Infrared thermography has a wide range of applications both in engineering and biomedicine. Resulting video-images provide immediate information about thermal conditions on the surface of the observed object but for the more sophisticated analysis the detail evaluation of separate images is necessary. The processing of thermal images is based upon data acquisition by special non-invasive sensors, efficient communication systems, and the application of selected machine learning methods in many cases. The present paper is devoted to the recognition of thermal regions in the facial area, detection of the body temperature, and evaluation of breathing frequency and its possible disorders. Data include video-sequences acquired on the home exercise bike and recorded during different load conditions. The proposed general methodology combines the use of neural networks and machine learning methods for the detection of the changing temperature ranges of the thermal camera. Selected digital signal processing methods are then used to find the mean body temperature and breathing frequency during the specified time period. Results show the temperature changes and breathing frequency between 0.48 and 0.56 Hz for selected experiments and different body loads.