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A 5.8-GHz ISM-Band radio-frequency sensor has been developed for non-contact measurement of respiration and heart rate from stationary and semi-stationary subjects at a distance of 0.5 to 1.5 meters. We report on the accuracy of the heart rate measurements obtained using two algorithmic approaches, as compared to a reference heart rate obtained using a pulse oximeter. Simultaneous Photoplethysmograph (PPG) and non-contact sensor recordings were recorded over fifteen minute periods for ten healthy subjects (8M/2F, ages 29.6 + or - 5.6 yrs) One algorithm is based on automated detection of individual peaks associated with each cardiac cycle; a second algorithm extracts a heart rate over a 60-second period using spectral analysis. Peaks were also extracted manually for comparison with the automated method. The peak-detection methods were less accurate than the spectral methods, but suggest the possibility of acquiring beat by beat data; the spectral algorithms measured heart rate to within + or -10% for the ten subjects chosen. Non-contact measurement of heart rate will be useful in chronic disease monitoring for conditions such as heart failure and cardiovascular disease.

We describe a contact-less method for measurement of respiration rate during sleep using a 5.8GHz radio-frequency bio-motion sensor. The sensor operates by sensing phase shifts in reflected radio waves from the torso caused by respiratory movements and other bodily movements such as twitches, positional changes etc. These non-respiratory motion artefacts can obscure reliable estimation of breathinig rates if conventional spectral analysis is used. This paper reports on the accuracy of the respiration rate estimates obtained via algorithmic approaches using Lomb-periodogram based analysis (which can deal with missing or corrupted data), as compared to conventional spectral analysis. Gold-standard respiration rates are derived by expert scoring of respiration rates measured through polysomnography (PSG) from sensors (Respiratory Inductance Plethysmography (RIP) belts) in contact with the subject in an accredited sleep laboratory. Specifically, respiration rates for 15-minute segments chosen from 10 subjects free of Sleep-Disorded Breathing (AHI〈5) were selected for analysis in this paper. Comparison to the expert annotation indicates strong agreement, with the Lomb-periodogram respiration rates with the average error between the measurements being less than 0.4 breaths/min and a standard deviation of 0.3 breaths/minute. Moreover, we showed that the proposed algorithm could track respiration rate over the complete night's recordings for those 10 subjects. We conclude that the non-contact biomotion sensor may provide a promising approach to continuous respiration rate monitoring of reasonable accuracy.

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