Digital channel-enabled distributed force decoding via small datasets for hand-centric interactions.

Tactile interfaces are essential for enhancing human-machine interactions, yet achieving large-scale, precise distributed force sensing remains challenging due to signal coupling and inefficient data processing. Inspired by the spiral structure of and the processing principles of neuronal systems, this study presents a digital channel-enabled distributed force decoding strategy, resulting in a phygital tactile sensing system named PhyTac. This innovative system effectively prevents marker overlap and accurately identifies multipoint stimuli up to 368 regions from coupled signals. By integrating physics into model training, we reduce the dataset size to just 45 kilobytes, surpassing conventional methods that typically exceed 1 gigabyte. Results demonstrate PhyTac's impressive fidelity of 97.7% across a sensing range of 0.5 to 25 newtons, enabling diverse applications in medical evaluation, sports training, virtual reality, and robotics. This research not only enhances our understanding of hand-centric actions but also highlights the convergence of physical and digital realms, paving the way for advancements in AI-based sensor technologies.