Article
Engineering, Multidisciplinary
Lizhi Pan, Kai Liu, Kun Zhu, Jianmin Li
Summary: Amputees have poorer performances in EMG pattern recognition compared to able-bodied individuals, and factors such as muscle weakness and atrophy, limb length, and motor cortex decrease have been studied. However, the impact of the absence of joint movements has not been explored. This study found that hand and wrist joint movements significantly affect EMG pattern recognition, providing a new perspective for future research.
JOURNAL OF BIONIC ENGINEERING
(2022)
Article
Engineering, Biomedical
Mohamed Z. Amrani, Christoph W. Borst, Nouara Achour
Summary: This study introduces and evaluates a multi-sensory hand pattern recognizer that utilizes MLP and DT classifiers to achieve optimal hand shape detection. By integrating LM controllers, VIVE Pro Eye camera, and MYO armband, the study successfully reaches an average classification accuracy of 98.31%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Chemistry, Analytical
Sara Abbaspour, Autumn Naber, Max Ortiz-Catalan, Hamid GholamHosseini, Maria Linden
Summary: This study compared the offline and real-time performance of nine different classification algorithms, showing that linear discriminant analysis and maximum likelihood estimation performed well in offline decoding, while the multilayer perceptron also excelled in real-time investigation.
Article
Engineering, Biomedical
Ziling Zhu, Jianan Li, William J. Boyd, Carlos Martinez-Luna, Chenyun Dai, Haopeng Wang, He Wang, Xinming Huang, Todd R. Farrell, Edward A. Clancy
Summary: Recent research has made progress in achieving simultaneous, independent, and proportional control of hand-wrist prostheses using surface electromyogram signals. Two regression-based controllers were evaluated and compared with a conventional sequential controller, with the regression controllers performing better in certain tasks.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Clinical Neurology
Chengpeng Hu, Tong Wang, Kenry W. C. Leung, Le Li, Raymond Kai-Yu Tong
Summary: This study demonstrated that FES-assisted cycling training improved lower limb function by developing coordinated muscle activation and facilitating an orderly myofiber arrangement. The results also indicated that EIM and sEMG can be used to evaluate lower extremity function alterations after rehabilitation training.
FRONTIERS IN NEUROLOGY
(2021)
Article
Engineering, Biomedical
Xinyu Song, Shirdi Shankara Van De Ven, Lanlan Liu, Frank J. Wouda, Hong Wang, Peter B. Shull
Summary: The study proposed a serious game rehabilitation system based on ADLs to train motor function and coordination, utilizing a multi-sensor fusion model to estimate users' natural upper limb movement. Results showed the significant impact of elbow extension/flexion on hand gesture recognition, the effectiveness of different sensor and classifier configurations, and the superior performance of the EMG+FMG-combined model against arm position changes. The system was found to improve stroke patients' ability to perform ADLs, demonstrating the potential of the proposed training system.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Computer Science, Information Systems
Haider Ali Javaid, Mohsin Islam Tiwana, Ahmed Alsanad, Javaid Iqbal, Muhammad Tanveer Riaz, Saeed Ahmad, Faisal Abdulaziz Almisned
Summary: This study proposed a classification and recognition of hand gestures using electromyography signals, achieving an overall accuracy of 83.9% with the ensemble (bagged tree) classifier having the highest accuracy. An embedded system-based classification approach was used to design an upper limb prosthesis, making the movement and performance of the prosthesis more flexible.
Article
Engineering, Electrical & Electronic
Hyeyun Lee, Soyoung Lee, Jaeseong Kim, Heesoo Jung, Kyung Jae Yoon, Srinivas Gandla, Hogun Park, Sunkook Kim
Summary: With the help of AI-based algorithms, the accuracy of gesture recognition using sEMG signals has increased. An array of bipolar stretchable sEMG electrodes, combined with a self-attention-based graph neural network, is developed to achieve highly accurate gesture recognition. The system can differentiate static and dynamic gestures with about 97% accuracy using a single trial per gesture. The array also has skin-like attributes and can provide stable EMG signals even after long-term testing and multiple reuses.
NPJ FLEXIBLE ELECTRONICS
(2023)
Article
Robotics
Younggeol Cho, Yeongseok Lee, Pyungkang Kim, Seokhwan Jeong, Kyung-Soo Kim
Summary: This article describes a preliminary platform of a robotic prosthetic hand system, called the MSC hand, which integrates innovative mechanical mechanisms and intuitive control methods. The hand utilizes mode-switchable twisted string actuators to achieve a wide range of grasping speed and force. It allows for rapid and precise gripping and simultaneous and proportional control of multiple fingers.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Biomedical
Markus Nowak, Ivan Vujaklija, Agnes Sturma, Claudio Castellini, Dario Farina
Summary: This study introduces a system for intuitive simultaneous control of multiple degrees of freedom in the hand and wrist based on EMG signals. By training a regression model, the system is able to accurately capture amplitude information and predict dynamic movements. The results show that the system can effectively control the hand and wrist in able-bodied individuals and even amputees, highlighting its translational potential for prosthetic control.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Physiology
Xinyu Song, Shirdi Shankara van de Ven, Shugeng Chen, Peiqi Kang, Qinghua Gao, Jie Jia, Peter B. Shull
Summary: This study proposes a wearable multimodal serious games approach for hand movement training after stroke. By fusing data from force myography, electromyography, and inertial measurement unit sensors, the hand gesture classification accuracy for stroke patients was improved. Patients showed higher enthusiasm and motivation in hand movement training while playing the serious games, and expressed confidence in the potential of this approach to improve upper limb motor function.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Neurosciences
Mo Han, Mehrshad Zandigohar, Sezen Yagmur Gunay, Gunar Schirner, Deniz Erdogmus
Summary: This study proposes a framework for classifying dynamic EMG signals into gestures and examines the impact of different movement phases using an unsupervised segmentation and labeling method. Data from large gesture vocabularies were collected for encoding transitions between grasp intents based on natural sequences of human grasp movements. A classifier was constructed for identifying gesture labels based on dynamic EMG signals without the need for supervised annotation of kinematic movements. Various training strategies using EMG data from different movement phases were evaluated in real-time performance transitions.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Engineering, Biomedical
Javier Navallas, Adrian Eciolaza, Cristina Mariscal, Armando Malanda, Javier Rodriguez-Falces
Summary: An analytical derivation of the EMG signal's amplitude probability density function is presented, showing the changes in the distribution as the muscle contraction increases. The EMG filling factor is introduced to quantify the degree to which the EMG signal has been built-up. The usefulness of the EMG filling factor and curve is demonstrated with simulated and real signals.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Chemistry, Analytical
Joonyoung Jung, Dong-Woo Lee, Yong Ki Son, Bae Sun Kim, Hyung Cheol Shin
Summary: A novel dual-channel electromyography method was proposed to estimate volitional electromyography signals during functional electrical stimulation. Experimental results showed that the method performed better under rapidly modulated stimulation intensities.
