Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study
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References listed on IDEAS
- 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.
- 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.
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Cited by:
- 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.
- 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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Keywords
activities of daily living; stroke; rehabilitation; telemedicine; remote sensing technology; machine learning; wearable electronic devices;All these keywords.
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