Tifex-Py: Time-Series Feature Extraction for Python in a Human Activity Recognition Scenario.

Abstract

Human Activity Recognition (HAR) is a valuable tool for healthcare and rehabilitation, enabling applications like remote patient monitoring and rehabilitation progress assessment. This paper introduces TIFEX-Py, a comprehensive Python toolbox designed for time series feature extraction in HAR. TIFEX-Py offers a rich set of 195 feature extraction methods across statistical, amplitude, spectral, and time-frequency domains. To evaluate its effectiveness, TIFEX-Py was applied to 11 publicly available HAR datasets: DSADS, HHAR, MHEALTH, MotionSense, PAMAP2, REALDISP, RealWorld, UniMiBSHAR, USC-HAD, WARD, and WISDM. Machine learning pipelines utilizing TIFEX-Py features, evaluated under both random and subject-stratified cross-validation settings, consistently achieved performance that is competitive with or superior to state-of-theart (SOTA) benchmark performances available for the datasets. In 11 out of 11 random split cross-validation scenarios, our pipeline surpassed or matched SOTA performance. For stratified by subject cross-validation, this was the case for more than half of the datasets. These results highlight the power of TIFEX-Py's feature space in representing time series data. TIFEX-Py is opensource and publicly available for researchers in rehabilitation and movement analysis fields.