Superficial temperature distribution patterns before and after physical activity in school children are indicative for personalized exercise coaching and disease prevention

Thermographic of the Microcirculation in Healthy Children Aged 3-10 Months as an Objective and Noninvasive Method of Assessment

BACKGROUND

The aim of this study was to assess if thermography as an objective and non-invasive research tool is capable of identifying the changes in the surface temperature of the body as a response to muscle stimulation in Vojta therapy. The research group consisted of children aged 3-10 months with slight abnormalities of the motor pattern, subjected to individually selected stimulation elements according to Vojta.

METHODS

The Vojta method of spontaneous motor assessment and the thermovision method of assessing the microcirculation properties of muscles were used for the evaluation.

RESULTS

In the study group, changes in the microcirculation parameters of the extensor muscles of the back occurred immediately after the therapy at the first examination.

CONCLUSIONS

The analysis featuring an objective assessment allows physiotherapists to diagnose local temperature changes based on the effect of microcirculation parameters in the musculofascial structures.

TRIAL REGISTRATION

The research was conducted as a pilot study for a scientific project approved by the Commission for Scientific Research of the University of Health and Sport Sciences in Wroclaw No 24/2021. The study is currently in the registration process with the Australian New Zealand Clinical Trials Registry.

Estimation of Heart Rate Variability Parameters by Machine Learning Approaches Applied to Facial Infrared Thermal Imaging

Heart rate variability (HRV) is a reliable tool for the evaluation of several physiological factors modulating the heart rate (HR). Importantly, variations of HRV parameters may be indicative of cardiac diseases and altered psychophysiological conditions. Recently, several studies focused on procedures for contactless HR measurements from facial videos. However, the performances of these methods decrease when illumination is poor. Infrared thermography (IRT) could be useful to overcome this limitation. In fact, IRT can measure the infrared radiations emitted by the skin, working properly even in no visible light illumination conditions. This study investigated the capability of facial IRT to estimate HRV parameters through a face tracking algorithm and a cross-validated machine learning approach, employing photoplethysmography (PPG) as the gold standard for the HR evaluation. The results demonstrated a good capability of facial IRT in estimating HRV parameters. Particularly, strong correlations between the estimated and measured HR (r = 0.7), RR intervals (r = 0.67), TINN (r = 0.71), and pNN50 (%) (r = 0.70) were found, whereas moderate correlations for RMSSD (r = 0.58), SDNN (r = 0.44), and LF/HF (r = 0.48) were discovered. The proposed procedure allows for a contactless estimation of the HRV that could be beneficial for evaluating both cardiac and general health status in subjects or conditions where contact probe sensors cannot be used.