journal article·2023-11

Injection‐on‐Skin Granular Adhesive for Interactive Human–Machine Interface

Sumin Kim, Jaepyo Jang, Kyumin Kang, Subin Jin, Heewon Choi, Donghee Son, Mikyung Shin

Advanced Materials· Volume 35· Issue 48
Publisher page

Abstract

Abstract Realization of interactive human–machine interfaces (iHMI) is improved with development of soft tissue‐like strain sensors beyond hard robotic exosuits, potentially allowing cognitive behavior therapy and physical rehabilitation for patients with brain disorders. Here, this study reports on a strain‐sensitive granular adhesive inspired by the core–shell architectures of natural basil seeds for iHMI as well as human–metaverse interfacing. The granular adhesive sensor consists of easily fragmented hydropellets as a core and tissue‐adhesive catecholamine layers as a shell, satisfying great on‐skin injectability, ionic‐electrical conductivity, and sensitive resistance changes through reversible yet robust cohesion among the hydropellets. Particularly, it is found that the ionic‐electrical self‐doping of the catecholamine shell on hydrosurfaces leads to a compact ion density of the materials. Based on these physical and electrical properties of the sensor, it is demonstrated that successful iHMI integration with a robot arm in both real and virtual environments enables robotic control by finger gesture and haptic feedback. This study expresses benefits of using granular hydrogel‐based strain sensors for implementing on‐skin writable bioelectronics and their bridging into the metaverse world.
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Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Muscle activation and electromyography studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University Suwon 16419 Republic of Korea

Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon 16419 Republic of Korea

Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

Department of Artificial Intelligence System Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

Department of Biomedical Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

Funding

National Research Foundation of Korea

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