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

Author

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  • Pin-Wei Chen

    (PlatformSTL, St. Louis, MO 63110, USA
    Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63108, USA
    These authors are co-first authors.)

  • Nathan A. Baune

    (PlatformSTL, St. Louis, MO 63110, USA
    Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63108, USA
    These authors are co-first authors.)

  • Igor Zwir

    (Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
    Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain)

  • 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
    Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
    Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
    Center for Rehabilitation Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USA)

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.

Suggested Citation

  • Pin-Wei Chen & Nathan A. Baune & Igor Zwir & Jiayu Wang & Victoria Swamidass & Alex W.K. Wong, 2021. "Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1634-:d:496074
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    References listed on IDEAS

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    1. Manal Alghamdi & Mouaz Al-Mallah & Steven Keteyian & Clinton Brawner & Jonathan Ehrman & Sherif Sakr, 2017. "Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
    2. Nicole A Capela & Edward D Lemaire & Natalie Baddour, 2015. "Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-18, April.
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    Cited by:

    1. Axelle Gelineau & Anaick Perrochon & Louise Robin & Jean-Christophe Daviet & Stéphane Mandigout, 2022. "Measured and Perceived Effects of Upper Limb Home-Based Exergaming Interventions on Activity after Stroke: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    2. Xiaoming Yang & Shamsulariffin Samsudin & Yuxuan Wang & Yubin Yuan & Tengku Fadilah Tengku Kamalden & Sam Shor Nahar bin Yaakob, 2023. "Application of Target Detection Method Based on Convolutional Neural Network in Sustainable Outdoor Education," Sustainability, MDPI, vol. 15(3), pages 1-21, January.

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