Cognitive computing in human activity recognition with a focus on healthcare

Human activity recognition (HAR) is a quintessence to empower a robot to distinguish the conduct of a personal care-receiver. As opposed to outward appearances, an activity recognition can see practices of a consideration beneficiary, who might be a senior adult, a youngster, or a chronic patient. Through HAR, a robot tracks the care patient activity and perceive human practices like unhealthy habits and anomalous activities. However, patient activity recognition through simple images is a highly challenging task. Several challenges such as the likeness of unmistakable human behaviors, disorder background, similarities in different human activities may significantly reduce the classification performance. Because of rapid developments in cutting-edge machine learning models, substantial solutions can arise from distinct deep learning algorithms, including convolutional neural network, generative adversarial network. In this chapter, the authors review several cognitive computing approaches in the advancement of human-robot interaction, especially in healthcare industries.