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.

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.

Adverse work-related physical exposures such as repetitive movements and awkward postures have negative health effects and lead to large financial costs. To address these problems, a multi-disciplinary consortium was formed with the aim of developing an ambulatory system for recording and analyzing risks for musculoskeletal disorders utilizing textile integrated sensors as part of the regular workwear. This paper presents the consortium, the Smart Workwear System, and a case study illustrating its potential to decrease adverse biomechanical exposure by promoting improved work technique.

Working postures and movements affect work efficiency and musculoskeletal health. To reduce the biomechanical exposure in physically demanding settings, working techniques may be improved by giving instant ergonomic feedback to the operator. This study investigates if feedback can be used to decrease adverse postures and movements in assembly work. A prototype solution of a smart textile workwear was used on a trainee assembly line. Posture and movement signals of 24 trainee operators were sampled via the workwear, transferred to a tablet for analyses and used to provide feedback suggesting improvements of work technique. Two modes of feedback were tested. Every participant’s work technique was measured before and after receiving the feedback and the results were compared. For upper arm elevation angle ≥60°, behaviour change is indicated, supporting a positive work technique change, and indicated a future usefulness of technical automatic feedback for operators.

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.

This paper presents needs and potentials for wearable sensors in occupational healthcare. In addition, it presents ongoing European and Swedish projects for developing personalized, and pervasive wearable systems for assessing risks of developing musculoskeletal disorders and cardiovascular diseases at work. Occupational healthcare should benefit in preventing diseases and disorders by providing the right feedback at the right time to the right person. Collected data from workers can provide evidence supporting the ergonomic and industrial tasks of redesigning the working environment to reduce the risks.

—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