Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics
This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang +5 more
wiley +1 more source
Multi-Task Learning in Deep Neural Networks for Mandarin-English Code-Mixing Speech Recognition
Mengzhe Chen +3 more
openalex +2 more sources
Information Transmission Strategies for Self‐Organized Robotic Aggregation
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Acoustic simulation of cochlear implant sound to approximate the perceptual experience of electric hearing. [PDF]
Kopsch AC, Plontke SK, Rahne T.
europepmc +1 more source
Speech Recognition using Linear Predictive Coding (LPC) and Adaptive Neuro-Fuzzy (ANFIS) to Control 5 DoF Arm Robot [PDF]
W. S. Mada Sanjaya +2 more
openalex +1 more source
A multimaterial robotic gripper fabricated via multimaterial 3D printing integrates conductive thermopolymer joints with flexible elastomeric components. Joule heating enables precise joint‐level stiffness modulation, enhanced by embedded temperature sensors, passive shape restoration, and active cooling, enabling versatile manipulation capabilities ...
Daniel Jee Seng Goh +4 more
wiley +1 more source
What Does That Head Tilt Mean? Brain Lateralization and Sex Differences in the Processing of Familiar Human Speech by Domestic Dogs. [PDF]
Buckley C +6 more
europepmc +1 more source
THE PREPROCESSING PROCEDURE OF DIGITAL STREAMS OF CODED SPEECH MESSAGES
A.N. Gomonov, D.V. Gerasin
openalex +1 more source
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source

