Feature extraction from smartphone inertial signals for human activity segmentation

This paper proposes the adaptation of well-known strategies successfully used in speech processing: Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual Linear Prediction (PLP) coefficients. Additionally characteristics like RASTA filtering or delta coefficients are also considered and evaluated for inertial signal processing. These adaptations have been incorporated into a Human Activity Recognition and Segmentation (HARS) system based on Hidden Markov Models (HMMs) for recognizing and segmenting six different physical activities: walking, walking–upstairs, walking-downstairs, sitting, standing and lying.