Self-optimizing framework for natural sensory feedback through transcutaneous electrical nerve stimulation

Self-optimizing framework for natural sensory feedback through transcutaneous electrical nerve stimulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Self-optimizing framework for natural sensory feedback through transcutaneous electrical nerve stimulation Solaiman Shokur, Franklin Leong, Silvestro Micera This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6745560/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The absence of sensory input following limb amputation significantly impairs prosthesis embodiment and natural interaction with the environment. Bidirectional prostheses integrating sensory feedback with electrical stimulation have shown promise in enhancing user control, task precision, and overall satisfaction. However, optimizing stimulation parameters to elicit naturalistic sensations remains a major challenge, particularly for transcutaneous electrical nerve stimulation (TENS), which often produces tingling rather than pressure-like sensations. In this study, we used the SELFOPT framework, a subject-centric approach that empowers users to iteratively refine stimulation parameters in real time, bypassing experimenter intervention. We compare SELFOPT to a traditional ramp-up approach (RAMP) in a study involving 25 healthy participants, optimizing TENS parameters for soft and heavy tap sensations. Our results demonstrate that SELFOPT-derived parameters elicit significantly more natural and pressure-like sensations compared to RAMP. Analysis of parameter selection revealed a complex relationship between pulse amplitude, pulse width, and pulse frequency for eliciting a natural sensation. Furthermore, SELFOPT provides retrospective insights into optimal parameter regions, contributing to a deeper understanding of sensory encoding. Beyond enhancing naturalness, SELFOPT presents a versatile framework for individualized parameter tuning in neuroprosthetic applications. Its interactive nature eliminates experimenter-subject bottlenecks, supports multi-parameter optimization, and enables real-time adaptation. These findings pave the way for more intuitive and embodied prosthetic technologies, with broader implications for neurostimulation-based sensory feedback systems. Biological sciences/Neuroscience/Somatosensory system/Touch receptors Biological sciences/Neuroscience/Peripheral nervous system/Somatic system Physical sciences/Engineering/Biomedical engineering Full Text Additional Declarations Yes there is potential Competing Interest. S.M. holds shares in SensArs, which aims to develop bionic limbs for amputees. S.M. and S.S. are the inventors of a thermal sensing device, sensory feedback system, and method using said thermal sensing device (application number EP22207038.5). S.M. and S.S. are cofounders of action potential neurotechnology, a startup working in nerve stimulation for motor restoration. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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