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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), Biomedical Engineering and Health Systems, Ergonomics.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.
Karolinska Institutet, Stockholm, Sweden; University of Borås, Borås, Sweden.ORCID iD: 0000-0003-4853-7731
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2019 (English)In: Ergonomics, ISSN 0014-0139, E-ISSN 1366-5847, Vol. 62, no 5, p. 694-705Article in journal (Refereed) Published
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. Vol. 62, no 5, p. 694-705
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: 000468779800007PubMedID: 30806164Scopus 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: 2019-11-12Bibliographically 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
2. Ergonomic Risk Assessment and Intervention through Smart Workwear Systems
Open this publication in new window or tab >>Ergonomic Risk Assessment and Intervention through Smart Workwear Systems
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The rapid development of wearable technology has provided opportunities to ergonomics research and practice with new ways for workload measurements, data analytics, risk assessment and intervention. This thesis aims at developing and evaluating methods using wearable technologies to assess physical risk factors at work, and further to give feedback to employees to improve their work techniques.

One smartphone application (ErgoArmMeter) was developed for the assessment of upper arm postures and movements at work. The application uses integrated signals of the embedded accelerometer and gyroscope, and processes and presents the assessment results directly after a measurement. Laboratory validation with 10 participants was performed using an optical tracking system as standard measurement. The results showed that the application had similar accuracy compared to standard inclinometry for static postures and improved accuracy in dynamic conditions. With its convenience and low cost, the application may be used by researchers and practitioners in various scenarios for risk assessment.

Three models for assessment of work metabolism (WM) using heart rate (HR) and accelerometers (ACCs) were evaluated during simulated work tasks with 12 participants against indirect calorimetry as standard measurement. The HR + arm-leg ACC model showed best accuracy in most work tasks. The HR-Flex model showed a small bias for the average of all tasks. For estimating WM in the field using wearable technologies, the HR-Flex model or the HR + arm-leg ACC model may be chosen depending on the need for accuracy level and resource availabilities. Further improvement of the classification algorithm in the HR + arm-leg ACC model is needed in order to suit various types of work.

Two smart workwear systems were developed and evaluated. Smart workwear system 1.0 consisted of a sensorized vest, an inertial measurement unit (IMU) and an Android tablet application. It assessed risks of high physiological workload and prolonged occupational sitting/standing. The results were visualized by color-coded risk levels. The system was evaluated with 8 participants from four occupations in a field study. It was perceived as useful, comfortable and not disturbing by most participants. Further development is required for the system for automated risk assessment of various ergonomic risk factors in real work situations.

Smart workwear system 2.0 consisted of an instrumented t-shirt with IMUs, vibration units and an Android smartphone application. It provided vibrotactile feedback to users’ upper arm and trunk when predefined angular thresholds were exceeded. The system was evaluated for work postures intervention in industrial order picking among 15 participants. It showed to be effective in improving the trunk and dominant upper arm postures. The system was perceived as comfortable and useful. The vibrotactile feedback was evaluated as supportive for learning regarding workplace and task design among the participants.

In conclusion, the research in this thesis showed that wearable technologies can be used both in the laboratory and field for assessment of physical risk factors at work and intervention in work technique improvement. With further research and development, smart workwear systems may contribute to automated risk assessment, prevention of work-related ill health, and improvement of the design and overall quality of work.

Abstract [sv]

Den snabba utvecklingen av bärbar teknik har skapat möjligheter för ergonomisk forskning och tillämpning genom nya sätt att mäta arbetsbelastning, dataanalys, riskbedömning och intervention. Denna avhandling syftar till att utveckla och utvärdera metoder att använda bärbar teknik för att utvärdera fysiska riskfaktorer i arbetet samt ge feedback till anställda för att förbättra sin arbetsteknik.

En smart mobilapplikation (ErgoArmMeter) utvecklades för att bedöma överarmställningar och -rörelser på jobbet. Applikationen använder integrerade signaler från den inbäddade accelerometern och gyroskopet, samt bearbetar och presenterar bedömningsresultaten direkt efter en mätning. En laboratorievalidering med 10 deltagare utfördes där ett optiskt spårningssystem användes som standardmätning. Resultaten visade att applikationen hade jämförbar noggrannhet med standard inklinometri för statiska arbetsställningar men bättre noggrannhet under dynamiska förhållanden. Applikationens enkelhet, bekvämlighet och låga kostnad gör att applikationen kan användas av forskare och praktiker i olika scenarier för ergonomisk riskbedömning.

