Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study
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Activities of daily livingStrokeRehabilitationTelemedicineRemote sensing technologyMachine learningWearable electronic devices
Chen, P.-W.; Baune, N.A.; Zwir, I.; Wang, J.; Swamidass, V.; Wong, A.W.K. Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study. Int. J. Environ. Res. Public Health 2021, 18, 1634. [https://doi.org/10.3390/ijerph18041634]
PatrocinadorUnited States Department of Health & Human Services 90BISA0015; United States Department of Health & Human Services National Institutes of Health (NIH) - USA K01HD095388
Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity (“atomic” activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.