Towards Smart Work Clothing for Automatic Risk Assessment of Physical Workload

Yang, Liyun; Lu, Ke; Diaz-Olivares, Jose A.; Seoane, Fernando; Lindecrantz, Kaj; Forsman, Mikael; Abtahi, Farhad; Eklund, Jörgen

doi:10.1109/access.2018.2855719

Cited by 32 publications

(33 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of these sensor-based technical solutions provide kinematics data or perform classification of the type of physical activity [ 42 , 43 ], but only a few have integrated a system for automatic mapping of the kinematics data to research-based risk level metrics that can be used for occupational applications for the risk assessment of physical workloads or MSDs risk factors [ 44 , 45 , 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

“…In the handful of existing systems, the risk assessment usually includes mapping of the kinematics data to criteria derived from research-based observation tools, e.g., the Strain Index [ 48 , 49 ], the Ovako Working Posture Assessment System (OWAS) [ 50 , 51 , 52 ], the Rapid Upper Limb Assessment (RULA) [ 45 , 52 , 53 , 54 ], the Revised NIOSH lifting equation [ 52 , 55 , 56 ], or a single criterion associated with, for example, the maximal acceptable levels of energy expenditure to avoid global (whole-body) fatigue [ 44 ], or a criterion based on threshold values associated with an increased risk of developing MSDs based on epidemiological studies [ 47 ]. The risk assessment may give useful information that can be used for the guidance of measures and evaluation of the effectiveness of previously implemented measures [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

Lind

Diaz-Olivares

Lindecrantz

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Work-related musculoskeletal disorders are a major concern globally affecting societies, companies, and individuals. To address this, a new sensor-based system is presented: the Smart Workwear System, aimed at facilitating preventive measures by supporting risk assessments, work design, and work technique training. The system has a module-based platform that enables flexibility of sensor-type utilization, depending on the specific application. A module of the Smart Workwear System that utilizes haptic feedback for work technique training is further presented and evaluated in simulated mail sorting on sixteen novice participants for its potential to reduce adverse arm movements and postures in repetitive manual handling. Upper-arm postures were recorded, using an inertial measurement unit (IMU), perceived pain/discomfort with the Borg CR10-scale, and user experience with a semi-structured interview. This study shows that the use of haptic feedback for work technique training has the potential to significantly reduce the time in adverse upper-arm postures after short periods of training. The haptic feedback was experienced positive and usable by the participants and was effective in supporting learning of how to improve postures and movements. It is concluded that this type of sensorized system, using haptic feedback training, is promising for the future, especially when organizations are introducing newly employed staff, when teaching ergonomics to employees in physically demanding jobs, and when performing ergonomics interventions.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

Lind

Diaz-Olivares

Lindecrantz

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In many industries, it is also common work-related musculoskeletal and cardiovascular disorders, which are often due to excessive workload. To improve traditional risk assessments, which are usually performed via self-reports or observations and that have relatively low reliability, the authors of [221] developed a smart vest with diverse sensors (i.e., ECG, Thoracic Electrical Bio-impedance (TEB), an Internal Measurement Unit (IMU)) that were connected through Bluetooth to an Android tablet where data were processed, stored and visualized.…”

Section: Industry Defense and Public Safetymentioning

confidence: 99%

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

2018

View full text Add to dashboard Cite

Technology has become ubiquitous, it is all around us and is becoming part of us. Together with the rise of the Internet-of-Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet-of-Smart-Clothing.

show abstract

“…This has already been explored in many other areas, such as the military and sports (Buttussi and Chittaro 2008;Coyle et al 2009;Seoane et al 2014;Mohino-Herranz et al 2015). There is an ongoing research effort aiming at developing smart wearable systems for automatic risk assessment at work, which facilitate risk identification, communication and interventions for better work environments (Yang, Grooten, and Forsman 2017;Abtahi et al 2017;Lind et al 2019;Eklund and Forsman 2018;Yang et al 2018). One step towards this aim is to evaluate methods that are applicable for estimating work metabolism (WM) with wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Yang

Forsman

et al. 2019

Ergonomics

Self Cite

View full text Add to dashboard Cite

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO 2 ), which is commonly assessed by heart rate (HR) in field studies. However, the VO 2 -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 hipworn accelerometers (ACC); and HR þ arm-leg ACC model, combining HR with wrist-and thighworn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR þ arm-leg ACC model had the overall best performance with limits of agreement (LoA) of À3.94 and 2.00 mL/min/kg, while the HR-Flex model had À5.01 and 5.36 mL/min/kg and the branched model, À6.71 and 1.52 mL/min/kg. In conclusion, the HR þ 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.Abbreviations: ACC: accelerometer; EE: energy expenditure; HR: heart rate; LoA: limits of agreement; RAS: relative aerobic strain; REE: resting energy expenditure; RHR: resting heart rate; VO2: oxygen consumption; WM: work metabolism ARTICLE HISTORY

show abstract

Towards Smart Work Clothing for Automatic Risk Assessment of Physical Workload

Cited by 32 publications

References 52 publications

A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Contact Info

Product

Resources

About