Fusion of AI and cloud in human activity recognition: potential and future directions

Human activity recognition (HAR) is vital to support day-to-day human activities and improve the interactions among users and external objects. HAR provides vital information about user personality and physiological traits, which can be analyzed through effective Artificial Intelligence (AI) based models. Different applications in computer vision and Machine Learning (ML), like video surveillance, healthcare, gesture recognition, person identification, and human-computer interaction, utilize the key principles of HAR. Recent surveys have centrally focused on the role of AI in HAR, where the computational requirements (both centralized and decentralized) are not discussed in detail. Owing to the gap, the article presents a detailed review of the fusion of AI and the cloud to address the dual objectives of model learning, resource requirements, and optimization. Based on research questions, we present a reference architecture that integrates AI and cloud for HAR in surveillance systems. A solution taxonomy is presented for diverse industrial verticals, and key challenges and future opportunities of HAR are discussed. A case study of Deep Learning (DL) based HAR is proposed for smart healthcare, where networking and security parameters are considered. The survey intends to assist industry and academia in the design of novel cloud and AI-based HAR systems with optimal control and resource management in various applicative verticals.