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Ergonomic Risk Assessment and Intervention through Smart Workwear Systems
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics.ORCID iD: 0000-0001-7285-824X
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 [en]
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.
Keywords [sv]
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: urn:nbn:se:kth:diva-263768ISBN: 978-91-7873-379-8 (print)OAI: oai:DiVA.org:kth-263768DiVA, id: diva2:1369715
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
List of papers
1. An iPhone application for upper arm posture and movement measurements
Open this publication in new window or tab >>An iPhone application for upper arm posture and movement measurements
2017 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126, Vol. 65, p. 492-500Article in journal (Refereed) Published
Abstract [en]

There is a need for objective methods for upper arm elevation measurements for accurate and convenient risk assessments. The aims of this study were (i) to compare a newly developed iOS application (iOS) for measuring upper arm elevation and angular velocity with a reference optical tracking system (OTS), and (ii) to compare the accuracy of the iOS incorporating a gyroscope and an accelerometer with using only an accelerometer, which is standard for inclinometry. The iOS-OTS limits of agreement for static postures (9 subjects) were -4.6° and 4.8°. All root mean square differences in arm swings and two simulated work tasks were <6.0°, and all mean correlation coefficients were >0.98. The mean absolute iOS-OTS difference of median angular velocity was <13.1°/s, which was significantly lower than only using an accelerometer (<43.5°/s). The accuracy of this iOS application compares well to that of today's research methods and it can be useful for practical upper arm measurements.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
work-related musculoskeletal disorders, accelerometer, gyroscope
National Category
Medical Ergonomics
Identifiers
urn:nbn:se:kth:diva-212177 (URN)10.1016/j.apergo.2017.02.012 (DOI)000408597400050 ()2-s2.0-85014522150 (Scopus ID)
Funder
AFA Insurance, 120237
Note

QC 20170817

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2019-11-12Bibliographically approved
2. Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system
Open this publication in new window or tab >>Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system
Show others...
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.

Keywords
Heart rate, work metabolism, motion sensing, wearable sensors, risk assessment, estimation models
National Category
Medical Engineering
Identifiers
urn:nbn:se:kth:diva-239148 (URN)10.1080/00140139.2019.1566579 (DOI)000468779800007 ()30806164 (PubMedID)2-s2.0-85062366366 (Scopus ID)
Funder
AFA Insurance, 150039
Note

QC 20190218

Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2019-11-12Bibliographically approved
3. Towards Smart Work Clothing for Automatic Risk Assessment of Physical Workload
Open this publication in new window or tab >>Towards Smart Work Clothing for Automatic Risk Assessment of Physical Workload
Show others...
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 40059-40072Article in journal (Refereed) Published
Abstract [en]

Work-related musculoskeletal and cardiovascular disorders are still prevalent in today's working population. Nowadays, risk assessments are usually performed via self-reports or observations, which have relatively low reliability. Technology developments in textile electrodes (textrodes), inertial measurement units, and the communication and processing capabilities of smart phones/tablets provide wearable solutions that enable continuous measurements of physiological and musculoskeletal loads at work with sufficient reliability and resource efficiency. In this paper, a wearable system integrating textrodes, motion sensors, and real-time data processing through a mobile application was developed as a demonstrator of risk assessment related to different types and levels of workload and activities. The system was demonstrated in eight subjects from four occupations with various workload intensities, during which the heart rate and leg motion data were collected and analyzed with real-time risk assessment and feedback. The system showed good functionality and usability as a risk assessment tool. The results contribute to designing and developing future wearable systems and bring new solutions for the prevention of work-related disorders.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Energy expenditure, sitting, standing, occupational health, preventive healthcare, wearable sensors, sensorized garments
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
urn:nbn:se:kth:diva-233434 (URN)10.1109/ACCESS.2018.2855719 (DOI)000441214800001 ()2-s2.0-85050003765 (Scopus ID)
Note

QC 20180821

Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-11-12Bibliographically approved
4. Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback
Open this publication in new window or tab >>Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Vibrotactile feedback training may be one possible method for interventions that target at learning better work technique and improving postures in manual handling. The aim of this study was to evaluate the effect of real-time vibrotactile feedback using a smart workwear system for work postures intervention in industrial order picking. Fifteen workers at an industrial manufacturing plant performed order-picking tasks, in which the vibrotactile feedback was used for postural training at work. The trunk and upper arm postures were recorded by the system. Questionnaires and semi-structured interviews were conducted about the users’ experience of the system. The results showed reduced time in adverse postures for the trunk and upper arms when the workers received feedback, and for trunk postures also after feedback withdrawal. The workers perceived the system as usable, comfortable and supportive for learning.

Keywords
work technique training, the Smart Workwear Consortium, intervention
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-263308 (URN)
Note

QC 20191113

Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-13Bibliographically approved

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