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Measuring vascular recovery rate after exercise
1 , 1 , * 2
1  Department of Biomedical Engineering, King’s College London, King’s Health Partners, London
2  Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, King’s Health Partners, London SE1 7EH, UK


The rate at which an individual recovers from exercise is known to be an indicator of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study we present a novel approach to assess vascular recovery rate using the photoplethysmogram (PPG) signal, which is monitored by smart wearables.

The Vortal dataset ( was used for this study, containing PPG signals measured from 39 healthy subjects before (baseline) and after intense exercise. Pulse wave analysis techniques were used to extract over 30 cardiovascular parameters from the PPG pulse wave shape. The rate at which each parameter returned back to its baseline value after exercise was quantified, and the consistency of changes between subjects was assessed statistically.

Most parameters exhibited significant changes after exercise. Parameters derived from both the amplitudes and timings of pulse wave features showed consistent changes between subjects, indicating that both the heart rate and vascular elasticity changed following exercise.

This study demonstrated the feasibility of assessing vascular recovery rate after exercise from the PPG. Candidate parameters were identified for further studies, which should investigate whether they are associated with cardiovascular fitness, and whether they provide additional information beyond that of heart rate recovery. These parameters could be measured by both clinical and consumer devices, providing an extra dimension to cardiovascular risk assessment.

Keywords: photoplethysmogram; exercise; pulse wave analysis; smart wearables
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