Data Science Approaches to Human Activity Recognition
TL;DR
The main issues that have been identified by the study include sensor noise, user variability and computational constraints and future prospects of the study is given on context-aware systems and edge intelligence.
Abstract
Human Activity Recognition (HAR) has become one of the essential research areas due to the blistering development of wearable sensory devices, smartphones or the Internet of Things (IoT). HAR aims at recognizing human physical actions like walking, sitting, standing, running and lying down automatically based on the data acquired by motion sensors and physiological sensors. The ability to scale and flexibility have gradually seen the replacement of traditional rule-based systems by data-driven systems. Machine learning models and deep learning provide data science approaches that can robustly extract features, classify, and infer in real time using complicated sensor signal data streams. In the current paper, the use of data science methods in HAR is evaluated and summed up in detail. It examines data collection techniques, preprocessing techniques, feature engineering techniques and classification models. Moreover, it reviews benchmark datasets and assessment measures that are prevalent in HAR studies. The HAR methodology based on data science pipelines is offered and tested on the example of standard datasets. Findings have shown that novel machine learning and deep learning neural networks are much more effective in recognition accuracy than the classical methods of statistics. The main issues that have been identified by the study include sensor noise, user variability and computational constraints and future prospects of the study is given on context-aware systems and edge intelligence. The results lead to the realization of efficient HAR to track healthcare, intelligent environments, and human-computer interface.