Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographiccontrol. Unlike myoelectric control strategies which measure electrical activation of muscles and use the extracted signals to determine the velocity of an end-effector; our sonomyography-based strategy measures mechanical muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Therefore, our sonomyography-based control is congruent with a prosthetic user’s innate proprioception of muscle deformation in the residual limb. In this work, we evaluated proprioceptive sonomyographic control with 5 prosthetic users and 5 able-bodied participants in a virtual target achievement and holding task for 5 different hand motions. We observed that with limited training, the performance of prosthetic users was comparable to that of able-bodied participants and thus conclude that proprioceptive sonomyographic control is a robust and intuitive prosthetic control strategy.

Objective: Wearable ultrasound is emerging as a new paradigm of real-time imaging in freely moving humans and has wide applications from cardiovascular health monitoring to human gesture recognition. However, current wearable ultrasound devices have typically employed pulse-echo imaging which requires high excitation voltages and sampling rates, posing safety risks, and requiring specialized hardware. Our objective was to develop and evaluate a wearable ultrasound system based on time delay spectrometry (TDS) that utilizes low-voltage excitation and significantly simplified instrumentation. Methods: We developed a TDS-based ultrasound system that utilizes continuous, frequency-modulated sweeps at low excitation voltages. By mixing the transmit and receive signals, the system digitizes the ultrasound signal at audio frequency (kHz) sampling rates. Wearable ultrasound transducers were developed, and the system was characterized in terms of imaging performance, acoustic output, thermal characteristics, and applications in musculoskeletal imaging. Results: The prototype TDS system is capable of imaging up to 6 cm of depth with signal-to-noise ratio of up to 42 dB at a spatial resolution of 0.33 mm. Acoustic and thermal radiation measurements were within clinically safe limits for continuous ultrasound imaging. We demonstrated the ability to use a 4-channel wearable system for dynamic imaging of muscle activity. Conclusion: We developed a wearable ultrasound imaging system using TDS to mitigate challenges with pulse echo-based wearable ultrasound imaging systems. Our device is capable of high-resolution, dynamic imaging of deep-seated tissue structures and is safe for long-term use. Significance: This work paves the way for low-voltage wearable ultrasound imaging devices with significantly reduced hardware complexity.

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R² of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Impairment of hand functions in individuals with spinal cord injury (SCI) severely disrupts activities of daily living. Recent advances have enabled rehabilitation assisted by robotic devices to augment the residual function of the muscles. Traditionally, electromyography-based muscle activity sensing interfaces have been utilized to sense volitional motor intent to drive robotic assistive devices. However, the dexterity and fidelity of control that can be achieved with electromyography-based control have been limited due to inherent limitations in signal quality. We have developed and tested a muscle-computer interface (MCI) utilizing sonomyography to provide control of a virtual cursor for individuals with motor-incomplete spinal cord injury. We demonstrate that individuals with SCI successfully gained control of a virtual cursor by utilizing contractions of muscles of the wrist joint. The sonomyography-based interface enabled control of the cursor at multiple graded levels demonstrating the ability to achieve accurate and stable endpoint control. Our sonomyography-based muscle-computer interface can enable dexterous control of upper-extremity assistive devices for individuals with motor-incomplete SCI.

Background: Despite significant advances in biosignal extraction techniques for studying neuromotor disorders, there remains an unmet need for a method that effectively links muscle structure and dynamics to muscle activation. Addressing this gap could improve the quantification of neuromuscular impairments and pave the way for precision rehabilitation. In this study, we demonstrate the proof of concept of recording multimodal signals from the brain, muscles, and resulting limb kinematics. We also explore the use of ultrasound imaging to extract limb kinematics. Methods: We collected data from three healthy volunteers and one individual with cerebral palsy during single degree-of-freedom ankle and wrist movements. Participants performed range of motion (ROM) tasks at approximately 1-second intervals, either volitionally or through functional electrical stimulation. We simultaneously recorded electroencephalography, surface electromyography (EMG), continuous ultrasound imaging, and motion capture data. Joint kinematics were computed from ultrasound imaging using a technique called sonomyography (SMG), and we evaluated the technical feasibility of estimating joint kinematics from both sonomyography and surface EMG signals. Results: The technical feasibility study evaluated joint angle prediction using EMG and SMG under volitional (FES-OFF) and electrically stimulated (FES-ON) conditions. Root mean squared error (RMSE) between predicted and measured joint angles was computed for multiple methods of extracting kinematics from EMG and SMG. EMG-based RMSE ranged from 0.34 to 0.57 (FES-OFF) and 0.43–0.51 (FES-ON). SMG-based RMSE ranged from 0.10 to 0.25 across all conditions and methods. Linear regression analysis produced $$R^2$$ R 2 values between 0.31 and 0.81 depending on joint, condition, and method. No significant RMSE difference was found between FES-ON and FES-OFF conditions within SMG. SMG RMSE values were also comparable to previously reported values (10-25%) in prior literature. Conclusion: Our findings suggest that sonomyography can be used as a noninvasive method for estimating joint kinematics when the joint movement is driven either by volition or by functional electrical stimulation. This technique can potentially be be useful in evaluating altered muscle dynamics and driving assistive and rehabilitation devices in individuals with neuromotor disorders such as cerebral palsy.

Thermal perception is important in the experience of touching real objects, and thermal display devices are of growing interest for applications in virtual reality, medicine, and wearable technologies. In this paper, we designed a new thermal display, and investigated the perception of spatially varying thermal stimuli, including the thermal grill illusion. The latter is a perceptual effect in which a burning sensation is elicited in response to touching a surface composed of spatially juxtaposed warm and cool areas. Using a computer controlled thermal display, we present experiments in which we measured temporal correlates of the perception of spatially inhomogeneous stimuli, or thermal grills. We assessed the intensity of responses elicited by thermal grill stimuli with different temperature settings, and measured the response time until the onset of burning sensations. We found that thermal grills elicited highly stereotyped responses. The experimental results also indicated that as the temperature difference increases, the intensity increases monotonically, while the response time decreases monotonically. Consequently, perceived intensity was inversely correlated with response time. Under current physiological explanations, responses to thermal stimuli depend on tissue heating, neural processing, and the spatial distribution (or juxtaposition) of surface temperatures. The results of this study could help to inform models accounting for these factors, enabling new applications of the thermal grill illusion.