Search

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s). © 2001-2011 IEEE.

The paper deals with fusion of physiological and GPS data acquired during cycling and their analysis using general methods of multichannel signal processing. Experimental data were acquired during 17 identical cycling routes each about 12 km long including more then 1100 segments of the length 60 s recorded with the varying sampling period. The proposed algorithm includes their initial analysis, de-nosing using selected digital filters, interpolation and resampling in the first stage followed by evaluation of the cross-correlation between the heart rate and the altitude gradient of positioning data recorded by the GPS system. Results obtained present (i) relation between the heart rate and the slope with the positive regression coefficient 6.04 and (ii) the heart rate and speed with the negative regression coefficient-1.67 over all segments analyzed. © 2015 IEEE.