Results 281 to 290 of about 5,245,598 (349)
Muscle Control of an Extra Robotic Digit
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 +7 more
wiley +1 more source
Gap junction innexin asymmetry in C. elegans suggests a diode blocking mechanism to prevent antidromic backpropagation from motor neurons to command interneurons. [PDF]
White J.
europepmc +1 more source
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 +8 more
wiley +1 more source
SST interneuron maturation extends beyond the second postnatal week and is driven by coordinated transcriptional and functional remodeling. [PDF]
Christodoulou O +5 more
europepmc +1 more source
Origami‐Inspired Structural Design for Aquatic‐Terrestrial Amphibious Robots
This work presents a lightweight amphibious origami robot actuated by a single shape memory alloy wire. A rigid foldable origami structure with displacement amplification enables efficient terrestrial crawling and aquatic swimming. The addition of fan‐shaped units allows controllable turning in both environments.
Weiqi Liu +5 more
wiley +1 more source
Molecular and neural circuit mechanisms of parvalbumin (PV) neurons in depression: Insights and advances. [PDF]
Li ZX +9 more
europepmc +1 more source
Catecholaminergic modulation and transmission in sympathethetic preganglionic neurons.
openaire +3 more sources
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 +4 more
wiley +1 more source

