Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle-computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle-computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Advancements in multiarticulate upper-limb prosthetics have outpaced the development of intuitive, non-invasive control mechanisms for implementing them. Surface electromyography is currently the most popular non-invasive control method, but presents a number of drawbacks including poor deep-muscle specificity. Previous research established the viability of ultrasound imaging as an alternative means of decoding movement intent, and demonstrated the ability to distinguish between complex grasps in able-bodied subjects via imaging of the anterior forearm musculature. In order to translate this work to clinical viability, able-bodied testing is insufficient. Amputation-induced changes in muscular geometry, dynamics, and imaging characteristics are all likely to influence the effectiveness of our existing techniques. In this work, we conducted preliminary trials with a transradial amputee participant to assess these effects, and potentially elucidate necessary refinements to our approach. Two trials were performed, the first using a set of three motion types, and the second using four. After a brief training period in each trial, the participant was able to control a virtual prosthetic hand in real-time; attempted grasps were successfully classified with a rate of 77% in trial 1, and 71% in trial 2. While the results are sub-optimal compared to our previous able-bodied testing, they are a promising step forward. More importantly, the data collected during these trials can provide valuable information for refining our image processing methods, especially via comparison to previously acquired data from able-bodied individuals. Ultimately, further work with amputees is a necessity for translation towards clinical application.

Monitoring muscle hemodynamics and oxygenation is important for studying muscle function and fatigue. Current state-of-the-art for noninvasive oximetry is near-infrared spectroscopy (NIRS), which provides relative oxygen saturation (SO2) with good sensitivity but has poor spatial resolution and sensing depth, and does not provide anatomical context. In this study, we demonstrated the utility of a dual-modality imaging system that could generate co-registered ultrasound (US) and photoacoustic (PA) images for real-time, functional imaging of human muscle. The system consisted of a wavelength-tunable pulsed laser (Opotek) integrated with a research ultrasound system (Verasonics). US images provided anatomical context and the PA images acquired at 690 nm and 830 nm were processed to estimate SO2 during a sustained isometric contraction and return to rest using a protocol approved by our Institutional Review Board. PA-based SO2 was compared to measurements acquired from a commercially available NIRS oximeter under the same conditions. Preliminary results showed good qualitative agreement between SO2 dynamics observed via PA-based approach and NIRS, with contraction coinciding with an exponential decay in SO2, and relaxation with a return to original levels. This in vivo study demonstrated the feasibility of PA imaging for measuring temporally and spatially resolved muscle oxygenation during functional tasks.

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Current commercially available prostheses based on myoelectric control have limited functionality, leading to many amputees abandoning use. Myoelectric control using surface electrodes has a number of limitations and lacks specificity for deep muscles, presenting a continued need for more robust strategies. We propose a new strategy for sensing muscle activity based on real-time ultrasound imaging. Results from our previous work demonstrate that complex motions could be classified with 92% accuracy in real-time. However, arm and hand repositioning during natural movements tend to alter the geometry of forearm musculature, possibly affecting performance. In this study, we evaluated the robustness of the image-based control strategy in the presence of varied forearm positions on able-bodied subjects. Ultrasound images of the forearm muscles were collected during two different scenarios using a Sonix RP with a 5–14 MHz linear probe. The subject was asked to perform four hand motions at eight different arm positions and three levels of wrist pronation. Images were analyzed to generate activity patterns for each motion and then classified. Results demonstrate that forearm positions do not significantly compromise reliability. We also show that performance could be further improved by including additional training activity patterns corresponding to motions performed in a few selected arm positions.