Camera-Based Method for Respiratory Rhythm Extraction From a Lateral Perspective

Abstract

This work proposes a new method based on computer vision algorithms to measure the respiratory rhythm of a subject from a lateral perspective. The proposed algorithm consists on tracking the motion of the intercostal and abdominal muscles by the means of dense optical flow, being the novelty of the proposed method the extraction of the respiratory signal from the phase of the optical flow, while extracting at the same time a quality index from the modulus. 15 healthy subjects were measured while seating, and 4 tests were performed for each subject involving different scenarios. The algorithm has been validated using a commercial wearable thorax inductive plethysmograph system. The instantaneous frequency for the constant frequency respiratory tests, and the breath to breath analysis and instantaneous frequency of the free breathing test have been computed to assess the performance and error of the proposed method for the respiratory acquisition. Finally, a statistical analysis has been performed to assess the accuracy and performance of the quality index. The results of the study show a high agreement between methods in the 0.1 Hz and 0.3 Hz test. For the Free breathing test, both the cycle by cycle and Instantaneous frequency results show a low error between methods with high sensitivity in the cycle detection. The hypothesis that the modulus of the optical flow could be used as a quality index has been corroborated, with very good statistical results. Moreover, due to the simplicity of the proposed algorithm, the proposed method can perform in real-time while measuring respiratory rhythm and assessing the quality of the acquired signal. Further studies taking into account external vibrations have to be performed, to assert that the proposed method can be used in demanding conditions.

Non-Contact Respiratory Monitoring Using an RGB Camera for Real-World Applications

“…Bland–Altman analysis evidenced lower bias when FO method is used in all the cases. FO method allow monitoring with bias of −0.02 ± 1.92 bpm and −0.03 ± 1.38 bpm in the seated postures which is comparable to those obtained in similar conditions with wearable devices [ 38 ] (MOD ± LOAs = 0.01 ± 2.39 bpm) and better than those obtained with other contactless techniques based on video [ 22 ] (MOD ± LOAs = 0.08 ± 1.48 bpm), radar [ 39 ] (MOD ± LOAs = −1.21 ± 6.99 bpm), and thermal cameras [ 40 ] (MOD ± LOAs = −0.30 ± 2.69 bpm). Errors found with the user in the supine position (bias up to −0.01 ± 3.88 bpm) are comparable to those of [ 41 ] (MOD ± LOAs = 0.36 ± 2.50 bpm) in which a combination of near-infrared and thermal imaging techniques are used.…”

“…In addition, ref. [ 20 ] tracks the deviation of selected feature points near the upper torso and head over the time and [ 21 , 22 ] use the optical flow method (hereinafter, FO). All the above-mentioned methods give good results in acquiring the respiratory pattern, but they have some limitations.…”

“…In the method proposed in [ 20 ] if there is no compensation by the tracking algorithm of small head motions, the estimated respiration will be inaccurate. Moreover, although [ 21 , 22 ] use FO methods, as in our work, there are some differences. In fact, ref.…”

“…Ref. [ 22 ] uses the phase of the calculated FO on healthy subjects while seating from a lateral perspective. However, this method necessarily requires the subject to be placed in a lateral position.…”

Study of Blood-Pressure Measurement Using Noninvasive Methods

“…Lastly, for participant S2 who was performing physical exercise for 2 min 30 s, blood pressure obtained prior and after working out was compared. PTT decreased when the subject underwent physical activity, which agreed with some published studies [8]. Although obtained results were conclusive with the consulted bibliography, they were not statistically significant since the tests were performed only on five people.…”