Feature extraction from smartphone inertial signals for human activity segmentation

The results presented in this paper overcome significantly baseline error rates, constituting a relevant contribution in the field. Adapted MFCC and PLP coefficients improve human activity recognition and segmentation accuracies while reducing feature vector size considerably. RASTA-filtering and delta coefficients contribute significantly to reduce the segmentation error rate obtaining the best results: an Activity Segmentation Error Rate lower than 0.5%.