TY - JOUR
T1 - Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning
T2 - A Proof-of-Concept Study
AU - den Hartog, Dylan
AU - van der Krogt, Marjolein M
AU - van der Burg, Sven
AU - Aleo, Ignazio
AU - Gijsbers, Johannes
AU - Bonouvrié, Laura A
AU - Harlaar, Jaap
AU - Buizer, Annemieke I
AU - Haberfehlner, Helga
N1 - Funding Information: Funding: The project was funded by the Netherlands Organization for Health Research and Devel‐ opment (ZonMW, Innovative medical device initiative (IMDI) project number 104022005). Move‐ shelf Labs B.V. (Utrecht, NL) provided support and in‐kind contribution to the project. Support is provided by the Small‐Scale Initiatives in Machine Learning (OpenSSI 2021) of the Netherlands eSci‐ ence Center. Helga Haberfehlner is funded by the Postdoctoral Fellow Marie Skłodowska‐Curie Actions—Seal of Excellence of the Research Foundation—Flanders (SoE fellowship_12ZZW22N). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor's office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0-4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.
AB - Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor's office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0-4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.
KW - cerebral palsy
KW - choreoathetosis
KW - dystonia
KW - home‐based
KW - inertial measurement unit
KW - machine learning
KW - wearable device
UR - http://www.scopus.com/inward/record.url?scp=85131548893&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/s22124386
DO - https://doi.org/10.3390/s22124386
M3 - Article
C2 - 35746168
SN - 1424-8220
VL - 22
JO - Sensors (Basel, Switzerland)
JF - Sensors (Basel, Switzerland)
IS - 12
M1 - 4386
ER -