Inferring User Intents from Motion in Hearing Healthcare

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2018Researchpeer-review

Standard

Inferring User Intents from Motion in Hearing Healthcare. / Johansen, Benjamin; Korzepa, Maciej Jan; Petersen, Michael Kai; Pontoppidan, Niels Henrik; Larsen, Jakob Eg.

Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery, 2018. p. 670-675 .

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2018Researchpeer-review

Harvard

Johansen, B, Korzepa, MJ, Petersen, MK, Pontoppidan, NH & Larsen, JE 2018, Inferring User Intents from Motion in Hearing Healthcare. in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery, pp. 670-675 , 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore, Singapore, 08/10/2018. https://doi.org/10.1145/3267305.3267683

APA

Johansen, B., Korzepa, M. J., Petersen, M. K., Pontoppidan, N. H., & Larsen, J. E. (2018). Inferring User Intents from Motion in Hearing Healthcare. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 670-675 ). Association for Computing Machinery. https://doi.org/10.1145/3267305.3267683

CBE

Johansen B, Korzepa MJ, Petersen MK, Pontoppidan NH, Larsen JE. 2018. Inferring User Intents from Motion in Hearing Healthcare. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery. pp. 670-675 . https://doi.org/10.1145/3267305.3267683

MLA

Johansen, Benjamin et al. "Inferring User Intents from Motion in Hearing Healthcare". Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery. 2018, 670-675 . https://doi.org/10.1145/3267305.3267683

Vancouver

Johansen B, Korzepa MJ, Petersen MK, Pontoppidan NH, Larsen JE. Inferring User Intents from Motion in Hearing Healthcare. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery. 2018. p. 670-675 https://doi.org/10.1145/3267305.3267683

Author

Johansen, Benjamin ; Korzepa, Maciej Jan ; Petersen, Michael Kai ; Pontoppidan, Niels Henrik ; Larsen, Jakob Eg. / Inferring User Intents from Motion in Hearing Healthcare. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery, 2018. pp. 670-675

Bibtex

@inproceedings{6a7963ae5752474c8a08c14c7e262492,
title = "Inferring User Intents from Motion in Hearing Healthcare",
abstract = "Sensors in our phones and wearables, leave digital traces of our activities. With active user participation, these devices serve as personal sensing devices, giving insights to human behavior, thoughts, intents and personalities. We discuss how acoustical environment data from hearing aids, coupled with motion and location data from smartphones, may provide new insights to physical and mental health. We outline an approach to model soundscape and context data to learn preferences for personalized hearing healthcare. Using Bayesian statistical inference we investigate how physical motion and acoustical features may interact to capture behavioral patterns. Finally, we discuss how such insights may offer a foundation for designing new types of participatory healthcare solutions, as preventive measures against cognitive decline, and physical health.",
keywords = "Hearing impairment, User behavior, Health, Aging, Augmented audio, Activity, Motion, Mental health",
author = "Benjamin Johansen and Korzepa, {Maciej Jan} and Petersen, {Michael Kai} and Pontoppidan, {Niels Henrik} and Larsen, {Jakob Eg}",
year = "2018",
doi = "10.1145/3267305.3267683",
language = "English",
isbn = "978-1-4503-5966-5",
pages = "670--675",
booktitle = "Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers",
publisher = "Association for Computing Machinery",

}

RIS

TY - GEN

T1 - Inferring User Intents from Motion in Hearing Healthcare

AU - Johansen, Benjamin

AU - Korzepa, Maciej Jan

AU - Petersen, Michael Kai

AU - Pontoppidan, Niels Henrik

AU - Larsen, Jakob Eg

PY - 2018

Y1 - 2018

N2 - Sensors in our phones and wearables, leave digital traces of our activities. With active user participation, these devices serve as personal sensing devices, giving insights to human behavior, thoughts, intents and personalities. We discuss how acoustical environment data from hearing aids, coupled with motion and location data from smartphones, may provide new insights to physical and mental health. We outline an approach to model soundscape and context data to learn preferences for personalized hearing healthcare. Using Bayesian statistical inference we investigate how physical motion and acoustical features may interact to capture behavioral patterns. Finally, we discuss how such insights may offer a foundation for designing new types of participatory healthcare solutions, as preventive measures against cognitive decline, and physical health.

AB - Sensors in our phones and wearables, leave digital traces of our activities. With active user participation, these devices serve as personal sensing devices, giving insights to human behavior, thoughts, intents and personalities. We discuss how acoustical environment data from hearing aids, coupled with motion and location data from smartphones, may provide new insights to physical and mental health. We outline an approach to model soundscape and context data to learn preferences for personalized hearing healthcare. Using Bayesian statistical inference we investigate how physical motion and acoustical features may interact to capture behavioral patterns. Finally, we discuss how such insights may offer a foundation for designing new types of participatory healthcare solutions, as preventive measures against cognitive decline, and physical health.

KW - Hearing impairment

KW - User behavior

KW - Health

KW - Aging

KW - Augmented audio

KW - Activity

KW - Motion

KW - Mental health

U2 - 10.1145/3267305.3267683

DO - 10.1145/3267305.3267683

M3 - Article in proceedings

SN - 978-1-4503-5966-5

SP - 670

EP - 675

BT - Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

PB - Association for Computing Machinery

ER -