ArticleInternational journal of environmental research and public health2021
Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Measured and Perceived Effects of Upper Limb Home-Based Exergaming Interventions on Activity after Stroke: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2022Pooled it
- User Needs for an Artificial Intelligence-Empowered Basic Activities of Daily Living Training System After Stroke: A Qualitative Study.Journal of multidisciplinary healthcare · 2026Article
- Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning.Archives of academic emergency medicine · 2025Article
- Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Automated Detection of In-Home Activities with Ultra-Wideband Sensors.Sensors (Basel, Switzerland) · 2024Article
- Smartphone assessment uncovers real-time relationships between depressed mood and daily functional behaviors after stroke.Journal of telemedicine and telecare · 2024Article
- SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies.World neurosurgery: X · 2024Review
- Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation.Sensors (Basel, Switzerland) · 2023Article
- Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.Sensors (Basel, Switzerland) · 2023Article
- Review
- XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2023Article
- A deep learning wearable-based solution for continuous at-home monitoring of upper limb goal-directed movements.Frontiers in neurology · 2023Article
- Visualization-Driven Time-Series Extraction from Wearable Systems Can Facilitate Differentiation of Passive ADL Characteristics among Stroke and Healthy Older Adults.Sensors (Basel, Switzerland) · 2022Article
- Effectiveness of Integrated Nursing Strategies on Clinical Outcomes and Health-Related Quality of Life Among Patients with Stroke: A Pilot Randomized Controlled Study.Journal of pharmacy & bioallied sciencesArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.