Invited: Human-Inspired Distributed Wearable AI
Shreyas Sen, Arunashish Datta
TL;DR
The paper tackles the energy bottleneck hindering scalable Wearable AI by proposing a Human-Inspired Distributed Network that connects leaf IoB nodes through Wi-R to an on-body hub, effectively creating an artificial nervous system. It argues that traditional RF communication is energy-inefficient for body-area networks and promotes Electro-Quasistatic Human Body Communication (EQS-HBC) as a more efficient, secure alternative for intra-body data transfer. Key contributions include the architectural vision of leaf nodes feeding an on-body hub, the integration of Wi-R as a high-speed, ultra-low-power link, and evidence that such a setup enables perpetually operable wearables through energy harvesting and edge computing. The work outlines practical implications for scalable, always-on wearable AI with future directions in miniaturized EQS hardware, security, and distributed edge–fog–cloud ecosystems.
Abstract
The explosive surge in Human-AI interactions, fused with a soaring fascination in wearable technology, has ignited a frenzy of innovation and the emergence of a myriad of Wearable AI devices, each wielding diverse form factors, tackling tasks from health surveillance to turbocharging productivity. This paper delves into the vision for wearable AI technology, addressing the technical bottlenecks that stand in the way of its promised advancements. Embracing a paradigm shift, we introduce a Human-Inspired Distributed Network for Wearable AI, enabled by high-speed ultra-low-power secure connectivity via the emerging 'Body as a Wire' (Wi-R) technology. This breakthrough acts as the missing link: the artificial nervous system, seamlessly interconnecting all wearables and implantables, ushering in a new era of interconnected intelligence, where featherweight, perpetually operating wearable AI nodes redefine the boundaries of possibility.