  The kinematics of human movements and locomotion are closely linked to the
activation and contractions of muscles. To investigate this, we present a
multimodal dataset with benchmarks collected using a novel pair of Intelligent
Knee Sleeves (Texavie MarsWear Knee Sleeves) for human pose estimation. Our
system utilizes synchronized datasets that comprise time-series data from the
Knee Sleeves and the corresponding ground truth labels from the visualized
motion capture camera system. We employ these to generate 3D human models
solely based on the wearable data of individuals performing different
activities. We demonstrate the effectiveness of this camera-free system and
machine learning algorithms in the assessment of various movements and
exercises, including extension to unseen exercises and individuals. The results
show an average error of 7.21 degrees across all eight lower body joints when
compared to the ground truth, indicating the effectiveness and reliability of
the Knee Sleeve system for the prediction of different lower body joints beyond
the knees. The results enable human pose estimation in a seamless manner
without being limited by visual occlusion or the field of view of cameras. Our
results show the potential of multimodal wearable sensing in a variety of
applications from home fitness to sports, healthcare, and physical
rehabilitation focusing on pose and movement estimation.
