Smart Work Clothes Give Better Health - Through Improved Work Technique, Work Organization and Production Technology
Musculoskeletal disorders (MSDs) constitute a major health problem for employees, and the economic consequences are substantial for the individuals, companies and the society. The ageing population creates a need for jobs to be sustainable so that employees can stay healthy and work longer. Prevention of MSD risks therefore needs to become more efficient, and more effective tools are thus needed for risk management. The use of smart work clothes is a way to automate data collection instead of manual observation.
The aim of this paper is to describe a new smart work clothes system that is under development, and to discuss future opportunities using new and smart technology for prevention of work injuries.
The system consists of a garment with textile sensors woven into the fabric for sensing heart rate and breathing. Tight and elastic first layer work wear is the basis for these sensors, and there are also pockets for inertial measurement units in order to measure movements and postures. The measurement data are sent wireless to a tablet or a mobile telephone for analysis. Several employees can be followed for a representative time period in order to assess a particular job and its workplace. Secondly, the system may be used for individuals to practice their work technique. The system also gives relevant information to a coach who can give feedback to the employees of how to improve their work technique. Thirdly, the data analysis may also give information to production engineers and managers regarding the risks. The information will support decisions on the type of actions needed, the body parts that are critical and the emergency of taking action.
KeywordsPrevention Observation methods Wearables
The research includes a handful number of projects with different participants in each. It is also a collaboration between KTH Royal Institute of Technology, Karolinska Institutet, University of Borås and other partners. The authors would like to acknowledge the following financiers, AFA Insurance, Vinnova and EIT Health, participating test persons and research colleagues including Kaj Lindecrantz, Fernando Seoane, Farhad Abtahi, Liyun Yang, Ke Lu, Carl Lind and Jose Diaz-Olivares.
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