Trends in human activity recognition with focus on machine learning and power requirements

Abstract

The advancement and availability of technology can be employed to improve our daily lives. One example is Human Activity Recognition (HAR). HAR research has been mainly explored using imagery but is currently evolving to the use of sensors and has the ability to have a positive impact, including individual health monitoring and removing the barrier of healthcare. To reach a marketable HAR device, state-of-the-art classifications and power consumption methods such as convolutional neural network (CNN), data compression and other emerging techniques are reviewed here. The review of the current literature creates a foundation in HAR and addresses the lack of available HAR datasets, recommendation of classification and power reduction techniques, current drawbacks and their respective solutions, as well as future trends in HAR. The lack of publicly available datasets makes it difficult for new users to explore the field of HAR. This paper dedicates a section to publicly available datasets for users to access. Finally, a framework is suggested for HAR applications, which envelopes the current literature and emerging trends in HAR.

Keywords

• HAR
• Machine learning
• Power consumption
• Wearable devices
• Telemedicine