Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine

Hicham Rifai; Paula Kelly; Yoshiki Shoji; Damon Berry; Matteo Zallio

doi:10.1007/978-3-319-73888-8_24

Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine

Hicham Rifai
, Paula Kelly
, Yoshiki Shoji
, Damon Berry
, Matteo Zallio

School of Electrical and Electronic Engineering

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

Abstract

Information technology is increasingly becoming an integral part of contemporary life. Most tasks that are performed over the course of a day, involve the use of different types of connected devices. About two billion contemporary consumers use smartphones [1]. These smartphones contain a variety of sensors that can collect information about their users such as their mobility patterns, daily activities and occupancy patterns [2]. Occupancy is an important aspect in developing responsive environments and for optimizing building performance. This work investigates the extent to which smartphones can be used to collect occupancy data in a work environment, compared to another method that uses smart power outlets for collecting occupancy data. The resultant data sets are validated against register entries, which are recorded manually by participants each time they change their occupancy state.

Original language	English
Title of host publication	Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018
Subtitle of host publication	Integrating People and Intelligent Systems
Editors	Waldemar Karwowski, Tareq Ahram
Publisher	Springer Verlag
Pages	143-148
Number of pages	6
ISBN (Print)	9783319738871
DOIs	https://doi.org/10.1007/978-3-319-73888-8_24
Publication status	Published - 2018
Event	1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018 - Dubai, United Arab Emirates Duration: 7 Jan 2018 → 9 Jan 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	722
ISSN (Print)	2194-5357

Conference

Conference	1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018
Country/Territory	United Arab Emirates
City	Dubai
Period	7/01/18 → 9/01/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Activity recognition
Assistive technology
Bluetooth
Energy consumption detection
Human factors
Human-systems integration
Privacy
User-centered design

Access to Document

10.1007/978-3-319-73888-8_24

https://arrow.tudublin.ie/ehsiart/13Licence: CC BY-NC-SA

Cite this

Rifai, H., Kelly, P., Shoji, Y., Berry, D., & Zallio, M. (2018). Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine. In W. Karwowski, & T. Ahram (Eds.), Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems (pp. 143-148). (Advances in Intelligent Systems and Computing; Vol. 722). Springer Verlag. https://doi.org/10.1007/978-3-319-73888-8_24

Rifai, Hicham ; Kelly, Paula ; Shoji, Yoshiki et al. / Human activity detection patterns : A pilot study for unobtrusive discovery of daily working routine. Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. editor / Waldemar Karwowski ; Tareq Ahram. Springer Verlag, 2018. pp. 143-148 (Advances in Intelligent Systems and Computing).

@inproceedings{350017d26dd943d1b3e88040e6b7190a,

title = "Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine",

abstract = "Information technology is increasingly becoming an integral part of contemporary life. Most tasks that are performed over the course of a day, involve the use of different types of connected devices. About two billion contemporary consumers use smartphones [1]. These smartphones contain a variety of sensors that can collect information about their users such as their mobility patterns, daily activities and occupancy patterns [2]. Occupancy is an important aspect in developing responsive environments and for optimizing building performance. This work investigates the extent to which smartphones can be used to collect occupancy data in a work environment, compared to another method that uses smart power outlets for collecting occupancy data. The resultant data sets are validated against register entries, which are recorded manually by participants each time they change their occupancy state.",

keywords = "Activity recognition, Assistive technology, Bluetooth, Energy consumption detection, Human factors, Human-systems integration, Privacy, User-centered design",

author = "Hicham Rifai and Paula Kelly and Yoshiki Shoji and Damon Berry and Matteo Zallio",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018 ; Conference date: 07-01-2018 Through 09-01-2018",

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doi = "10.1007/978-3-319-73888-8\_24",

language = "English",

isbn = "9783319738871",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

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editor = "Waldemar Karwowski and Tareq Ahram",

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}

Rifai, H, Kelly, P, Shoji, Y, Berry, D & Zallio, M 2018, Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine. in W Karwowski & T Ahram (eds), Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 722, Springer Verlag, pp. 143-148, 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018, Dubai, United Arab Emirates, 7/01/18. https://doi.org/10.1007/978-3-319-73888-8_24

Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine. / Rifai, Hicham; Kelly, Paula; Shoji, Yoshiki et al.
Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. ed. / Waldemar Karwowski; Tareq Ahram. Springer Verlag, 2018. p. 143-148 (Advances in Intelligent Systems and Computing; Vol. 722).

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

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T2 - 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018

AU - Rifai, Hicham

AU - Kelly, Paula

AU - Shoji, Yoshiki

AU - Berry, Damon

AU - Zallio, Matteo

N1 - Publisher Copyright: © Springer International Publishing AG 2018.

PY - 2018

Y1 - 2018

N2 - Information technology is increasingly becoming an integral part of contemporary life. Most tasks that are performed over the course of a day, involve the use of different types of connected devices. About two billion contemporary consumers use smartphones [1]. These smartphones contain a variety of sensors that can collect information about their users such as their mobility patterns, daily activities and occupancy patterns [2]. Occupancy is an important aspect in developing responsive environments and for optimizing building performance. This work investigates the extent to which smartphones can be used to collect occupancy data in a work environment, compared to another method that uses smart power outlets for collecting occupancy data. The resultant data sets are validated against register entries, which are recorded manually by participants each time they change their occupancy state.

AB - Information technology is increasingly becoming an integral part of contemporary life. Most tasks that are performed over the course of a day, involve the use of different types of connected devices. About two billion contemporary consumers use smartphones [1]. These smartphones contain a variety of sensors that can collect information about their users such as their mobility patterns, daily activities and occupancy patterns [2]. Occupancy is an important aspect in developing responsive environments and for optimizing building performance. This work investigates the extent to which smartphones can be used to collect occupancy data in a work environment, compared to another method that uses smart power outlets for collecting occupancy data. The resultant data sets are validated against register entries, which are recorded manually by participants each time they change their occupancy state.

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KW - Assistive technology

KW - Bluetooth

KW - Energy consumption detection

KW - Human factors

KW - Human-systems integration

KW - Privacy

KW - User-centered design

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M3 - Conference contribution

AN - SCOPUS:85040236139

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T3 - Advances in Intelligent Systems and Computing

SP - 143

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BT - Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018

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PB - Springer Verlag

Y2 - 7 January 2018 through 9 January 2018

ER -

Rifai H, Kelly P, Shoji Y, Berry D, Zallio M. Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine. In Karwowski W, Ahram T, editors, Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Springer Verlag. 2018. p. 143-148. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-73888-8_24

Human activity detection patterns: A pilot study for unobtrusive discovery of daily working routine

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this