Articles tagged with Electromyography
A new study has found that combining signals from electromyography and force myography can improve the accuracy of prosthetic control. The researchers used a combination of these two techniques to classify hand gestures with high accuracy, outperforming both methods alone.
A team of researchers from the University of Buenos Aires has developed a method to translate the vocal muscle activity of birds during sleep into synthetic songs. This breakthrough uses electromyography data and dynamical systems models to recreate the sounds of dreaming birds, providing a new window into the subconscious mind of avians.
A KAIST research team has developed a stretchable and adhesive microneedle sensor that can detect physiological signals without being affected by sweat and dead skin. The sensor allows for long-term stable control of wearable robots, enabling precise movement recognition for rehabilitation treatments.
A new study from Chiba University introduces a novel system for evaluating muscle coordination and fatigue using bull's-eye electrodes. The system provides images of muscle activity at nine different points, enabling the assessment of muscle disorder due to fatigue.
A deep learning-based framework called EMGSense enables accurate wearable EMG device usage through AI self-training techniques. It achieves average accuracy of 91.9% in gesture recognition and 81.2% in activity recognition, outperforming state-of-the-art approaches.
Limbitless Solutions has been selected as one of eight winners for the 2023 Unity for Humanity Grant. The organization will use $85,000 to develop its video game Quantum's Pursuit, which aims to empower children and adults with limb differences to learn how to use their bionic arms in a fun and engaging way.
Researchers at the University of Missouri have designed a soft and breathable material that can be worn on the skin without causing discomfort. The material, made from liquid-metal elastomer composite, has integrated antibacterial and antiviral properties to prevent the formation of harmful pathogens.
Researchers developed an iEMG classifier framework for detecting myopathy and neuropathy, achieving high accuracy in three muscle types and low computational time. The study showed promise for real-time implementation, aiding clinicians in making quick and accurate diagnoses.
Researchers found that calf muscle stiffness contributes to passive ankle joint stiffness, especially when dorsiflexed. The study suggests that exercises to relieve triceps surae stiffness can improve joint flexibility and reduce injury risk.
A study by Dr. Ryoichi Ema found that eccentric cycling has higher neuromuscular activation of the rectus femoris muscle and can be performed with less effort. This suggests that eccentric cycling exercise is beneficial for improving athletic performance and preventing muscle damage.
Researchers developed a hybrid machine-learning approach combining CNN and LSTM to recognize complex hand gestures in prosthetic hands. The technique achieved far superior performance than traditional machine learning efforts, with an accuracy of over 80%, but struggled with certain pinching gestures.
Researchers developed a technology that accurately detects lies by analyzing facial muscle contractions, achieving a success rate of 73%. The study identified two distinct groups of 'liars' based on cheek muscle and eyebrow activation, with potential implications for real-life deception detection.
Researchers developed a wearable, biocompatible, and low-cost biosensor that measures electromyography signals in muscles. The sensor uses a gold-silver nanocomposite printed onto fabric, providing a comfortable and effective way to track muscle activity.
Researchers developed an elastic kirigami patch to capture electromyographic signals from palm muscles of baseball players, revealing differences between curveballs and fastballs. This innovation enables better understanding of muscular activity in various sports and could aid medical research for motor disorders.
A researcher at the University of Pittsburgh is using ultrasound imaging to develop a more precise interface between exoskeletons and individual muscles in people with incomplete spinal cord injuries. This technology aims to provide more efficient rehabilitation tools, reducing the risk of falls during robot-assisted walking.
A study of 78 Spanish footballers found variations in muscle contraction and recovery times depending on their field position. The rectus femoris muscle, which extends the knee, shows different characteristics for central defenders, midfielders, and goalkeepers.
Researchers found that people can learn to recognize early symptoms of injury and take steps to maintain their health. Training in muscle relaxation and breathing techniques significantly decreased symptoms and improved well-being among volunteers.