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Recent studies have revealed that sensitive and private attributes could be decoded from sEMG signals, which incurs significant privacy threats to the users of sEMG applications. Most researches so far focus on improving the accuracy and reliability of sEMG models, but much less attention has been paid to their privacy. To fill this gap, this paper implemented and evaluated a framework to optimize the sEMG-based data-sharing mechanism. Our primary goal is to remove sensitive attributes in the sEMG features before sharing them with primary tasks while maintaining the data utility. We disentangled the identity-invariant task-relevant representations from original sEMG features. We shared it with the downstream pattern recognition tasks to reduce the chance of sensitive attributes being inferred by potential attackers. The proposed method was evaluated on data from twenty subjects, with training and testing data acquired 3-25 days apart. Experimental results show that the disentangled representations significantly lower the success rate of identity inference attacks compared to the original feature and its sparse representations generated by the state-of-the-art feature projection methods. Furthermore, the utility of the disentangled representation is also evaluated in hand gesture recognition tasks, showing superior performance over other methods. This work shows that disentangled representations of sEMG signals are a promising solution for privacy-reserving applications. 

Hand impairment is prevalent in individuals after stroke. Regaining independent finger control is especially challenging. An objective and continuous assessment of finger impairment could inform clinicians and allow them to prescribe targeted therapies. The objective of this preliminary work was to quantify the neuromuscular factors that contribute to impairment in independent finger control in chronic stroke survivors. We obtained high-density electromyographic (HD-EMG) signals of extrinsic finger muscles and fingertip forces, while stroke or control participants were instructed to produce independent finger forces. We observed an impaired ability to isolate individual muscle compartment activation (i.e., co-activation of muscle compartment). This muscle co-activation pattern correlated with finger independence as well as clinical assessment scales on hand impairment. Our preliminary work showed that HD-EMG recordings can be used to continuously monitor activation abnormalities of small finger muscles in contribution to impaired finger independence. With further development, the outcomes can provide a basis for clinical decision making to reduce hand impairments of stroke survivors. 

Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks. 

A reliable and functional neural interface is necessary to control individual finger movements of assistive robotic hands. Non-invasive surface electromyogram (sEMG) can be used to predict fingertip forces and joint kinematics continuously. However, concurrent prediction of kinematic and dynamic variables in a continuous manner remains a challenge. The purpose of this study was to develop a neural decoding algorithm capable of concurrent prediction of fingertip forces and finger dynamic movements. High-density electromyogram (HD-EMG) signal was collected during finger flexion tasks using either the index or middle finger: isometric, dynamic, and combined tasks. Based on the data obtained from the two first tasks, motor unit (MU) firing activities associated with individual fingers and tasks were derived using a blind source separation method. MUs assigned to the same tasks and fingers were pooled together to form MU pools. Twenty MUs were then refined using EMG data of a combined trial. The refined MUs were applied to a testing dataset of the combined task, and were divided into five groups based on the similarity of firing patterns, and the populational discharge frequency was determined for each group. Using the summated firing frequencies obtained from five groups of MUs in a multivariate linear regression model, fingertip forces and joint angles were derived concurrently. The decoding performance was compared to the conventional EMG amplitude-based approach. In both joint angles and fingertip forces, MU-based approach outperformed the EMG amplitude approach with a smaller prediction error (Force: 5.36±0.47 vs 6.89±0.39 %MVC, Joint Angle: 5.0±0.27° vs 12.76±0.40°) and a higher correlation (Force: 0.87±0.05 vs 0.73±0.1, Joint Angle: 0.92±0.05 vs 0.45±0.05) between the predicted and recorded motor output. The outcomes provide a functional and accurate neural interface for continuous control of assistive robotic hands. 

Background: Myoelectric-based decoding has gained popularity in upper-limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. Methods: We extracted MU information from high-density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects’ maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. Results: We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64 %MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36 %MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC). Conclusion: Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices. 

Improving prosthetic hand functionality is critical in reducing abandonment rates and improving the amputee’s quality of life. Techniques such as joint force estimation and gesture recognition using myoelectric signals could enable more realistic control of the prosthetic hand. To accelerate the translation of these advanced control strategies from lab to clinic, We created a virtual prosthetic control environment that enables rich user interactions and dexterity evaluation. The virtual environment is made of two parts, namely the Unity scene for rendering and user interaction, and a Python back-end to support accurate physics simulation and communication with control algorithms. By utilizing the built-in tracking capabilities of a virtual reality headset, the user can visualize and manipulate a virtual hand without additional motion tracking setups. In the virtual environment, we demonstrate actuation of the prosthetic hand through decoded EMG signal streaming, hand tracking, and the use of a VR controller. By providing a flexible platform to investigate different control modalities, we believe that our virtual environment will allow for faster experimentation and further progress in clinical translation. 

Neuromuscular electrical stimulation (NMES) targeting the muscle belly is commonly used to restore muscle strength in individuals with neurological disorders. However, early onset of muscle fatigue is a major limiting factor. Transcutaneous nerve stimulation (TNS) can delay muscle fatigue compared with traditional NMES techniques. However, the recruitment of Ia afferent fibers has not be specifically targeted to maximize muscle activation through the reflex pathway, which can lead to more orderly recruitment of motor units, further delaying fatigue. This preliminary study assessed the distribution of M-wave and H-reflex of intrinsic and extrinsic finger muscles. TNS was delivered using an electrode array placed along the medial side of the upper arm. Selective electrode pairs targeted the median and ulnar nerves innervating the finger flexors. High-density electromyography (HD EMG) was utilized to quantify the spatial distribution of the elicited activation of finger intrinsic and extrinsic muscles along the hand and forearm. The spatial patterns were characterized through isolation of the M-wave and H-reflex across various stimulation levels and EMG channels. Our preliminary results showed that, by altering the stimulation amplitude, distinct M-wave and H-reflex responses were evoked across EMG channels. In addition, distinct stimulation locations appeared to result in varied levels of reflex recruitment. Our findings indicate that it is possible to adjust stimulation parameters to maximize reflex activation, which can potentially facilitate physiological recruitment order of motoneurons.