  A transhumeral prosthesis restores missing anatomical segments below the
shoulder, including the hand. Active prostheses utilize real-valued, continuous
sensor data to recognize patient target poses, or goals, and proactively move
the artificial limb. Previous studies have examined how well the data collected
in stationary poses, without considering the time steps, can help discriminate
the goals. In this case study paper, we focus on using time series data from
surface electromyography electrodes and kinematic sensors to sequentially
recognize patients' goals. Our approach involves transforming the data into
discrete events and training an existing process mining-based goal recognition
system. Results from data collected in a virtual reality setting with ten
subjects demonstrate the effectiveness of our proposed goal recognition
approach, which achieves significantly better precision and recall than the
state-of-the-art machine learning techniques and is less confident when wrong,
which is beneficial when approximating smoother movements of prostheses.
