The complex interplay of neural and muscular activity underlying involuntary rhythmic wrist tremors poses significant challenges for effective symptom management, particularly with identifying key tremor characteristics such as dominant frequency and amplitude. This study introduces an ultrasound (US)-enhanced data-driven modeling framework that uses wrist angle kinematics as state variables, which are augmented with muscle-specific ultrasound data inputs. We explore three modeling configurations: a wrist kinematics-only baseline model, a fixed-parameter US-augmented model, and a real-time adaptive US-enhanced model updated using a recursive least squares (RLS) algorithm. Comprehensive validation using time- and frequency-domain analyses was conducted using experimental data from six patients with tremor. Results show that integrating US input improves modeling specificity and accuracy by capturing internal muscle dynamics and temporal evolution patterns that are not accessible through IMU alone. Specifically, the time-domain modeling error (nRMSE) decreased substantially from 49.36% in the baseline model to 24.53% in the US-augmented model. The adaptive model further reduced the error to 0.48%, demonstrating its ability to account for the variability of tremor behaviors dynamically. Moreover, this work introduces, for the first time in tremor research, a comprehensive eigenvalue analysis of the data-driven model to extract clinically relevant tremor characteristics. The method enables accurate estimation of dominant tremor frequencies (average across patients nRMSE = 12.7%), quantification of dominant state contributions (mean $\Delta _{\text{dominance}}$ = 18.73%), and reliable tremor event detection (F1-score = 0.796). These findings highlight the framework's ability to not only reproduce tremor trajectories but also uncover how tremor behavior evolves over time in response to underlying neuromuscular activity. This work establishes a foundation for real-time tremor tracking and model-based control strategies, such as closed-loop afferent stimulation. By leveraging the unique sensing capabilities of ultrasound, the proposed framework offers a promising path toward personalized tremor modeling and intervention.

A hybrid exoskeleton is a class of wearable robotic technology that simultaneously uses a powered exoskeleton and functional electrical stimulation (FES) to generate assistive joint torques for people with impaired mobility due to neurological disorders such as spinal cord injury (SCI). The hybrid assistive technology benefits from FES that actively elicits force from paralyzed muscles via their neural excitation, leading to muscle strengthening. The main technical barrier to realizing the hybrid technology is to attain stable coordination between FES and the exoskeleton despite the quick onset of FES-induced muscle fatigue, which causes a rapid decline in the muscle force. Current methods to measure the induced fatigue lack direct muscle state measurements and may be ineffective at capturing the muscle force decay due to FES. Instead, ultrasound (US) imaging accurately quantifies FES-related muscle contractility and fatigue due to the direct visualization of muscle fibers. In this article, we use real-time US imaging-derived muscle strain changes as biomarkers of FES-induced fatigue in an optimal controller that modulates exoskeleton assistance and FES dosage. To demonstrate that real-time US imaging is a promising muscle–machine interface technology that can optimize shared control in a hybrid exoskeleton, we perform experiments involving continuous seated knee extension and over-ground walking tasks on two participants with SCI and four participants without disabilities. Furthermore, this work helps design a novel and unprecedented robotic gait technology with the capability to impart FES-associated therapeutic benefits while assisting the gait of neurologically impaired individuals, including those with SCI, stroke, multiple sclerosis, etc.