TY - GEN
T1 - OmniVib
T2 - 33rd Annual CHI Conference on Human Factors in Computing Systems, CHI 2015
AU - Alvina, Jessalyn
AU - Perrault, Simon T.
AU - Roumen, Thijs
AU - Zhao, Shengdong
AU - Azh, Maryam
AU - Fjeld, Morten
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2015/4/18
Y1 - 2015/4/18
N2 - Previous research has shown that one's palm can reliably recognize 10 or more spatiotemporal vibrotactile patterns. However, recognition of the same patterns on other body parts is unknown. In this paper, we investigate how users perceive spatiotemporal vibrotactile patterns on the arm, palm, thigh, and waist. Results of the first two experiments indicate that precise recognition of either position or orientation is difficult across multiple body parts. Nonetheless, users were able to distinguish whether two vibration pulses were from the same location when played in quick succession. Based on this finding, we designed eight spatiotemporal vibrotactile patterns and evaluated them in two additional experiments. The results demonstrate that these patterns can be reliably recognized (>80%) across the four tested body parts, both in the lab and in a more realistic context. © Copyright 2015 ACM.
AB - Previous research has shown that one's palm can reliably recognize 10 or more spatiotemporal vibrotactile patterns. However, recognition of the same patterns on other body parts is unknown. In this paper, we investigate how users perceive spatiotemporal vibrotactile patterns on the arm, palm, thigh, and waist. Results of the first two experiments indicate that precise recognition of either position or orientation is difficult across multiple body parts. Nonetheless, users were able to distinguish whether two vibration pulses were from the same location when played in quick succession. Based on this finding, we designed eight spatiotemporal vibrotactile patterns and evaluated them in two additional experiments. The results demonstrate that these patterns can be reliably recognized (>80%) across the four tested body parts, both in the lab and in a more realistic context. © Copyright 2015 ACM.
KW - Arm
KW - Mobile device
KW - Notification
KW - Palm
KW - Spatiotemporal vibrotactile pattern
KW - Tactile feedback
KW - Thigh
KW - Waist
UR - http://www.scopus.com/inward/record.url?scp=84951028014&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84951028014&origin=recordpage
U2 - 10.1145/2702123.2702341
DO - 10.1145/2702123.2702341
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450331456
VL - 2015-April
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 2487
EP - 2496
BT - CHI 2015 - Proceedings of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings
PB - Association for Computing Machinery
Y2 - 18 April 2015 through 23 April 2015
ER -