| 1. |
- Lind, Carl Mikael, et al.
(författare)
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Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Abtahi, Farhad, 1981-, et al.
(författare)
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Big Data & Wearable Sensors Ensuring Safety and Health @Work
- 2017
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Ingår i: GLOBAL HEALTH 2017, The Sixth International Conference on Global Health Challenges. - 9781612086040
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Konferensbidrag (refereegranskat)abstract
- —Work-related injuries and disorders constitute a major burden and cost for employers, society in general and workers in particular. We@Work is a project that aims to develop an integrated solution for promoting and supporting a safe and healthy working life by combining wearable technologies, Big Data analytics, ergonomics, and information and communication technologies. The We@Work solution aims to support the worker and employer to ensure a healthy working life through pervasive monitoring for early warnings, prompt detection of capacity-loss and accurate risk assessments at workplace as well as self-management of a healthy working life. A multiservice platform will allow unobtrusive data collection at workplaces. Big Data analytics will provide real-time information useful to prevent work injuries and support healthy working life
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| 3. |
- Abtahi, Farhad, 1981-, et al.
(författare)
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Wearable Sensors Enabling Personalized Occupational Healthcare
- 2018
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Ingår i: Intelligent Environments 2018. - Amsterdam : IOS Press. - 9781614998730 - 9781614998747 ; , s. 371-376
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents needs and potentials for wearable sensors inoccupational healthcare. In addition, it presents ongoing European and Swedishprojects for developing personalized, and pervasive wearable systems for assessingrisks of developing musculoskeletal disorders and cardiovascular diseases at work.Occupational healthcare should benefit in preventing diseases and disorders byproviding the right feedback at the right time to the right person. Collected datafrom workers can provide evidence supporting the ergonomic and industrial tasksof redesigning the working environment to reduce the risks.
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| 4. |
- Lind, Carl, et al.
(författare)
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Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback
- 2020
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Ingår i: Applied Ergonomics. - : Elsevier. - 0003-6870 .- 1872-9126. ; 89
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Tidskriftsartikel (refereegranskat)abstract
- Vibrotactile feedback training may be one possible method for interventions that target at learning better work techniques and improving postures in manual handling. This study aimed to evaluate the short term effect of real-time vibrotactile feedback on postural exposure using a smart workwear system for work postures intervention in simulated 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 system recorded the trunk and upper arm postures. Questionnaires and semi-structured interviews were conducted about the users’ experience of the system. The results showed reduced time in trunk inclination ≥20°, ≥30° and ≥45° and dominant upper arm elevation ≥30° and ≥45° when the workers received feedback, and for trunk inclination ≥20°, ≥30° and ≥45° and dominant upper arm elevation ≥30°, after feedback withdrawal. The workers perceived the system as useable, comfortable, and supportive for learning. The system has the potential of contributing to improved postures in order picking through an automated short-term training program. © 2020 Elsevier Ltd
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| 5. |
- Lu, Ke, et al.
(författare)
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Fusion of heart rate, respiration and motion measurements from a wearable sensor system to enhance energy expenditure estimation
- 2018
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 18:9
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.
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| 6. |
- Vega-Barbas, Mario, et al.
(författare)
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P-Ergonomics Platform : Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing
- 2019
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:5
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Tidskriftsartikel (refereegranskat)abstract
- Preventive healthcare has attracted much attention recently. Improving people's lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals' work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability.
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| 7. |
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| 8. |
- Yang, Liyun, 1992-, et al.
(författare)
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Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system.
- 2019
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Ingår i: Ergonomics. - : Taylor & Francis. - 0014-0139 .- 1366-5847. ; 62:5, s. 694-705
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Tidskriftsartikel (refereegranskat)abstract
- 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. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.
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| 9. |
- Yang, Liyun, et al.
(författare)
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Towards Smart Work Clothing for Automatic Risk Assessment of Physical Workload
- 2018
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 6, s. 40059-40072
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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