ArticleBiosensors2023
An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts.
Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed, 15 citations in OpenAlex.
- Emergency Clinical Decision for Sports Injury Management: A Wearable Sensor-Driven Framework from Training to Rehabilitation.Biosensors · 2026Review
- Butterworth Filtering at 500 Hz Optimizes PPG-Based Heart Rate Variability Analysis for Wearable Devices: A Comparative Study.Sensors (Basel, Switzerland) · 2025Article
- Advancements in Sensor Technology for Monitoring and Management of Chronic Coronary Syndrome.Sensors (Basel, Switzerland) · 2025Review
- Machine Learning-Based VOBiosensors · 2025Article
- Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review.Biosensors · 2024Review
- Diagnostic Accuracy of a Portable Electromyography and Electrocardiography Device to Measure Sleep Bruxism in a Sleep Apnea Population: A Comparative Study.Clocks & sleep · 2023Article
- Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2023Article
- Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2023Article
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Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
The heart rate (HR) is a widely used clinical variable that provides important information on a physical user's state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user's wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user's HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.
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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.