Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile
Wenwen Zhang, Arvin Tashakori, Zenan Jiang, Amir Servati, Harishkumar, Narayana, Saeid Soltanian, Rou Yi Yeap, Meng Han Ma, Lauren Toy, Peyman, Servati

TL;DR
This paper introduces a multimodal dataset and a camera-free system using intelligent knee sleeves for accurate 3D lower body motion estimation, demonstrating potential in fitness, sports, healthcare, and rehabilitation.
Contribution
The study presents a novel wearable system and dataset for real-time 3D lower body pose estimation without cameras, enabling accurate movement analysis across different activities and individuals.
Findings
Average error of 7.21 degrees in joint prediction
Effective pose estimation without visual occlusion
Potential applications in healthcare and sports
Abstract
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…
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Taxonomy
TopicsHuman Pose and Action Recognition · Sports Performance and Training · Infrared Thermography in Medicine
