ArticleScience advances2025
Printed sensing human-machine interface with individualized adaptive machine learning.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Inverse Design of Pre-Strain via a Predictive Electromechanical Model for Enhanced Strain Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Materials-Structure-Hardware-Algorithm Co-Driven Hierarchical Optimization for Flexible Sensors.Nano-micro letters · 2026Review
- Dynamic Manipulation Skill Learning for Tactile Myoelectric Prosthetic Hands in Tool Handling.Cyborg and bionic systems (Washington, D.C.) · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing intelligent robots with integrated sensing capabilities is critical for advanced manufacturing, medical robots, and embodied intelligence. Existing robotic sensing technologies are limited to recording of acceleration, driving torque, pressure feedback, and so on. Expanding and integrating with the multimodal sensors to mimic and even surpass the human feeling is substantially underdeveloped. Here, we introduce a printed soft human-machine interface consisting of an e-skin-enabled gesture recognitions with feedback stimulus and a soft robot with multimodal perception of contact pressure, temperature, thermal conductivity, and electrical conductivity. The sensing e-skin with adaptive machine learning was able to decode and classify the hand gestures with re-wearable convenience and individual's differences. The soft interface provides the bidirectional communications between robotics and human bodies in the close-loop. This work could substantially extend the robotic intelligence and pave the way for more practical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.