A5068772120- Muscle activation and electromyography studies
- EEG and Brain-Computer Interfaces
- Stroke Rehabilitation and Recovery
- Advanced Sensor and Energy Harvesting Materials
- Neuroscience and Neural Engineering
- Cerebral Palsy and Movement Disorders
- Hand Gesture Recognition Systems
- Motor Control and Adaptation
- Robot Manipulation and Learning
- Cloud Computing and Resource Management
- Spinal Cord Injury Research
- Gait Recognition and Analysis
- Medical Imaging and Analysis
- Gaze Tracking and Assistive Technology
- Context-Aware Activity Recognition Systems
- Balance, Gait, and Falls Prevention
- Neural Networks and Applications
- Neurological disorders and treatments
- Sports Performance and Training
- Software System Performance and Reliability
Friedrich-Alexander-Universität Erlangen-Nürnberg
2022-2025
Assiut University
2024
Restoring motor function in individuals with spinal cord injuries (SCIs), strokes, or amputations is a crucial challenge. Recent studies show that spared neurons can still be voluntarily controlled using surface electromyography (EMG), even without visible movement. To harness these signals, we developed wireless, high-density EMG bracelet and software framework, MyoGestic. Our system enables rapid adaptation of machine learning models to users’ needs, allowing real-time decoding dimensions....
Natural control of assistive devices requires continuous positional encoding and decoding the user's volition. Human movement is encoded by recruitment rate coding spinal motor units. Surface electromyography provides some information on neural code usually decoded into finger joint angles. However, current approaches to mapping electrical signal angles are unsatisfactory. There no methods that allow precise estimation during natural hand movements within large numbers degrees freedom hand....
Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders.However, the lack of public data and easyto-use open-source algorithms hinders comparison clinical application development.To address these challenges, this publication introduces gaitmap ecosystem, comprehensive set open source Python packages gait footworn IMUs.Methods: This initial release includes over 20 stateof-the-art algorithms, enables easy access to seven...
Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on skin. sEMG state-of-the-art method used to control active upper limb prostheses because of association between its amplitude and neural drive sent from spinal cord muscles. However, accurately estimating kinematics freely moving human hand using extrinsic remains challenge. Deep learning has been recently successfully applied this problem mapping...
Objective: Surface electromyography (sEMG) can sense the motor commands transmitted to muscles. This work presents a deep learning method that decode electrophysiological activity of forearm muscles into movements human hand. Methods: We have recorded kinematics and kinetics hand during wide range grasps individual digit cover 22 degrees freedom at slow (0.5 Hz) comfortable (1.5 movement speeds in 13 healthy participants. The input model consists 320 non-invasive EMG sensors placed on...
Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity generated by muscles using sensors placed on skin. It has been widely used in field of prosthetics and other assistive systems because physiological connection between muscle movement dynamics. However, most existing sEMG-based decoding algorithms show limited number detectable degrees freedom can be proportionally simultaneously controlled real-time, which limits use EMG wide range...
Abstract Theories about the neural control of movement are largely based on movement-sensing devices that capture dynamics predefined anatomical landmarks. However, neuromuscular interfaces such as surface electromyography (sEMG) can potentially overcome limitations these technologies by directly sensing motor commands transmitted to muscles. This allows for continuous, real-time prediction kinematics and kinetics without being limited biological physical constraints affect motion-based...
Küderle et al., (2023). tpcp: Tiny Pipelines for Complex Problems - A set of framework independent helpers algorithms development and evaluation. Journal Open Source Software, 8(82), 4953, https://doi.org/10.21105/joss.04953
<p>Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity generated by muscles using sensors placed on skin. It has been widely used in field of prosthetics and other assistive systems because physiological connection between muscle movement dynamics. However, most existing sEMG-based decoding algorithms show limited number detectable degrees freedom can be proportionally simultaneously controlled real-time, which limits use EMG wide range...
The loss of bilateral hand function is a debilitating challenge for millions individuals that suffered motor-complete spinal cord injury (SCI). We have recently demonstrated in eight tetraplegic the presence highly functional spared motor neurons extrinsic muscles are still capable generating proportional flexion and extension signals. In this work, we hypothesized an artificial intelligence (AI) system could automatically learn electromyographic (EMG) patterns encode attempted movements...
The human hand possesses a large number of degrees freedom. Hand dexterity is encoded by the discharge times spinal motor units (MUs). Most our knowledge on neural control movement based MUs during isometric contractions. Here we designed noninvasive framework to study neurons dynamic movements with aim understand sinusoidal digit flexion and extension at different rates force development. included 320 high-density surface EMG electrodes placed forearm muscles, markerless 3D kinematics...
The loss of bilateral hand function is a debilitating challenge for millions individuals that suffered motor-complete spinal cord injury (SCI). We have recently demonstrated in eight tetraplegic the presence highly functional spared motor neurons extrinsic muscles are still capable generating proportional flexion and extension signals. In this work, we hypothesized an artificial intelligence (AI) system could automatically learn electromyographic (EMG) patterns encode attempted movements...
In myoelectric control, simultaneous control of multiple degrees freedom can be challenging due to the dexterity human hand. Numerous studies have focused on hand functionality, however, they only a few freedom. this paper, 3DCNN-MLP model is proposed that uses high-density sEMG signals estimate 20 joint positions and grip force simultaneously. The deep learning maps muscle activity kinematics kinetics. models' performance also evaluated in estimating forces with real-time resolution. This...
Restoring limb motor function in individuals with spinal cord injury (SCI), stroke, or amputation remains a critical challenge, one which affects millions worldwide. Recent studies show through surface electromyography (EMG) that spared neurons can still be voluntarily controlled, even without visible movement . These signals decoded and used for intent estimation; however, current wearable solutions lack the necessary hardware software intuitive interfacing of degrees freedom after neural...
<p>Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity generated by muscles using sensors placed on skin. It has been widely used in field of prosthetics and other assistive systems because physiological connection between muscle movement dynamics. However, most existing sEMG-based decoding algorithms show limited number detectable degrees freedom can be proportionally simultaneously controlled real-time, which limits use EMG wide range...
<p>Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on skin. sEMG state-of-the-art method used to control active upper limb prostheses because of association between its amplitude and neural drive sent from spinal cord muscles. However, accurately estimating kinematics freely moving human hand using extrinsic remains challenge. Deep learning has been recently successfully applied this problem...
<p>Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on skin. sEMG state-of-the-art method used to control active upper limb prostheses because of association between its amplitude and neural drive sent from spinal cord muscles. However, accurately estimating kinematics freely moving human hand using extrinsic remains challenge. Deep learning has been recently successfully applied this problem...
<p>Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and algorithms hinders comparison clinical application development. To address these challenges, this publication introduces gaitmap ecosystem, comprehensive set open source Python packages gait foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets,...
<p>Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and algorithms hinders comparison clinical application development. To address these challenges, this publication introduces gaitmap ecosystem, comprehensive set open source Python packages gait foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets,...