ArticleSensors (Basel, Switzerland)2020
Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Advancements in Wearable Sensor Technologies for Health Monitoring in Terms of Clinical Applications, Rehabilitation, and Disease Risk Assessment: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Machine Learning-Based Risk Stratification Model to Guide Cardiopulmonary Exercise Training in Cardiovascular Patients.Yonsei medical journal · 2026Article
- Meta-heuristic and machine learning based functional capacity prediction using gait parameters in patients with heart failure.Scientific reports · 2026Article
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- Article
- Machine Learning Application to Predict Bicycle Ergometer Test Results: a Prospective Cohort Study.Sovremennye tekhnologii v meditsine · 2026Article
- Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation.Clinical Medicine Insights. Cardiology · 2026Review
- Development and validation of a multidimensional tool for baseline functional phenotyping in cardiac rehabilitation.BMC cardiovascular disorders · 2025Article
- Long-Term Outcomes of Digital Cardiac Rehabilitation.JACC. Asia · 2025Review
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Design Requirements for Cardiac Telerehabilitation Technologies Supporting Athlete Values: Qualitative Interview Study.JMIR rehabilitation and assistive technologies · 2025Article
- Exploring the experience of community hospital-led home-based cardiac rehabilitation health management in patients with coronary heart disease: a qualitative focus group study.Frontiers in cardiovascular medicine · 2025Article
- Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives.Frontiers in public health · 2025Article
- Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied-An Exploratory Study.Journal of clinical medicine · 2024Article
- Systematic review of Internet of medical things for cardiovascular disease prevention among Australian first nations.Heliyon · 2023Review
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.Journal of clinical medicine · 2023Review
- Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.
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Registered trials
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