Exploration of Sensor-Based Activity Recognition Based on Time Series Feature Extraction

Abstract

Sensor-based human activity recognition (HAR) has gained its momentum and become an active research topic due to the advance of machine learning (ML) algorithms and ubiquitous sensing devices in our daily life. Recent research trend in ML algorithms for HAR is deep learning-based approaches that have already developed state-of-the-art learning models in various tasks. However, complex deep learning models may not be the best choice when it comes to data sufficiency problems and model transparency. Exploratory data analysis (EDA) can benefit feature extraction, which is an important step in a machine learning pipeline. In this study, to explore sensor-based HAR, a widely used HAR dataset is adopted to examine the effectiveness of time series feature extraction together with conventional machine learning models. Experimental results show that EDA can be beneficial for obtaining data insights and determining better features for HAR classification.