In this section
@inproceedings{Li:2024:10.1145/3623509.3633374,
author = {Li, Y and Zhou, Y and Shen, C and Stewart, R},
doi = {10.1145/3623509.3633374},
pages = {1--10},
publisher = {ACM},
title = {E-textile sleeve with graphene strain sensors for arm gesture classification of mid-air interactions},
url = {http://dx.doi.org/10.1145/3623509.3633374},
year = {2024}
}
TY - CPAPER
AB - Arm gestures play a pivotal role in facilitating natural mid-air interactions. While computer vision techniques aim to detect these gestures, they encounter obstacles like obfuscation and lighting conditions. Alternatively, wearable devices have leveraged interactive textiles to recognize arm gestures. However, these methods predominantly emphasize textile deformation-based interactions, like twisting or grasping the sleeve, rather than tracking the natural body movement.This study bridges this gap by introducing an e-textile sleeve system that integrates multiple ultra-sensitive graphene e-textile strain sensors in an arrangement that captures bending and twisting along with an inertia measurement unit into a sports sleeve. This paper documents a comprehensive overview of the sensor design, fabrication process, seamless interconnection method, and detachable hardware implementation that allows for reconfiguring the processing unit to other body parts. A user study with ten participants demonstrated that the system could classify six different fundamental arm gestures with over 90% accuracy.
AU - Li,Y
AU - Zhou,Y
AU - Shen,C
AU - Stewart,R
DO - 10.1145/3623509.3633374
EP - 10
PB - ACM
PY - 2024///
SP - 1
TI - E-textile sleeve with graphene strain sensors for arm gesture classification of mid-air interactions
UR - http://dx.doi.org/10.1145/3623509.3633374
UR - http://0.0.0.26/
UR - http://hdl.handle.net/10044/1/109847
ER -
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