Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.
Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.
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DOI：
10.3390/ijerph18041634
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发表时间：
2021-02-09
影响因子：
--
通讯作者：
Wong AWK
中科院分区：
文献类型：
--
作者：
Chen PW;Baune NA;Zwir I;Wang J;Swamidass V;Wong AWK
使用可穿戴技术测量日常生活活动（ADL）可能比目前中风后患者的临床评估提供更高的精度和粒度。本研究旨在开发和确定使用机器学习（ML）算法和可穿戴传感器检测不同ADL的准确性。11名中风后患者在ADL模拟实验室参加了这项试点研究，两次研究访视。我们收集了重复活动（“原子”活动）性能数据块，以在一次访问期间训练我们的ML算法。我们使用在单独的会话中收集的独立半自然主义活动数据来评估我们的ML算法。我们测试了决策树，随机森林，支持向量机（SVM）和极端梯度提升（XGBoost）用于模型开发。XGBoost是最好的分类模型。基于10个ADL任务，我们实现了82%的准确率。使用包括七个任务的模型，准确率提高到90%。ADL任务包括切食物、吸尘、扫地、涂果酱或黄油、叠衣服、吃饭、刷牙、脱/穿衬衫、擦橱柜和扣衬衫。结果提供了初步证据，ADL功能可以预测足够的准确性，使用可穿戴传感器和ML。使用外部验证（独立的训练和测试数据集）和半自然主义测试数据是该研究的主要优势，也是向现实环境中ADL监测的长期目标迈出的一步。需要进一步的研究来提高ADL预测的准确性，增加监测的任务数量，并在实验室环境之外测试模型。
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.
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DOI：
10.1016/j.jht.2012.06.005
发表时间：
2013-04
期刊：
Journal of hand therapy : official journal of the American Society of Hand Therapists
影响因子：
--
作者：
Lang CE;Bland MD;Bailey RR;Schaefer SY;Birkenmeier RL
通讯作者：
Birkenmeier RL
影响因子：
3.7
作者：
Alghamdi M;Al-Mallah M;Keteyian S;Brawner C;Ehrman J;Sakr S
通讯作者：
Sakr S
影响因子：
--
作者：
Sager, MA;Franke, T;Winograd, CH
通讯作者：
Winograd, CH
DOI：
10.1093/geronj/39.6.686
发表时间：
1984-01-01
期刊：
JOURNALS OF GERONTOLOGY
影响因子：
--
作者：
RUBENSTEIN, LZ;SCHAIRER, C;KANE, R
通讯作者：
KANE, R
影响因子：
4.1
作者：
Galperin, Irina;Hillel, Inbar;Hausdorff, Jeffrey M.
通讯作者：
Hausdorff, Jeffrey M.