ITU

An Affect Detection Technique Using Mobile Commodity Sensors in the Wild

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Standard

An Affect Detection Technique Using Mobile Commodity Sensors in the Wild. / Mottelson, Aske; Hornbæk, Kasper.

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA : Association for Computing Machinery, 2016. p. 781–792 (UbiComp '16).

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Mottelson, A & Hornbæk, K 2016, An Affect Detection Technique Using Mobile Commodity Sensors in the Wild. in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, New York, NY, USA, UbiComp '16, pp. 781–792. https://doi.org/10.1145/2971648.2971654

APA

Mottelson, A., & Hornbæk, K. (2016). An Affect Detection Technique Using Mobile Commodity Sensors in the Wild. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 781–792). Association for Computing Machinery. UbiComp '16 https://doi.org/10.1145/2971648.2971654

Vancouver

Mottelson A, Hornbæk K. An Affect Detection Technique Using Mobile Commodity Sensors in the Wild. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: Association for Computing Machinery. 2016. p. 781–792. (UbiComp '16). https://doi.org/10.1145/2971648.2971654

Author

Mottelson, Aske ; Hornbæk, Kasper. / An Affect Detection Technique Using Mobile Commodity Sensors in the Wild. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA : Association for Computing Machinery, 2016. pp. 781–792 (UbiComp '16).

Bibtex

@inproceedings{90a2fc3de8bf4531a47181bd117857ca,
title = "An Affect Detection Technique Using Mobile Commodity Sensors in the Wild",
abstract = "Current techniques to computationally detect human affect often depend on specialized hardware, work only in laboratory settings, or require substantial individual training. We use sensors in commodity smartphones to estimate affect in the wild with no training time based on a link between affect and movement. The first experiment had 55 participants do touch interactions after exposure to positive or neutral emotion-eliciting films; negative affect resulted in faster but less precise interactions, in addition to differences in rotation and acceleration. Using off-the-shelf machine learning algorithms we report 89.1% accuracy in binary affective classification, grouping participants by their self-assessments. A follow up experiment validated findings from the first experiment; the experiment collected naturally occurring affect of 127 participants, who again did touch interactions. Results demonstrate that affect has direct behavioral effect on mobile interaction and that affect detection using common smartphone sensors is feasible.",
keywords = "affect detection, smartphone, touch, crowdsourcing, affective computing",
author = "Aske Mottelson and Kasper Hornb{\ae}k",
year = "2016",
doi = "10.1145/2971648.2971654",
language = "Udefineret/Ukendt",
isbn = "9781450344616",
series = "UbiComp '16",
pages = "781–792",
booktitle = "Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
publisher = "Association for Computing Machinery",
address = "USA",

}

RIS

TY - GEN

T1 - An Affect Detection Technique Using Mobile Commodity Sensors in the Wild

AU - Mottelson, Aske

AU - Hornbæk, Kasper

PY - 2016

Y1 - 2016

N2 - Current techniques to computationally detect human affect often depend on specialized hardware, work only in laboratory settings, or require substantial individual training. We use sensors in commodity smartphones to estimate affect in the wild with no training time based on a link between affect and movement. The first experiment had 55 participants do touch interactions after exposure to positive or neutral emotion-eliciting films; negative affect resulted in faster but less precise interactions, in addition to differences in rotation and acceleration. Using off-the-shelf machine learning algorithms we report 89.1% accuracy in binary affective classification, grouping participants by their self-assessments. A follow up experiment validated findings from the first experiment; the experiment collected naturally occurring affect of 127 participants, who again did touch interactions. Results demonstrate that affect has direct behavioral effect on mobile interaction and that affect detection using common smartphone sensors is feasible.

AB - Current techniques to computationally detect human affect often depend on specialized hardware, work only in laboratory settings, or require substantial individual training. We use sensors in commodity smartphones to estimate affect in the wild with no training time based on a link between affect and movement. The first experiment had 55 participants do touch interactions after exposure to positive or neutral emotion-eliciting films; negative affect resulted in faster but less precise interactions, in addition to differences in rotation and acceleration. Using off-the-shelf machine learning algorithms we report 89.1% accuracy in binary affective classification, grouping participants by their self-assessments. A follow up experiment validated findings from the first experiment; the experiment collected naturally occurring affect of 127 participants, who again did touch interactions. Results demonstrate that affect has direct behavioral effect on mobile interaction and that affect detection using common smartphone sensors is feasible.

KW - affect detection

KW - smartphone

KW - touch

KW - crowdsourcing

KW - affective computing

U2 - 10.1145/2971648.2971654

DO - 10.1145/2971648.2971654

M3 - Konferencebidrag i proceedings

SN - 9781450344616

T3 - UbiComp '16

SP - 781

EP - 792

BT - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

PB - Association for Computing Machinery

CY - New York, NY, USA

ER -

ID: 86133753