Walk Identification using a smart carpet and Mel-Frequency Cepstral Coefficient (MFCC) features

We have developed a real-time system for inhome activity monitoring which could be used to assist the independent living of elders. Our system is a context-aware, and unobtrusive floor-based sensor, which recognizes persons walking or falling, monitors their moving activities and stores the data for regular functional assessment. Here we report an in-depth analysis of the waveform generated by the sensors. We studied the analog characteristics of the signals such as power spectrum, pulse width, number of peeks, and signal shape. Then, we used the Mel-Frequency Cepstral Coefficient to extract features which later were utilized in the classification process. We have evaluated the performance of our technique using the dataset collected from 10 subjects who performed walks under different environmental conditions. We were able to use computational features of the generated waveform, by extracting the Mel Frequency Cepstral Coefficients and using computation intelligence to distinguish different people with an average accuracy of 82%.