Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study

Pin-Wei Chen, Nathan A. Baune, Igor Zwir, Jiayu Wang, Victoria Swamidass and Alex W.K. Wong
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Pin-Wei Chen: PlatformSTL, St. Louis, MO 63110, USA
Nathan A. Baune: PlatformSTL, St. Louis, MO 63110, USA
Igor Zwir: Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
Jiayu Wang: Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
Victoria Swamidass: PlatformSTL, St. Louis, MO 63110, USA
Alex W.K. Wong: Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63108, USA

IJERPH, 2021, vol. 18, issue 4, 1-16

Abstract: 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.

Keywords: activities of daily living; stroke; rehabilitation; telemedicine; remote sensing technology; machine learning; wearable electronic devices (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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