| Summary: | The measurement of heart rate variability (HRV) is the preferred method for assessing the function of the autonomic nervous system (ANS). Traditional HRV detection requires an electrocardiogram (ECG) or photoelectric sensor. In this paper, we propose a new method for HRV measurement using a rear smartphone camera as a sensor. Video signals from the fingertips of 24 college students were acquired using the rear camera of an HTC M8d smartphone. ECG signals were simultaneously recorded as reference. The video signals were converted into single-frame image sequences over time. Each image frame was transformed into point data through superpositioning of pixel color attribute values and averaging according to space. The point data were sorted by time to obtain a photoplethysmogram (PPG). Finally, the Hilbert transform was used to extract the pulse-to-pulse interval and the R-to-R interval for the PPG and ECG, respectively. Sixteen HRV parameters (mean HR, max HR, min HR, SDNN, RMSSD, NN50, pNN50, VLF, LF, HF, TP, LFnu, HFnu, LF/HF, SD1, and SD2) were analyzed. All 16 HRV parameters were highly correlated (all rs > 0.95, ps <; 0.05), and the effect size (ES) differences were small (ES <; 0.175) for all indices except for RMSSD, HF, and SD1. Compared with the ECG method, the errors of the 13 HRV parameters measured using this method were within acceptable ranges. The results suggest that this technique can be used as a convenient method to assess and quantify ANS activity and balance.
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