ArticleSensors (Basel, Switzerland)2018
Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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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
26 citing papers in PubMed.
- Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization.Sensors (Basel, Switzerland) · 2026Article
- Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination.Sensors (Basel, Switzerland) · 2025Article
- Machine learning applications in the analysis of sedentary behavior and associated health risks.Frontiers in artificial intelligence · 2025Review
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach.Sensors (Basel, Switzerland) · 2024Article
- How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?Sensors (Basel, Switzerland) · 2023Review
- Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments.Sensors (Basel, Switzerland) · 2023Article
- Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.Sensors (Basel, Switzerland) · 2022Review
- Inertial Data-Based AI Approaches for ADL and Fall Recognition.Sensors (Basel, Switzerland) · 2022Article
- Wearables and their applications for the rehabilitation of elderly people.Medical & biological engineering & computing · 2022Review
- Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?Sensors (Basel, Switzerland) · 2022Article
- Context-Aware Probabilistic Models for Predicting Future Sedentary Behaviors of Smartphone Users.Journal of healthcare informatics research · 2022Article
- Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Cardiovascular and Pre-Frailty Risk Assessment during Shelter-In-Place Measures Based on Multimodal Biomarkers Collected from Smart Telemedical Wearables.Journal of clinical medicine · 2021Article
- Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model.Sensors (Basel, Switzerland) · 2021Article
- Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare.Sensors (Basel, Switzerland) · 2021Article
- Directing and Orienting ICT Healthcare Solutions to Address the Needs of the Aging Population.Healthcare (Basel, Switzerland) · 2021Review
- Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques.Sensors (Basel, Switzerland) · 2021Review
- The Internet of Things in Geriatric Healthcare.Journal of healthcare engineering · 2021Review
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.
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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.