Results 151 to 160 of about 123,306 (259)

Muscle Control of an Extra Robotic Digit

Advanced Robotics Research, EarlyView.
This study compares muscle‐ and movement‐based control for operating a supernumerary robotic thumb. While movement control performs better in the proposed tasks, muscle‐based (EMG) control promotes broader motor learning. The results highlight the promise and challenges of using biosignals for human augmentation, offering new insights into intuitive ...
Julien Russ, Lucy Dowdall, Francesco Cenciarelli, Kitty Goodridge, Mabel Ziman, Hristo Dimitrov, Danielle Clode, Tamar R. Makin   +7 more
wiley   +1 more source

The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review. [PDF]

Cogn Neurodyn
Miltiadous A, Ntetska A, Aspiotis V, Moustakli E, Tsipouras MG, Tzallas AT, Giannakeas N, Glavas E, Angelidis P, Tzimourta KD.   +9 more
europepmc   +1 more source

A Multidirectional Textile Interface for Remote Control Using Dynamic Area‐Based Capacitance Modulation

Advanced Robotics Research, EarlyView.
Here, we present a textile, wearable capacitive interface enabling multidirectional remote control by dynamically modulating electrode overlap and spacing via a freely gliding upper electrode. A forearm‐mounted prototype drives robotic and media tasks with 12–15 ms latency, maintains < 0.8% drift after 500 cycles, and remains stably functional at 90 ...
Cagatay Gumus, Fulya Yenilmez, Kadir Ozlem, Hakan Kestir, Eren Tasdemir, Berkant Bakisli, Gökhan Ince, Ozgur Atalay, Asli Tuncay Atalay   +8 more
wiley   +1 more source

Estimation of positron emission tomography amyloid load and related biomarkers in Alzheimer's disease using evoked potential tomography EEG: development and internal validation in a cross-sectional cohort. [PDF]

Alzheimers Res Ther
Rauchmann BS, Hamet J, Lai J, Kafi H, Rexwinkle J, Brendel M, Franzmeier N, Kurz C, Pogarell O, Levin J, Höglinger G, Perneczky R.   +11 more
europepmc   +1 more source

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

Advanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao, Hongzhou Ai, Wenjun Xu, Zude Zhou, Lihui Wang   +4 more
wiley   +1 more source

Preparing Emergency Medicine Residents to Lead Level 1 Multidisciplinary Trauma Teams: A Scoping Review. [PDF]

AEM Educ Train
Khan AS, Cushman CJ, Mecham C, Stroever S, Morris T.   +4 more
europepmc   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

Differences in functional block during extrastimulation from the right atrial appendage and Bachmann bundle region; a potential mechanism of observed anti-fibrillatory effects of Bachmann bundle pacing. [PDF]

Europace
Atwater BD, Alkalbani M, Hollis Z.
europepmc   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Expert experience-guided virtual datasets for adaptive automatic driving in metro trains. [PDF]

Sci Rep
Huang Y, Zhao W, Chen D, Lin G.
europepmc   +1 more source