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Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics. IMM, Karolinska Institutet.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).ORCID iD: 0000-0002-3256-9029
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics. IMM, Karolinska Institutet.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology.ORCID iD: 0000-0003-4853-7731
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2019 (English)In: Ergonomics, ISSN 0014-0139, E-ISSN 1366-5847Article in journal (Other academic) Accepted
Abstract [en]

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO2), which is commonly assessed by heart rate (HR) in field studies. However, the VO2–HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of −3.94 and 2.00 mL/min/kg, while the HR-Flex model had −5.01 and 5.36 mL/min/kg and the branched model, −6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Heart rate; work metabolism; motion sensing; wearable sensors; risk assessment; estimation models
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-239148DOI: 10.1080/00140139.2019.1566579ISI: 000468779800007Scopus ID: 2-s2.0-85062366366OAI: oai:DiVA.org:kth-239148DiVA, id: diva2:1263825
Funder
AFA Insurance, 150039
Note

QC 20190218

Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2019-06-11Bibliographically approved
In thesis
1. Wearable Solutions for P-Health at Work: Precise, Pervasive and Preventive
Open this publication in new window or tab >>Wearable Solutions for P-Health at Work: Precise, Pervasive and Preventive
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With a demographic change towards an older population, the structure of the labor force is shifting, and people are expected to work longer within their extended life span. However, for many people, wellbeing has been compromised by work-related problems before they reach the retirement age. Prevention of chronic diseases such as cardiovascular diseases and musculoskeletal disorders is needed to provide a sustainable working life. Therefore, pervasive tools for risk assessment and intervention are needed. The vision is to use wearable technologies to promote a sustainable work life, to be more detailed, to develop a system that integrates wearable technologies into workwear to provide pervasive and precise occupational disease prevention. This thesis presents some efforts towards this vision, including system-level design for a wearable risk assessment and intervention system, as well as specific insight into solutions for in-field assessment of physical workload and technologies to make smart sensing garments. The overall system is capable of providing unobtrusive monitoring of several signs, automatically estimating risk levels and giving feedback and reports to different stakeholders. The performance and usability of current energy expenditure estimation methods based on heart rate monitors and accelerometers were examined in occupational scenarios. The usefulness of impedance pneumography-based respiration monitoring for energy expenditure estimation was explored. A method that integrates heart rate, respiration and motion information using a neuronal network for enhancing the estimation is shown. The sensing garment is an essential component of the wearable system. Smart textile solutions that improve the performance, usability and manufacturability of sensing garments, including solutions for wiring and textile-electronics interconnection as well as an overall garment design that utilizes different technologies, are demonstrated.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 42
Series
TRITA-CBH-FOU ; 2018-59
Keywords
wearable technology, occupational health, energy expenditure, smart textile
National Category
Medical Engineering
Research subject
Applied Medical Technology
Identifiers
urn:nbn:se:kth:diva-239156 (URN)978-91-7873-042-1 (ISBN)
Public defence
2018-12-10, Sal T2, Hälsovägen 11, Flemingsberg, 10:00 (English)
Opponent
Supervisors
Note

QC 20181119

Available from: 2018-11-19 Created: 2018-11-16 Last updated: 2018-11-19Bibliographically approved

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Yang, LiyunLu, KeForsman, MikaelLindecrantz, KajSeoane, FernandoEklund, Jörgen

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