  The last few decades have witnessed an emerging trend of wearable soft
sensors; however, there are important signal-processing challenges for soft
sensors that still limit their practical deployment. They are error-prone when
displaced, resulting in significant deviations from their ideal sensor output.
In this work, we propose a novel prototype that integrates an elbow pad with a
sparse network of soft sensors. Our prototype is fully bio-compatible,
stretchable, and wearable. We develop a learning-based method to predict the
elbow orientation angle and achieve an average tracking error of 9.82 degrees
for single-user multi-motion experiments. With transfer learning, our method
achieves the average tracking errors of 10.98 degrees and 11.81 degrees across
different motion types and users, respectively. Our core contributions lie in a
solution that realizes robust and stable human joint motion tracking across
different device displacements.
