Quantitatively deciphering the relationship between muscle activation and thickness deformation is essential for diagnosing muscle-related diseases and monitoring muscle health (e.g., Facioscapulohumeral Dystrophy). Despite the potential of ultrasound (US) imaging and sensing to measure changes in muscle thickness during movements, it remains challenging to make a fully portable device, considering the wiring and data collection. On the other hand, surface electromyography (sEMG) can record muscle bioelectrical signals and measure muscle activations, offering a unique perspective that correlates with underlying changes in muscle thickness. This paper introduces a deep-learning-based approach that used sEMG signals to infer muscle deformation. Using a hierarchical combination of self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data, eliminating the dependency on applying ultrasound imaging techniques. The experimental results on six healthy subjects indicated that our approach could accurately predict muscle excursion with an average precision of 0.923 $\pm$ 0.900 mm, showing benefits in measuring muscle deformation only with a sEMG device. This technique facilitates real-time portable muscle health monitoring by sEMG to provide bioelectrical signals and biomechanical information. It indicates the great potential of using this technique in clinical diagnostics, sports science, and rehabilitation.

In Total Knee Replacement Arthroplasty (TKA), surgical robotics can provide image-guided navigation to fit implants with high precision. Its tracking approach highly relies on inserting bone pins into the bones tracked by the optical tracking system. This is normally done by invasive, radiative manners (implantable markers and CT scans), which introduce unnecessary trauma and prolong the preparation time for patients. To tackle this issue, ultrasound-based bone tracking could offer an alternative. In this study, we proposed a novel deep-learning structure to improve the accuracy of bone tracking by an A-mode ultrasound (US). We first obtained a set of ultrasound dataset from the cadaver experiment, where the ground truth locations of bones were calculated using bone pins. These data were used to train the proposed CasAtt-UNet to predict bone location automatically and robustly. The ground truth bone locations and those locations of US were recorded simultaneously. Therefore, we could label bone peaks in the raw US signals. As a result, our method achieved sub-millimeter precision across all eight bone areas with the only exception of one channel in the ankle. This method enables the robust measurement of lower extremity bone positions from 1D raw ultrasound signals. It shows great potential to apply A-mode ultrasound in orthopedic surgery from safe, convenient, and efficient perspectives.