Towards The Development of a Low-Latency, Biosignal-Controlled Human-Machine Interaction System

With the rise of SG, IoT, and cybernetic connectivity, the role of human intervention in the digital and physical manipulation of objects, factory automation, and manufacturing has become increasingly critical in meeting unprecedented quality standards. As a result, a reliable Human-centered system construct for enabling seamless and accurate control is urgently needed to interlink human intents with remote targets. In this paper, we report the development of a universal system pipeline, highlighting key enabling modules, and both physical and bioelectrical sensors as input modalities to demonstrate a near-natural motion synchronization between a human and a robotic arm. This effort was exemplified by the formulation of a method to reduce modular and system-level operational latency to achieve congruent human-machine interaction (HMI) through analyzing and simulating common mechanical motions. Furthermore, we explored several efficient machine learning (ML) model that reliably works with a variety of time-series-based biosignals reflective of intents, thus allowing a diversity of sensors to contribute as the system inputs. We believe our system pipeline represents a first step in unveiling otherwise hidden components within Biosignal-Controlled HMI systems and meeting the key challenges will bring us closer to the establishment of a natural, human-intent controlled, remotely operated HMI platform, with applications that extend far beyond major sectors of academia and industry.