Noncontact Vital Sign Detection based on Stepwise Atomic Norm Minimization

Li Sun, Hong Hong, Yusheng Li, Chen Gu, Feng Xi, Changzhi Li, Xiaohua Zhu

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Noncontact techniques for detecting vital signs have attracted great interest due to the benefits shown in medical monitoring and military applications. A rapid remote evaluation on physiological signal frequencies is needed in search and rescue operations as well as intensive care. However, the presence of respiration harmonics causes aliasing problems to heart-rate estimation, especially when the data volume is limited. By taking advantage of the simple pattern of physiological signals, we propose a stepwise atomic norm minimization method (StANM) to accurately assess the respiration and heartbeat frequencies with a limited data volume. First, the respiration frequency is estimated by the conventional atomic norm minimization. Then the frequencies of respiration harmonics are generated based on the inherent relationship between the fundamental tone and the harmonics. Finally, with the pre-estimated frequencies, we locate the heartbeat frequency by solving a modified atomic norm minimization problem. Simulations and experiments show that the proposed method can accurately estimate physiological frequencies from 6.5-second-long raw data with a 4-Hz sampling rate.

Original languageEnglish
Article number7307118
Pages (from-to)2479-2483
Number of pages5
JournalIEEE Signal Processing Letters
Issue number12
StatePublished - Dec 2015


  • Atomic norm minimization
  • Vital sign detection
  • line spectral estimation
  • noncontact
  • super-resolution


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