Learning Human-Environment Interactions via Wearable AI Interfaces

Wearable artificial intelligence (AI) interfaces are reshaping the boundaries between humans and the environment. While prior works often focus on narrow human-machine interactions, this review proposes an intact interaction information flow. It introduces a comprehensive interaction blueprint spanning local interaction and global interaction to the interaction entity, showing how humans interact with the environment. This review first examines advances in wearable form factors, sensing performance improvement strategies, and data analysis. Special emphasis is onspot on how AI interprets heterogeneous data from tactile signatures for local interaction, wearable vision for global interaction with human motion, and electrophysiological signals for the interaction entity. We then discuss the essential applications of this interaction framework, such as human-machine interaction and smart healthcare. By discussing potential barriers in device reliability, algorithm generalization, and scalable applications of wearable AI interfaces, this review provides an outlook on data-driven inverse sensor design, general intelligence strategies, and building a standard ecosystem for scalable applications. The wearable AI interfaces are toward on-body intelligence, actively perceiving, understanding, and assisting in the complex dynamic human-environment interactions.