TY - GEN
T1 - SmartSleeve
T2 - 30th Annual ACM Symposium on User Interface Software and Technology, UIST 2017
AU - Parzer, Patrick
AU - Sharma, Adwait
AU - Vogl, Anita
AU - Steimle, Jürgen
AU - Olwal, Alex
AU - Haller, Michael
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/20
Y1 - 2017/10/20
N2 - Over the last decades, there have been numerous efforts in wearable computing research to enable interactive textiles. Most work focus, however, on integrating sensors for planar touch gestures, and thus do not fully take advantage of the flexible, deformable and tangible material properties of textile. In this work, we introduce SmartSleeve, a deformable textile sensor, which can sense both surface and deformation gestures in real-time. It expands the gesture vocabulary with a range of expressive interaction techniques, and we explore new opportunities using advanced deformation gestures, such as, Twirl, Twist, Fold, Push and Stretch. We describe our sensor design, hardware implementation and its novel non-rigid connector architecture. We provide a detailed description of our hybrid gesture detection pipeline that uses learning-based algorithms and heuristics to enable real-time gesture detection and tracking. Its modular architecture allows us to derive new gestures through the combination with continuous properties like pressure, location, and direction. Finally, we report on the promising results from our evaluations which demonstrate real-time classification.
AB - Over the last decades, there have been numerous efforts in wearable computing research to enable interactive textiles. Most work focus, however, on integrating sensors for planar touch gestures, and thus do not fully take advantage of the flexible, deformable and tangible material properties of textile. In this work, we introduce SmartSleeve, a deformable textile sensor, which can sense both surface and deformation gestures in real-time. It expands the gesture vocabulary with a range of expressive interaction techniques, and we explore new opportunities using advanced deformation gestures, such as, Twirl, Twist, Fold, Push and Stretch. We describe our sensor design, hardware implementation and its novel non-rigid connector architecture. We provide a detailed description of our hybrid gesture detection pipeline that uses learning-based algorithms and heuristics to enable real-time gesture detection and tracking. Its modular architecture allows us to derive new gestures through the combination with continuous properties like pressure, location, and direction. Finally, we report on the promising results from our evaluations which demonstrate real-time classification.
KW - Deformation gestures
KW - Smart textile
KW - Surface gestures
UR - http://www.scopus.com/inward/record.url?scp=85038845813&partnerID=8YFLogxK
U2 - 10.1145/3126594.3126652
DO - 10.1145/3126594.3126652
M3 - Conference contribution
T3 - UIST 2017 - Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology
SP - 565
EP - 577
BT - UIST 2017 - Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology
PB - Association for Computing Machinery, Inc
Y2 - 22 October 2017 through 25 October 2017
ER -