Abstract
Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research.
Highlights
Understanding the process of learning in humans has been an area of interest for decades
One key aspect of the learning process that has been challenging to researchers in the artificial intelligence (AI) community is designing systems which leverage knowledge gained from solving a task into improved performance when solving similar or dissimilar problems
Statistical and classical machine learning (ML) techniques such as support vector machines (SVM), knearest neighbour (KNN), naive Bayesian and latent Dirichlet allocation (LDA) are some of the commonest methods applied in Human activity recognition (HAR) using 3D human skeleton data [71]
Summary
Understanding the process of learning in humans has been an area of interest for decades. One key aspect of the learning process that has been challenging to researchers in the artificial intelligence (AI) community is designing systems which leverage knowledge gained from solving a task into improved performance when solving similar or dissimilar problems. This is where the concept of transfer learning (TL) comes in. Vision-based HAR methods, transfer learning of human activities and the challenges in these areas are discussed.
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