SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force from Highly Sparse Ultrasound Images.

Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared to surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches for tracking muscle anatomical structures or extracting features from brightness-mode (B-mode) images and amplitude-mode (A-mode) signals. This paper uses an offline regression convolutional neural network (CNN) called SonoMyoNet to estimate continuous isometric force from sparse ultrasound scanlines. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to estimate continuous isometric force accurately. The performance of SonoMyoNet was evaluated by varying the number of scanlines to simulate the placement of multiple single-element ultrasound transducers in a wearable system. Results showed that SonoMyoNet could accurately predict isometric force with just four scanlines and is immune to speckle noise and shifts in the scanline location. Thus, the proposed network reduces the computational load involved in feature tracking algorithms and estimates muscle force from the global features of sparse ultrasound images.

The Development of Disinhibited Social Engagement Disorder: A Case Report on the Psychiatric Implications of Prenatal Fentanyl Use.

Exposure to drugs during pregnancy can result in neonatal abstinence syndrome (NAS), low birth weight, attention-deficit/hyperactivity disorder (ADHD), and behavioral issues, particularly during the school-age years. Recent research has expanded our understanding of the consequences of fentanyl exposure during pregnancy beyond the more commonly recognized effects, including respiratory complications, neurodevelopmental effects, increased risk of substance use disorders, gastrointestinal complications, cardiovascular effects, epigenetic changes, behavioral and emotional regulation disruptions, and long-term cognitive impairments. We present the case of a five-year-old female placed in foster care with a past medical history of asthma and a past psychiatric history of ADHD, oppositional defiant disorder (ODD), and disinhibited social engagement disorder. Her mother has a long history of substance use during pregnancy. From our interaction with the patient presented, we see that prenatal exposure to substances such as fentanyl and the disruption of attachment figures can have profound and lasting effects on a child's life, encompassing behavioral and cognitive aspects.

Sonomyography Combined with Vibrotactile Feedback Enables Precise Target Acquisition Without Visual Feedback.

Upper limb prosthesis users currently lack haptic feedback from their terminal devices, which significantly limits their ability to meaningfully interact with their environment. Users therefore rely heavily on visual feedback when using terminal devices. Previously, it has been shown that force-related feedback from an end-effector or virtual environment can help the user minimize errors and improve performance. Currently, myoelectric control systems enable the user to control the velocity of terminal devices. We have developed a novel control method using ultrasound sensing, called sonomyography, that enables position control based on mechanical deformation of muscles. In this paper, we investigated whether the proprioceptive feedback from muscle deformation combined with vibrotactile haptic feedback can minimize the need for visual feedback. Able bodied subjects used sonomyography to control a virtual cursor, and performed a target acquisition task. The effect of visual and haptic feedback on performance of a target acquisition task was systematically tested. We found that subjects made large errors when they tried to reacquire a target without visual feedback, but in the presence of real-time haptic feedback, the precision of the target position improved, and were similar to when visual feedback was used for target acquisition. This result has implications for improving the performance of prosthetic control systems.

Evaluation of the Role of Proprioception During Proportional Position Control Using Sonomyography: Applications in Prosthetic Control.

Prosthetics need to incorporate the users sense of proprioception into the control paradigm to provide intuitive control, and reduce training times and prosthetic rejection rates. In the absence of functional tasks with a prosthetic, virtual cursor control tasks have been used to train users to control multiple degrees of freedom. In this study, A proportional position signal was derived from the cross-sectional ultrasound images of the users forearm. We designed a virtual cursor control task with one degree of freedom to measure the users ability to repeatably and accurately acquire different levels of muscle flexion, using only their sense of proprioception. The experiment involved a target acquisition task, where the cursors height corresponded to the extent of muscle flexion. Users were asked to acquire targets on a screen. Visual feedback was disabled at certain times during the experiment, to isolate the effect of proprioception. We found that as visual feedback was taken away from the subjects, position error increased but their stability error did not change significantly. This indicates that users are not perfect at using only their proprioceptive sense to reacquire a level of muscle flexion, in the absence of haptic or visual feedback. However, they are adept at retaining an acquired flexion level without drifting. These results could help to quantify the role of proprioception in target acquisition tasks, in the absence of haptic or visual feedback.