Deep learning approaches for human activity recognition using wearable technology

Abstract

The need for long-term monitoring of individuals in their natural environment has initiated the development of a various number of wearable healthcare sensors for a wide range of applications: medical monitoring in clinical or home environments, physical activity assessment of athletes and recreators, baby monitoring in maternity hospitals and homes etc. Neural networks (NN) are data-driven type of modelling. Neural networks learn from experience, without knowledge about the model of phenomenon, but knowing the desired 'output' data for the training 'input' data. The most promising concept of machine learning that involves NN is the deep learning (DL) approach. The focus of this review is on approaches of DL for physiological activity recognition or human movement analysis purposes, using wearable technologies. This review shows that deep learning techniques are useful tools for health condition prediction or overall monitoring of data, streamed by wearable systems. Despite the considerable progress and wide field of applications, there are still some limitations and room for improvement of DL approaches for wearable healthcare systems, which may lead to more robust and reliable technology for personalized healthcare.

Keywords

• deep learning
• human activity monitoring
• human machine interface
• wearable sensors
• smart sensors
• multimodal interface