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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.