TY - JOUR
T1 - DisPad
T2 - Flexible On-Body Displacement of Fabric Sensors for Robust Joint-Motion Tracking
AU - CHEN, Xiaowei
AU - JIANG, Xiao
AU - FANG, Jiawei
AU - GUO, Shihui
AU - LIN, Juncong
AU - LIAO, Minghong
AU - LUO, Guoliang
AU - FU, Hongbo
PY - 2023/3
Y1 - 2023/3
N2 - 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. © 2023 ACM.
AB - 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. © 2023 ACM.
KW - domain adaption
KW - fuzzy entropy
KW - long short-term memory
KW - motion tracking
KW - robust signal processing
KW - soft sensors
KW - textile sensors
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85152483126&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85152483126&origin=recordpage
U2 - 10.1145/3580832
DO - 10.1145/3580832
M3 - RGC 21 - Publication in refereed journal
SN - 2474-9567
VL - 7
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
IS - 1
M1 - 5
ER -