TY - JOUR
T1 - Eliciting Contact-Based and Contactless Gestures with Radar-Based Sensors
AU - Magrofuoco, Nathan
AU - Perez-Medina, Jorge Luis
AU - Roselli, Paolo
AU - Vanderdonckt, Jean
AU - Villarreal, Santiago
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - Radar sensing technologies now offer new opportunities for gesturally interacting with a smart environment by capturing microgestures via a chip that is embedded in a wearable device, such as a smartwatch, a finger or a ring. Such microgestures are issued at a very small distance from the device, regardless of whether they are contact-based, such as on the skin, or contactless. As this category of microgestures remains largely unexplored, this paper reports the results of a gesture elicitation study that was conducted with twenty-five participants who expressed their preferred user-defined gestures for interacting with a radar-based sensor on nineteen referents that represented frequent Internet-of-things tasks. This study clustered the $25\times 19=475$ initially elicited gestures into four categories of microgestures, namely, micro, motion, combined, and hybrid, and thirty-one classes of distinct gesture types and produced a consensus set of the nineteen most preferred microgestures. In a confirmatory study, twenty new participants selected gestures from this classification for thirty referents that represented tasks of various orders; they reached a high rate of agreement and did not identify any new gestures. This classification of radar-based gestures provides researchers and practitioners with a larger basis for exploring gestural interactions with radar-based sensors, such as for hand gesture recognition.
AB - Radar sensing technologies now offer new opportunities for gesturally interacting with a smart environment by capturing microgestures via a chip that is embedded in a wearable device, such as a smartwatch, a finger or a ring. Such microgestures are issued at a very small distance from the device, regardless of whether they are contact-based, such as on the skin, or contactless. As this category of microgestures remains largely unexplored, this paper reports the results of a gesture elicitation study that was conducted with twenty-five participants who expressed their preferred user-defined gestures for interacting with a radar-based sensor on nineteen referents that represented frequent Internet-of-things tasks. This study clustered the $25\times 19=475$ initially elicited gestures into four categories of microgestures, namely, micro, motion, combined, and hybrid, and thirty-one classes of distinct gesture types and produced a consensus set of the nineteen most preferred microgestures. In a confirmatory study, twenty new participants selected gestures from this classification for thirty referents that represented tasks of various orders; they reached a high rate of agreement and did not identify any new gestures. This classification of radar-based gestures provides researchers and practitioners with a larger basis for exploring gestural interactions with radar-based sensors, such as for hand gesture recognition.
KW - Contact-based gesture
KW - contactless gesture
KW - gestural interaction
KW - gesture classification
KW - gesture elicitation study
KW - microgesture
KW - radar sensing
UR - http://www.scopus.com/inward/record.url?scp=85076986077&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2951349
DO - 10.1109/ACCESS.2019.2951349
M3 - Article
AN - SCOPUS:85076986077
SN - 2169-3536
VL - 7
SP - 176982
EP - 176997
JO - IEEE Access
JF - IEEE Access
M1 - 8890919
ER -