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HealthyOffice: mood recognition at work using smartphones and wearable sensors

HealthyOffice: mood recognition at work using smartphones and wearable sensors
HealthyOffice: mood recognition at work using smartphones and wearable sensors
Stress, anxiety and depression in the workplace are detrimental to human health and productivity with significant financial implications. Recent research in this area has focused on the use of sensor technologies, including smartphones and wearables embedded with physiological and movement sensors. In this work, we explore the possibility of using such devices for mood recognition, focusing on work environments. We propose a novel mood recognition framework that is able to identify five intensity levels for eight different types of moods every two hours. We further present a smartphone app (‘HealthyOffice’), designed to facilitate self-reporting in a structured manner and provide our model with the ground truth. We evaluate our system in a small-scale user study where wearable sensing data is collected in an office environment. Our experiments exhibit promising results allowing us to reliably recognize various classes of perceived moods.
IEEE
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Khan, Aftab
b21653e0-3b0b-43b7-bb2f-71c0f7c104e6
Kalogridis, Georgios
39c90935-a9d1-4d0a-b6b2-9b41f1689a37
Vatsikas, Stefanos
5abd7377-7678-4881-a50a-1667688cd5fe
Lewis, Tim
defb0930-783d-4dc7-8a41-7113fec8e0b1
Sooriyabandara, Mahesh
a41159e2-75c5-4e76-9683-d66a8d840efc
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Khan, Aftab
b21653e0-3b0b-43b7-bb2f-71c0f7c104e6
Kalogridis, Georgios
39c90935-a9d1-4d0a-b6b2-9b41f1689a37
Vatsikas, Stefanos
5abd7377-7678-4881-a50a-1667688cd5fe
Lewis, Tim
defb0930-783d-4dc7-8a41-7113fec8e0b1
Sooriyabandara, Mahesh
a41159e2-75c5-4e76-9683-d66a8d840efc

Zenonos, Alexandros, Khan, Aftab, Kalogridis, Georgios, Vatsikas, Stefanos, Lewis, Tim and Sooriyabandara, Mahesh (2016) HealthyOffice: mood recognition at work using smartphones and wearable sensors. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE. 6 pp . (doi:10.1109/PERCOMW.2016.7457166).

Record type: Conference or Workshop Item (Paper)

Abstract

Stress, anxiety and depression in the workplace are detrimental to human health and productivity with significant financial implications. Recent research in this area has focused on the use of sensor technologies, including smartphones and wearables embedded with physiological and movement sensors. In this work, we explore the possibility of using such devices for mood recognition, focusing on work environments. We propose a novel mood recognition framework that is able to identify five intensity levels for eight different types of moods every two hours. We further present a smartphone app (‘HealthyOffice’), designed to facilitate self-reporting in a structured manner and provide our model with the ground truth. We evaluate our system in a small-scale user study where wearable sensing data is collected in an office environment. Our experiments exhibit promising results allowing us to reliably recognize various classes of perceived moods.

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More information

e-pub ahead of print date: 21 April 2016
Published date: 21 April 2016
Venue - Dates: The Second IEEE International Workshop on Sensing Systems and Applications Using Wrist Worn Smart Devices, 2016, Sydney, Australia, 2016-03-14 - 2016-03-18
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 408403
URI: http://eprints.soton.ac.uk/id/eprint/408403
PURE UUID: 3bb2cd89-c342-4be3-9412-ed3d0be2843c
ORCID for Alexandros Zenonos: ORCID iD orcid.org/0000-0003-4694-1642

Catalogue record

Date deposited: 20 May 2017 04:02
Last modified: 16 Dec 2019 18:20

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