A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor

Knowing the perceived exertion of workers during their physical activities facilitates the decision-making of supervisors regarding the worker allocation in the appropriate job, actions to prevent accidents, and reassignment of tasks, among others. However, although wearable heart rate sensors repre...

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Veröffentlicht in:Mobile information systems 2018-01, Vol.2018 (2018), p.1-17
Hauptverfasser: Pancardo, Pablo, Acosta-Escalante, Francisco, Hernández-Nolasco, J. A.
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container_end_page 17
container_issue 2018
container_start_page 1
container_title Mobile information systems
container_volume 2018
creator Pancardo, Pablo
Acosta-Escalante, Francisco
Hernández-Nolasco, J. A.
description Knowing the perceived exertion of workers during their physical activities facilitates the decision-making of supervisors regarding the worker allocation in the appropriate job, actions to prevent accidents, and reassignment of tasks, among others. However, although wearable heart rate sensors represent an effective way to capture perceived exertion, ergonomic methods are generic and they do not consider the diffuse nature of the ranges that classify the efforts. Personalized monitoring is needed to enable a real and efficient classification of perceived individual efforts. In this paper, we propose a heart rate-based personalized method to assess perceived exertion; our method uses fuzzy logic as an option to manage imprecision and uncertainty in involved variables. We applied some experiments to cleaning staff and obtained results that highlight the importance of a custom method to classify perceived exertion of people doing physical work.
doi_str_mv 10.1155/2018/4216172
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source Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection
subjects Classification
Decision making
Fuzzy logic
Heart rate
Physical work
Supervisors
Wearable technology
Workplaces
title A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor
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