  Accurate bone tracking is crucial for kinematic analysis in orthopedic
surgery and prosthetic robotics. Traditional methods (e.g., skin markers) are
subject to soft tissue artifacts, and the bone pins used in surgery introduce
the risk of additional trauma and infection. For electromyography (EMG), its
inability to directly measure joint angles requires complex algorithms for
kinematic estimation. To address these issues, A-mode ultrasound-based tracking
has been proposed as a non-invasive and safe alternative. However, this
approach suffers from limited accuracy in peak detection when processing
received ultrasound signals. To build a precise and real-time bone tracking
approach, this paper introduces a deep learning-based method for anatomical
region recognition and bone tracking using A-mode ultrasound signals,
specifically focused on the knee joint. The algorithm is capable of
simultaneously performing bone tracking and identifying the anatomical region
where the A-mode ultrasound transducer is placed. It contains the fully
connection between all encoding and decoding layers of the cascaded U-Nets to
focus only on the signal region that is most likely to have the bone peak, thus
pinpointing the exact location of the peak and classifying the anatomical
region of the signal. The experiment showed a 97% accuracy in the
classification of the anatomical regions and a precision of around 0.5$\pm$1mm
under dynamic tracking conditions for various anatomical areas surrounding the
knee joint. In general, this approach shows great potential beyond the
traditional method, in terms of the accuracy achieved and the recognition of
the anatomical region where the ultrasound has been attached as an additional
functionality.
