Heart rate variability(HRV),as a key indicator for evaluating autonomic nervous system function,has significant value in areas such as cardiovascular disease screening and emotion monitoring.Although traditional conta...Heart rate variability(HRV),as a key indicator for evaluating autonomic nervous system function,has significant value in areas such as cardiovascular disease screening and emotion monitoring.Although traditional contact-based measurement methods offer high precision,they suffer from issues such as poor comfort and low user compliance.This paper proposes a non-contact HRV monitoring method using frequency modulated continuous wave(FMCW)radar,highlighting adaptive cycle segmentation and peak extraction as core innovations.Key advantages of this method include:1)effective suppression of motion artifacts and respiratory harmonics by leveraging cardiac energy concentration;2)precise heartbeat cycle identification across physiological states via adaptive segmentation,addressing time-varying differences;3)adaptive threshold adjustment using discrete energy signals and a support vector machine(SVM)model based on morphological-temporal-spectral characteristics,reducing complexity while maintaining precision.Previous approaches predominantly process radar signals holistically through algorithms to uniformly extract inter-beat intervals(IBIs),which may result in high computational complexity and inadequate dynamic adaptability.In contrast,our method achieved higher precision than conventional holistic processing approaches,while maintaining comparable precision with lower computational complexity than previous optimization algorithms.Experimental results demonstrate that the system achieves an average IBI error of 8.28 ms(RMSE of 15.3 ms),which is reduced by about 66%compared with the traditional holistically peak seeking method.The average errors of SDNN and RMSSD are 2.65 ms and 4.33 ms,respectively.More than 92%of the IBI errors are controlled within 20 ms.The distance adaptability test showed that although the accuracy of long-distance measurement decreased slightly(<6 ms),the overall detection performance remained robust at different distances.This study provided a novel estimation algorithm for non-contact HRV detection,offering new perspectives for future health monitoring.展开更多
基金supported by National Natural Science Foundation of China(Nos.62320106002,U22A2014)National Key Research and Development Program of China(No.2021YFA1401103)+2 种基金2022 Wuxi Taihu Talent Program:Innovative Leading Talent Team(No.1096010241230120)Fundamental Research Funds for Central Universities(No.1322050205250910)Wuxi Municipal Basic Research Project(No.K20241026).
文摘Heart rate variability(HRV),as a key indicator for evaluating autonomic nervous system function,has significant value in areas such as cardiovascular disease screening and emotion monitoring.Although traditional contact-based measurement methods offer high precision,they suffer from issues such as poor comfort and low user compliance.This paper proposes a non-contact HRV monitoring method using frequency modulated continuous wave(FMCW)radar,highlighting adaptive cycle segmentation and peak extraction as core innovations.Key advantages of this method include:1)effective suppression of motion artifacts and respiratory harmonics by leveraging cardiac energy concentration;2)precise heartbeat cycle identification across physiological states via adaptive segmentation,addressing time-varying differences;3)adaptive threshold adjustment using discrete energy signals and a support vector machine(SVM)model based on morphological-temporal-spectral characteristics,reducing complexity while maintaining precision.Previous approaches predominantly process radar signals holistically through algorithms to uniformly extract inter-beat intervals(IBIs),which may result in high computational complexity and inadequate dynamic adaptability.In contrast,our method achieved higher precision than conventional holistic processing approaches,while maintaining comparable precision with lower computational complexity than previous optimization algorithms.Experimental results demonstrate that the system achieves an average IBI error of 8.28 ms(RMSE of 15.3 ms),which is reduced by about 66%compared with the traditional holistically peak seeking method.The average errors of SDNN and RMSSD are 2.65 ms and 4.33 ms,respectively.More than 92%of the IBI errors are controlled within 20 ms.The distance adaptability test showed that although the accuracy of long-distance measurement decreased slightly(<6 ms),the overall detection performance remained robust at different distances.This study provided a novel estimation algorithm for non-contact HRV detection,offering new perspectives for future health monitoring.