Article
Computer Science, Information Systems
Yilin Liu, Shijia Zhang, Mahanth Gowda
Summary: This article introduces NeuroPose, a system that demonstrates the feasibility of 3-D finger motion tracking using wearable electromyography (EMG) sensors. The system combines anatomical constraints of finger motion with machine learning architectures to extract 3-D finger motion from EMG data. The system achieves high accuracy and robustness to sensor variations and user wrist positions. It also validates the mirrored bilateral training approach for prosthetic devices.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Biophysics
Dan Qiu, Sang Wook Lee, Mukarram Amine, Derek G. Kamper
JOURNAL OF BIOMECHANICS
(2017)
Article
Neurosciences
Hoi B. Nguyen, Sang Wook Lee, Michelle L. Harris-Love, Peter S. Lum
JOURNAL OF NEUROPHYSIOLOGY
(2017)
Article
Engineering, Biomedical
Sang Wook Lee, Billy C. Vermillion, Shashwati Geed, Alexander W. Dromerick, Derek G. Kamper
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2018)
Article
Neurosciences
Jinsook Roh, Sang Wook Lee, Kevin D. Wilger
JOURNAL OF MOTOR BEHAVIOR
(2019)
Article
Neurosciences
Kristin M. Quick, Jessica L. Mischel, Patrick J. Loughlin, Aaron P. Batista
JOURNAL OF NEUROPHYSIOLOGY
(2018)
Article
Neurosciences
Matthew D. Golub, Patrick T. Sadtler, Emily R. Oby, Kristin M. Quick, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Aaron P. Batista, Steven M. Chase, Byron M. Yu
NATURE NEUROSCIENCE
(2018)
Article
Engineering, Biomedical
Billy C. Vermillion, Alexander W. Dromerick, Sang Wook Lee
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2019)
Article
Engineering, Biomedical
Dong Hyun Kim, Sang Wook Lee, Hyung-Soon Park
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2019)
Article
Biophysics
Hien Nguyen, Billy C. Vermillion, Sang Wook Lee
JOURNAL OF BIOMECHANICS
(2019)
Article
Neurosciences
Sang Wook Lee, Dan Qiu, Heidi C. Fischer, Megan O. Conrad, Derek G. Kamper
JOURNAL OF NEUROPHYSIOLOGY
(2020)
Article
Neurosciences
Pattamon Panyakaew, Hyun Joo Cho, Sang Wook Lee, Tianxia Wu, Mark Hallett
JOURNAL OF NEUROSCIENCE
(2020)
Article
Neurosciences
Jing Wang, Peter S. Lum, Reza Shadmehr, Sang Wook Lee
Summary: This study found that visual amplification can change the perceived effort associated with using a limb, influencing arm selection and preparation time for movements. Movements requiring less effort may start more quickly.
JOURNAL OF NEUROPHYSIOLOGY
(2021)
Article
Clinical Neurology
Hae-Won Shin, Hyun Joo Cho, Sang Wook Lee, Hitoshi Shitara, Mark Hallett
Summary: In patients with CD, sensory tricks lead to a significant increase in late CNV, suggesting that sensory tricks may affect mechanisms related to the motor preparatory phase. These tricks may normalize impaired motor preparation in dystonia, contributing to improved dystonic symptoms.
PARKINSONISM & RELATED DISORDERS
(2021)
Article
Neurosciences
Hien Nguyen, Thanh Phan, Reza Shadmehr, Sang Wook Lee
Summary: This study investigated the effects of unilateral and bilateral impairments on bimanual task performance in stroke survivors. Unilateral impairments of the more-impaired limb, including weakness and loss of directional control, mainly contribute to bimanual asymmetry, but stroke survivors generally produce higher force with their more-impaired limb than their relative capacity. Bilateral force coordination was significantly impaired in stroke survivors, but its degree of impairment was not related to their unilateral impairments.
JOURNAL OF NEUROPHYSIOLOGY
(2023)
Article
Clinical Neurology
Hien Nguyen, Thanh Phan, Reza Shadmehr, Sang Wook Lee
Summary: The reduction in use of the impaired arm following stroke may be primarily due to the subjective increase in effort required to use that arm. Lowering the energetic cost of reaching increases the use of the less-used arm in stroke survivors. Task accuracy requirement also influences arm choice in both stroke survivors and neurologically-intact subjects.
NEUROREHABILITATION AND NEURAL REPAIR
(2023)