Key result
Wearable HRV machine learning detects stress and drowsiness with up to ~85% accuracy during motion.
Why the study?
Heart rate variability reflects autonomic nervous system activity, but research gaps remain in optimizing signal processing and machine learning processes across clinical and ambulatory states.
While HRV analysis via wearable sensors and machine learning shows promise for detecting stress and morbidity, significant challenges remain regarding signal accuracy during motion.
May support wearable HRV-ML for stress detection in controlled settings; leaves open motion validation before clinical use.
Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.
No takes yet. Share an insight, caveat, or question.
Ishaque et al. (2023) conducted a review in Heart Rate Variability (HRV) analysis. HRV signal analysis was evaluated. Heart rate variability signal analysis using wearable devices and machine learning can detect physiological states like stress and drowsiness, though accuracy in motion ranges from 59% to 85%.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: