Key result
A machine learning algorithm was developed to reconstruct reliable heart rate values from PPG signals corrupted by motion artifacts in individuals with autism spectrum disorder.
Why the study?
PPG signals from wrist-worn devices are corrupted by motion artifacts, a problem amplified in individuals with autism spectrum disorder who are reluctant to wear foreign bodies and hinder proper device attachment.
Does a machine learning approach improve the reconstruction of reliable heart rate signals from PPG in users performing activities with intensive wrist movement?
Does a machine learning approach improve the reconstruction of reliable heart rate signals from PPG in users performing activities with intensive wrist movement?
A machine learning algorithm applied to data from a custom wristband can reconstruct reliable heart rate signals from PPG in the presence of motion artifacts, facilitating monitoring in populations such as individuals with autism spectrum disorder.
Hypothesis-generating for wearable HR monitoring in ASD; prospective validation needed before clinical adoption.
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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Vicente-Samper et al. (2023) studied Autism spectrum disorder (ASD). Machine learning algorithm for HR reconstruction was evaluated on Heart rate reconstruction. A machine learning algorithm was developed to reconstruct reliable heart rate values from PPG signals corrupted by motion artifacts in individuals with autism spectrum disorder.
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