In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316806 | PMC |
| http://dx.doi.org/10.1038/s41467-024-50616-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Skin-Inspired and Self-Powered Piezoionic Sensors for Smart Wearable Applications.
Small
January 2025
College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, Jiangsu, 215123, China.
Bio-inspired by tactile function of human skin, piezoionic skin sensors recognize strain and stress through converting mechanical stimulus into electrical signals based on ion transfer. However, ion transfer inside sensors is significantly restricted by the lack of hierarchical structure of electrode materials, and then impedes practical application. Here, a durable nanocomposite electrode is developed based on carbon nanotubes and graphene, and integrated into piezoionic sensors for smart wearable applications, such as facial expression and exercise posture recognitions.
View Article and Find Full Text PDFAn organic electrochemical neuron for a neuromorphic perception system.
Proc Natl Acad Sci U S A
January 2025
Department of Chemistry and the Materials Research Center, Northwestern University, Evanston, IL 60208.
Human perception systems are highly refined, relying on an adaptive, plastic, and event-driven network of sensory neurons. Drawing inspiration from Nature, neuromorphic perception systems hold tremendous potential for efficient multisensory signal processing in the physical world; however, the development of an efficient artificial neuron with a widely calibratable spiking range and reduced footprint remains challenging. Here, we report an efficient organic electrochemical neuron (OECN) with reduced footprint (<37 mm) based on high-performance vertical OECT (vOECT) complementary circuitry enabled by an advanced n-type polymer for balanced p-/n-type vOECT performance.
View Article and Find Full Text PDFImproving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.
Sensors (Basel)
December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
View Article and Find Full Text PDFThe Interplay Between Muscular Activity and Pattern Recognition of Electro-Stimulated Haptic Cues During Normal Walking: A Pilot Study.
Bioengineering (Basel)
December 2024
School of Mechanical Engineering, Chung-Ang University, 84 Heukseok-Ro, Dongjak District, Seoul 06974, Republic of Korea.
This pilot study explored how muscle activation influences the pattern recognition of tactile cues delivered using electrical stimulation (ES) during each 10% window interval of the normal walking gait cycle (GC). Three healthy adults participated in the experiment. After identifying the appropriate threshold, ES as the haptic cue was applied to the gastrocnemius lateralis (GL) and biceps brachii (BB) of participants walking on a treadmill.
View Article and Find Full Text PDFTriboelectric tactile sensor for pressure and temperature sensing in high-temperature applications.
Nat Commun
January 2025
Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, PR China.
Skin-like sensors capable of detecting multiple stimuli simultaneously have great potential in cutting-edge human-machine interaction. However, realizing multimodal tactile recognition beyond human tactile perception still faces significant challenges. Here, an extreme environments-adaptive multimodal triboelectric sensor was developed, capable of detecting pressure/temperatures beyond the range of human perception.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!