AIMC Topic: Electromyography

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Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.

Medical & biological engineering & computing
Jump locomotion is the basic movement of human. However, no thorough research on the recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor information fusion and machine learning to recognize the human jump phase...

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges.

Sensors (Basel, Switzerland)
Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of e...

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison.

Journal of neuroengineering and rehabilitation
BACKGROUND: Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculatur...

Exploring the Relationship Between EMG Feature Space Characteristics and Control Performance in Machine Learning Myoelectric Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In myoelectric machine learning (ML) based control, it has been demonstrated that control performance usually increases with training, but it remains largely unknown which underlying factors govern these improvements. It has been suggested that the i...

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance.

Sensors (Basel, Switzerland)
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand g...

Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
BACKGROUND: Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing.

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot.

Sensors (Basel, Switzerland)
(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human-robot collaboration applications. Al...

User training for machine learning controlled upper limb prostheses: a serious game approach.

Journal of neuroengineering and rehabilitation
BACKGROUND: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requir...

A Novel Point-in-Polygon-Based sEMG Classifier for Hand Exoskeleton Systems.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In the early 2000s, data from the latest World Health Organization estimates paint a picture where one-seventh of the world population needs at least one assistive device. Fortunately, these years are also characterized by a marked technological driv...

Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review.

IEEE reviews in biomedical engineering
Sign language is used as a primary form of communication by many people who are Deaf, deafened, hard of hearing, and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to unde...