AIMC Topic: Electromyography

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High-level locomotion intent estimation from electromyography and body posture.

Journal of neural engineering
Once we learn a reliable gait, we no longer have to consciously contract individual muscles to walk, or think about the fine-grained low-level control of our joints. Instead, we mainly make decisions on where we want to end up, at what pace and throu...

Muscle synergy-driven ensemble learning framework for individualized stroke gait rehabilitation.

Scientific reports
This study proposes a novel ensemble machine learning (ML) framework integrating neurophysiological principles from muscle synergy analysis to support clinical decisions in stroke gait rehabilitation. The framework leverages spatial and temporal feat...

Localized Muscular Fatigue in Robotic-Assisted Laparoscopic Surgery: Predictive Modeling Study.

JMIR formative research
BACKGROUND: Robotic-assisted surgery (RAS) has grown rapidly in recent decades, and several RAS procedures have become the standard. However, the physical and mental demands of minimally invasive surgery (MIS) techniques can lead to ergonomic shortco...

Shared human-machine control of an intelligent bionic hand improves grasping and decreases cognitive burden for transradial amputees.

Nature communications
Bionic hands can replicate many movements of the human hand, but our ability to intuitively control these bionic hands is limited. Humans' manual dexterity is partly due to control loops driven by sensory feedback. Here, we describe the integration o...

A hybrid EMG-EEG interface for robust intention detection and fatigue-adaptive control of an elbow rehabilitation robot.

Scientific reports
Accurate detection of user intention is a critical requirement for intelligent control systems in upper-limb rehabilitation robots. However, electromyography (EMG)-based recognition can degrade significantly under muscle fatigue. To address this limi...

Perceived fatigue progression tracking during manual handling tasks using sEMG recordings.

Journal of neuroengineering and rehabilitation
Physical fatigue significantly contributes to work-related musculoskeletal disorders, highlighting the need to understand its effects during manual handling tasks for effective prevention strategies. This study examines the correlation between change...

Deep learning for motion classification in ankle exoskeletons using surface EMG and IMU signals.

Scientific reports
Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility, support rehabilitation, and reduce fall risks, particularly among the aging population. Their effectiveness depends on accurate, real-time prediction of u...

Spinal interneuron population dynamics underlying flexible pattern generation.

Nature communications
The mammalian spinal locomotor network is composed of diverse populations of interneurons that collectively orchestrate and execute a range of locomotor behaviors. As the number of identified classes of spinal interneurons constituting the locomotor ...

CS-Net: convolutional spider neural network for surface-EMG-based hybrid gesture recognition.

Journal of neural engineering
In this paper, we propose a novel neural network architecture, the convolutional spider neural network (CS-Net), combined with a transfer learning (TL) strategy, to classify hybrid gestures that integrate wrist postures and hand movements.The CS-Net ...

Automated Classification of Sleep-Wake States and Seizures in Mice.

eNeuro
Sleep-wake states bidirectionally interact with epilepsy and seizures, but the mechanisms are unknown. A barrier to comprehensive characterization and the study of mechanisms has been the difficulty of annotating large chronic recording datasets. To ...