Details
| Original language | English |
|---|---|
| Title of host publication | CHI '12 |
| Subtitle of host publication | Proceedings of the SIGCHI Conference on Human Factors in Computing Systems |
| Pages | 1239-1248 |
| Number of pages | 10 |
| Publication status | Published - 5 May 2012 |
| Externally published | Yes |
| Event | 30th ACM Conference on Human Factors in Computing Systems, CHI 2012 - Austin, TX, United States Duration: 5 May 2012 → 10 May 2012 |
Abstract
When the user is engaged with a real-world task it can be inappropriate or difficult to use a smartphone. To address this concern, we developed ShoeSense, a wearable system consisting in part of a shoe-mounted depth sensor pointing upward at the wearer. ShoeSense recognizes relaxed and discreet as well as large and demonstrative hand gestures. In particular, we designed three gesture sets (Triangle, Radial, and Finger-Count) for this setup, which can be performed without visual attention. The advantages of ShoeSense are illustrated in five scenarios: (1) quickly performing frequent operations without reaching for the phone, (2) discreetly performing operations without disturbing others, (3) enhancing operations on mobile devices, (4) supporting accessibility, and (5) artistic performances. We present a proof-of-concept, wearable implementation based on a depth camera and report on a lab study comparing social acceptability, physical and mental demand, and user preference. A second study demonstrates a 94-99% recognition rate of our recognizers.
Keywords
- Gesture set, Gestures, Mobile, Sensor placement, Shoe, Wearable
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
Cite this
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CHI '12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2012. p. 1239-1248.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - ShoeSense
T2 - 30th ACM Conference on Human Factors in Computing Systems, CHI 2012
AU - Bailly, Gilles
AU - Müller, Jörg
AU - Rohs, Michael
AU - Wigdor, Daniel
AU - Kratz, Sven
N1 - Copyright: Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012/5/5
Y1 - 2012/5/5
N2 - When the user is engaged with a real-world task it can be inappropriate or difficult to use a smartphone. To address this concern, we developed ShoeSense, a wearable system consisting in part of a shoe-mounted depth sensor pointing upward at the wearer. ShoeSense recognizes relaxed and discreet as well as large and demonstrative hand gestures. In particular, we designed three gesture sets (Triangle, Radial, and Finger-Count) for this setup, which can be performed without visual attention. The advantages of ShoeSense are illustrated in five scenarios: (1) quickly performing frequent operations without reaching for the phone, (2) discreetly performing operations without disturbing others, (3) enhancing operations on mobile devices, (4) supporting accessibility, and (5) artistic performances. We present a proof-of-concept, wearable implementation based on a depth camera and report on a lab study comparing social acceptability, physical and mental demand, and user preference. A second study demonstrates a 94-99% recognition rate of our recognizers.
AB - When the user is engaged with a real-world task it can be inappropriate or difficult to use a smartphone. To address this concern, we developed ShoeSense, a wearable system consisting in part of a shoe-mounted depth sensor pointing upward at the wearer. ShoeSense recognizes relaxed and discreet as well as large and demonstrative hand gestures. In particular, we designed three gesture sets (Triangle, Radial, and Finger-Count) for this setup, which can be performed without visual attention. The advantages of ShoeSense are illustrated in five scenarios: (1) quickly performing frequent operations without reaching for the phone, (2) discreetly performing operations without disturbing others, (3) enhancing operations on mobile devices, (4) supporting accessibility, and (5) artistic performances. We present a proof-of-concept, wearable implementation based on a depth camera and report on a lab study comparing social acceptability, physical and mental demand, and user preference. A second study demonstrates a 94-99% recognition rate of our recognizers.
KW - Gesture set
KW - Gestures
KW - Mobile
KW - Sensor placement
KW - Shoe
KW - Wearable
UR - http://www.scopus.com/inward/record.url?scp=84862094172&partnerID=8YFLogxK
U2 - 10.1145/2207676.2208576
DO - 10.1145/2207676.2208576
M3 - Conference contribution
AN - SCOPUS:84862094172
SN - 9781450310154
SP - 1239
EP - 1248
BT - CHI '12
Y2 - 5 May 2012 through 10 May 2012
ER -