A Special Section on Deep & Advanced Machine Learning Approaches for Human Behavior Analysis

Abstract

Increasingly, there have been attempts to utilize physiological information collected from different non-intrusive devices and sensors (e.g., electroencephalogram, electrocardiograph, electrodermal activity, and skin conductance) for different activities and studies, such as using the data to train machine-/deep-learning models in order to facilitate medical diagnosis and other decision-making. Given the constant advances in machine and deep learning methods, such as deep learning, transfer learning, reinforcement learning, and federated learning, we can also utilize such techniques in cognitive computing to facilitate human behavior analysis. For example, transfer learning uses data or knowledge acquired on solved problems to help solve unsolved but very relevant problems. Transfer learning is often used in cognitive computing to use differences between individuals or tasks to improve learning efficiency and effectiveness. Transfer learning can also be integrated with deep learning to take advantage of the progress of deep learning and transfer learning.