Tre modeller för bedömning av arbetsmetabolism med hjälp av hjärtfrekvens (HR) och accelerometrar (ACCs) utvärderades i simulerade arbetsuppgifter med 12 deltagare mot indirekt kalorimetri som standardmätning. “HR + arm-leg ACC modellen” visade bästa noggrannhet i de flesta arbetsuppgifter. “HR-Flex modellen” visade en liten avvikelse för genomsnittet av alla uppgifter. För att bedöma arbetsmetabolism i arbetslivet med användning av bärbar teknik kan “HR-Flex modellen” eller “HR + arm-leg ACC modellen” väljas beroende på behovet av noggrannhet och tillgängliga resurser. Ytterligare förbättring av klassificeringsalgoritmen i ”HR + arm-leg ACC modellen” behövs för att passa olika typer av arbete.

Två system för smarta arbetskläder utvecklades och utvärderades. Smarta arbetskläder 1.0 bestod av en sensoriserad väst, en IMU-sensor (Inertial Measurement Unit) och en applikation på en Android surfplatta. Systemet bedömde riskerna för hög fysisk arbetsbelastning och långvarigt sittande/stående på arbetet. Resultaten visualiserades med färgkodade risknivåer. Systemet utvärderades med 8 deltagare från fyra yrken i en fältstudie. Det upplevdes som användbart, bekvämt och inte störande av de flesta deltagare. Vidareutveckling av systemet krävs för automatiserad riskbedömning av olika ergonomiska riskfaktorer i arbetslivet.

Smarta arbetskläder 2.0 bestod av en instrumenterad t-shirt med IMU-enheter, vibrationsenheter och en applikation på en Android smart mobil. Systemet gav vibrotaktil återkoppling till användarnas dominanta överarm och bål/rygg när fördefinierade vinkeltrösklar överskreds. Systemet utvärderades beträffande arbetsställningar i en intervention i industriell materialplockning med 15 deltagare. Det visade sig effektivt förbättra arbetsställningar av bålen/ryggen och överarmen. Systemet upplevdes som bekvämt och användbart. Den vibrotaktila återkopplingen befanns stödjande för inlärning av deltagarna när det gäller utformning av arbetsplats och arbetsuppgift.

Sammanfattningsvis visar forskningen i denna avhandling att bärbar teknik kan användas både i laboratoriet och arbetslivet för att bedöma fysiska riskfaktorer i arbetet samt för interventioner syftande till förbättring av arbetsteknik. Med ytterligare forskning och utveckling kan system för smarta arbetskläder bidra till automatiserad riskbedömning, förebygga arbetsrelaterad ohälsa och förbättra utformningen av arbetet och arbetsplatsen.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 77
Series
TRITA-CBH-FOU ; 2019:53
Keywords
Physical Workload, Work Postures, Energy Consumption, Oxygen Uptake, Risk Assessment, Measurement Methods, Work-Related Musculoskeletal Disorders, Work-Related Ill Health, Wearable Sensors, Wearable Systems, Feedback, Ergonomic Intervention., Fysisk arbetsbelastning, Arbetsställningar, Energiförbrukning, Syreupptag, Riskbedömning, Mätmetoder, Arbetsrelaterade muskuloskeletala besvär, Arbetsrelaterad ohälsa, Bärbara sensorer, Bärbara system, Återkoppling, Ergonomisk intervention.
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-263768 (URN)978-91-7873-379-8 (ISBN)
Public defence
2019-12-06, Lecture hall T1, Hälsovägen 11C, Huddinge, 13:00 (English)
Opponent
Supervisors
Note

Thesis in the Karolinska Institutet and KTH Royal Institute of Technology joint doctoral programme in medical technology.

QC 2019-11-14

Available from: 2019-11-14 Created: 2019-11-12 Last updated: 2019-11-14Bibliographically approved

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

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