With the use of a wireless, wearable, passive knitted smart fabric device as a strain gauge sensor, the proposed algorithm can estimate biomedical feedback such as respiratory activity. Variations in physical properties of Radio Frequency Identification (RFID) signals can be used to wirelessly detect physiological processes and states. However, it is typical for ambient noise artifacts to appear in the RFID signal making it difficult to identify physiological processes. This paper introduces a new technique for finding these repetitive physiological signals and identifying them into two states, active and inactive, using k-means clustering. The algorithm detects these biomedical events without the need to completely remove the noise components using a semi-unsupervised approach, and with these results, predict the next biomedical event using these classification results. This approach enables real-time noninvasive monitoring for use with actuating medical devices for therapy. Using this approach, the algorithm predicts the onset of respiratory activity in a simulated environment within approximately one second.
Keywords: Adaptive Signal Processing; Biomedical Signal Processing; Prediction Methods.