Cognitive aid: task assistance based on mental workload estimation

Qiushi Zhou; Namrata Srivastava; Jorge Goncalves; Joshua Newn; Tilman Dingler; Eduardo Velloso

doi:10.1145/3290607.3313010

Cognitive aid: task assistance based on mental workload estimation

Qiushi Zhou, Namrata Srivastava, Jorge Goncalves, Joshua Newn, Tilman Dingler, Eduardo Velloso

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

5 Citations (Scopus)

Abstract

In this work, we evaluate the potential of using wearable non-contact (infrared) thermal sensors through a user study (N=12) to measure mental workload. Our results indicate the possibility of mental workload estimation through the temperature changes detected using the prototype as participants perform two task variants with increasing difficulty levels. While the sensor accuracy and the design of the prototype can be further improved, the prototype showed the potential of building AR-based systems with cognitive aid technology for ubiquitous task assistance from the changes in mental workload demands. As such, we demonstrate our next steps by integrating our prototype into an existing AR headset (i.e. Microsoft HoloLens).

Original language	English
Title of host publication	Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
Editors	Anna Cox, Vassilis Kostakos
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Number of pages	6
ISBN (Electronic)	9781450359719
DOIs	https://doi.org/10.1145/3290607.3313010
Publication status	Published - 2019
Externally published	Yes
Event	International Conference on Human Factors in Computing Systems 2019 - Glasgow, United Kingdom Duration: 4 May 2019 → 9 May 2019 Conference number: 37th https://chi2019.acm.org (Website) https://dl.acm.org/doi/proceedings/10.1145/3290605 (Proceedings)

Conference

Conference	International Conference on Human Factors in Computing Systems 2019
Abbreviated title	CHI 2019
Country/Territory	United Kingdom
City	Glasgow
Period	4/05/19 → 9/05/19
Internet address	https://chi2019.acm.org (Website) https://dl.acm.org/doi/proceedings/10.1145/3290605 (Proceedings)

Keywords

Affective computing
Cognitive load
Thermal sensor

Access to Document

10.1145/3290607.3313010

Cite this

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title = "Cognitive aid: task assistance based on mental workload estimation",

abstract = "In this work, we evaluate the potential of using wearable non-contact (infrared) thermal sensors through a user study (N=12) to measure mental workload. Our results indicate the possibility of mental workload estimation through the temperature changes detected using the prototype as participants perform two task variants with increasing difficulty levels. While the sensor accuracy and the design of the prototype can be further improved, the prototype showed the potential of building AR-based systems with cognitive aid technology for ubiquitous task assistance from the changes in mental workload demands. As such, we demonstrate our next steps by integrating our prototype into an existing AR headset (i.e. Microsoft HoloLens).",

keywords = "Affective computing, Cognitive load, Thermal sensor",

author = "Qiushi Zhou and Namrata Srivastava and Jorge Goncalves and Joshua Newn and Tilman Dingler and Eduardo Velloso",

year = "2019",

doi = "10.1145/3290607.3313010",

language = "English",

editor = "Anna Cox and Vassilis Kostakos",

booktitle = "Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "International Conference on Human Factors in Computing Systems 2019, CHI 2019 ; Conference date: 04-05-2019 Through 09-05-2019",

url = "https://chi2019.acm.org, https://dl.acm.org/doi/proceedings/10.1145/3290605",

}

Zhou, Q, Srivastava, N, Goncalves, J, Newn, J, Dingler, T & Velloso, E 2019, Cognitive aid: task assistance based on mental workload estimation. in A Cox & V Kostakos (eds), Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems., 3313010, Association for Computing Machinery (ACM), New York NY USA, International Conference on Human Factors in Computing Systems 2019, Glasgow, United Kingdom, 4/05/19. https://doi.org/10.1145/3290607.3313010

Cognitive aid: task assistance based on mental workload estimation. / Zhou, Qiushi; Srivastava, Namrata; Goncalves, Jorge et al.
Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. ed. / Anna Cox; Vassilis Kostakos. New York NY USA: Association for Computing Machinery (ACM), 2019. 3313010.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review

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T1 - Cognitive aid

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AU - Zhou, Qiushi

AU - Srivastava, Namrata

AU - Goncalves, Jorge

AU - Newn, Joshua

AU - Dingler, Tilman

AU - Velloso, Eduardo

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N2 - In this work, we evaluate the potential of using wearable non-contact (infrared) thermal sensors through a user study (N=12) to measure mental workload. Our results indicate the possibility of mental workload estimation through the temperature changes detected using the prototype as participants perform two task variants with increasing difficulty levels. While the sensor accuracy and the design of the prototype can be further improved, the prototype showed the potential of building AR-based systems with cognitive aid technology for ubiquitous task assistance from the changes in mental workload demands. As such, we demonstrate our next steps by integrating our prototype into an existing AR headset (i.e. Microsoft HoloLens).

AB - In this work, we evaluate the potential of using wearable non-contact (infrared) thermal sensors through a user study (N=12) to measure mental workload. Our results indicate the possibility of mental workload estimation through the temperature changes detected using the prototype as participants perform two task variants with increasing difficulty levels. While the sensor accuracy and the design of the prototype can be further improved, the prototype showed the potential of building AR-based systems with cognitive aid technology for ubiquitous task assistance from the changes in mental workload demands. As such, we demonstrate our next steps by integrating our prototype into an existing AR headset (i.e. Microsoft HoloLens).

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Cognitive aid: task assistance based on mental workload estimation

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Conference

Keywords

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