | 0.75 | 0.77 | |
Subject 12 | dys lower | 775 | KNN | SFS | 0.93 | 0.51 | 0.61 | 0.44 |
dys upper | 1237 | ENS | ALL | 0.85 | 0.46 | 0.54 | 0.41 |
Dataset | Mean F1 Score Validation | Mean F1 Score Test | Mean Precision Test | Mean Recall Test |
---|---|---|---|---|
dys lower | 0.97 ± 0.03 | 0.67 ± 0.19 | 0.82 ± 0.18 | 0.66 ± 0.26 |
dys upper | 0.93 ± 0.06 | 0.68± 0.14 | 0.73 ± 0.13 | 0.66 ± 0.16 |
Dataset | Samples | Best Algorithm | Model | F1 Score Validation | F1 Score Test | Precision Test | Recall Test |
---|---|---|---|---|---|---|---|
dys lower | 4533 | ENS | SFS | 0.93 | 0.45 | 0.43 | 0.48 |
dys upper | 4976 | KNN | SFS | 0.91 | 0.34 | 0.32 | 0.36 |
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den Hartog, D.; van der Krogt, M.M.; van der Burg, S.; Aleo, I.; Gijsbers, J.; Bonouvrié, L.A.; Harlaar, J.; Buizer, A.I.; Haberfehlner, H. Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. Sensors 2022, 22, 4386. https://doi.org/10.3390/s22124386
den Hartog D, van der Krogt MM, van der Burg S, Aleo I, Gijsbers J, Bonouvrié LA, Harlaar J, Buizer AI, Haberfehlner H. Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. Sensors. 2022; 22(12):4386. https://doi.org/10.3390/s22124386
Chicago/Turabian Styleden Hartog, Dylan, Marjolein M. van der Krogt, Sven van der Burg, Ignazio Aleo, Johannes Gijsbers, Laura A. Bonouvrié, Jaap Harlaar, Annemieke I. Buizer, and Helga Haberfehlner. 2022. "Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study" Sensors 22, no. 12: 4386. https://doi.org/10.3390/s22124386
APA Styleden Hartog, D., van der Krogt, M. M., van der Burg, S., Aleo, I., Gijsbers, J., Bonouvrié, L. A., Harlaar, J., Buizer, A. I., & Haberfehlner, H. (2022). Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. Sensors, 22(12), 4386. https://doi.org/10.3390/s22124386