Objective: Sonomyography has been shown to be a promising method for decoding volitional motor intent from analysis of ultrasound images of the forearm musculature. The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions, and to investigate the effect of using a sparse set of ultrasound scanlines for motion classification for ultrasound-based muscle–computer interfaces (MCIs). Methods: The optimal placement of the ultrasound transducer along the forearm was identified using freehand three-dimensional reconstructions of the muscle thickness during rest and motion completion. Based on the ultrasound images acquired from the optimally placed transducer, classification accuracy with equally spaced scanlines across the cross-sectional field of view was determined. Furthermore, the unique contribution of each scanline to class discrimination using Fisher criterion (FC) and mutual information (MI) with respect to motion discriminability was determined. Results: Experiments with five able-bodied subjects show that the maximum muscle deformation occurred between 40%–50% of the forearm length for multiple degrees-of-freedom. The average classification accuracy was 94% ± 6% with the entire 128-scanline image and 94% ± 5% with four equally spaced scanlines. However, no significant improvement in classification accuracy was observed with optimal scanline selection using FC and MI. Conclusion: For an optimally placed transducer, a small subset of ultrasound scanlines can be used instead of a full imaging array without sacrificing performance in terms of classification accuracy for multiple degrees-of-freedom. Significance: The selection of a small subset of transducer elements can enable the reduction of computation, and simplification of the instrumentation and power consumption of wearable sonomyographic MCIs, particularly for rehabilitation and gesture recognition applications.

Noninvasive methods for estimation of joint and muscle forces have widespread clinical and research applications. Surface electromyography (sEMG) provides a measure of the neural activation of muscles which can be used to estimate the force produced by the muscle. However, sEMG based measures of force suffer from poor signal-to-noise ratio and limited spatiotemporal specificity. In this paper, we propose a sonomyography or ultrasound imaging-based offline approach for estimating continuous isometric force from a sparse set of ultrasound scanlines. Our approach isolates anatomically relevant features from A-mode scanlines isolated from B-mode images, thus greatly reducing the dimensionality of the feature space and the computational complexity involved in traditional ultrasound-based methods. We evaluate the performance of four regression methodologies for force prediction using the reduced sonomyographic feature set. We also evaluate the feasibility of a practical wearable sonomyography-based system by simulating the effect of transducer placement and varying the number of transducers used in force prediction. Our results demonstrate that Gaussian process regression models outperform other regression methods in predicting continuous force levels from just four equispaced transducers in offline settings. We believe that these findings will aid in the design of wearable, robust and computationally efficient sonomyography-based force prediction systems.

Objective : Sonomyography, or ultrasound-based sensing of muscle deformation, is an emerging modality for upper limb prosthesis control. Although prior studies have shown that individuals with upper limb loss can achieve successful motion classification with sonomyography, it is important to better understand the time-course over which proficiency develops. In this study, we characterized user performance during their initial and subsequent exposures to sonomyography. Method : Ultrasound images corresponding to a series of hand gestures were collected from individuals with transradial limb loss under three scenarios: during their initial exposure to sonomyography (Experiment 1), during a subsequent exposure to sonomyography where they were provided biofeedback as part of a training protocol (Experiment 2), and during testing sessions held on different days (Experiment 3). User performance was characterized by offline classification accuracy, as well as metrics describing the consistency and separability of the sonomyography signal patterns in feature space. Results : Classification accuracy was high during initial exposure to sonomyography (96.2 ± 5.9%) and did not systematically change with the provision of biofeedback or on different days. Despite this stable classification performance, some of the feature space metrics changed. Conclusions : User performance was strong upon their initial exposure to sonomyography and did not improve with subsequent exposure. Clinical Impact : Prosthetists may be able to quickly assess if a patient will be successful with sonomyography without submitting them to an extensive training protocol, leading to earlier socket fabrication and delivery.

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.

Regional anesthesia delivery for ocular surgery involves insertion of a syringe needle into the orbital space at the proper position and orientation such that ocular structures remain undamaged while avoiding adverse systemic reactions. Additionally, anesthetic fluid must be injected at an appropriate rate to achieve painless and rapid akinesia. Training on human subjects is risky and animal cadavers do not emulate human ocular anatomy. Thus, a training system, which closely replicates human ocular anatomical structure and provides real-time qualitative feedback on the effectiveness and safety of the anesthetic procedure would significantly mitigate risks associated with real life procedures. This paper presents a rapid prototyped, anatomically precise training manikin that detects the proximity and touch of syringe needle to the extraocular muscles and alarms the trainee to avoid injury. The proximity of the needle to the muscle structure is detected using capacitive sensors integrated in the manikin. A Hall-effect sensor-based measurement scheme for detection of rate of injection from a syringe has been developed. The specially designed syringe piston provides illusion of fluid flow inside the manikin for the trainee while reducing anesthesia wastage. A virtual instrument developed measures the output from capacitive sensing electrodes and Hall-effect sensor and displays it to the trainee through a graphical user interface. The proposed capacitive touch and proximity detection schemes have been validated by tests performed on the prototype system. The rate of injection was measured in real time on a prototype syringe, demonstrating the practical use of the system for medical training purposes.