Key feature method: The digital revolution in ergonomics

Discover the digital Leitmerkmalmethode, which is based on developments by the BAuA and various scientific partners. Learn more about the differences, origins and advantages of this method compared to traditional approaches.

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EAWS and the digital Key Indicator Method method: differences

The digital Key Indicator Method determines the instantaneous physical stress during manual work. At present, the (digital)Key Indicator Method can be divided into six variants: 1. manual lifting, holding, and carrying of loads (LMM-HHT), 2. manual pulling and pushing of loads (LMM-ZS), 3. manual work processes (LMM-MA), 4. the exercise of whole-body forces (LMM-GK), 5. body movement (LMM-KB) and 6. forced postures (LMM-KH). The digitaö Key Indicator Method is available in German and English respectively. These methods can now be performed and used in a digital manner (digital master feature method). In addition to the digital Key Indicator Method, physical loads on the body can also be determined with the help of EAWS. EAWS can also be performed in digital form. In the following, the origins, procedures, and reasons for the digital reference trait method and EAWS will be explained in more detail.


EAWS and the digital Key Indicator Method: Origin

The guidelines for the digital Key Indicator Method are based on the Council Convention of 1990. The decision includes the minimum requirements for the protection of employees during manual handling of loads, in particular through the manual load back injuries can not be excluded.

The digital Key Indicator Method method is based on joint developments by the Federal Institute for Occupational Safety and Health (BAuA) and various scientific partners in coordination with the State Committee for Occupational Safety and Health and Safety Technology (LASI).

The (digital) Key Indicator Method LMM-HHT was first published in 2001, and the (digital) guidance feature method LMM-ZS and the LMM-MA appeared over the next 11 years. In October 2019, the BAuA last published the three extended leading characteristic methods "LMM-HHT", "LMM-ZS" and "LMM-MA" and additionally the three new leading characteristic methods "LMM-GK", "LMM-KB" and "LMM-KH".

The digital Key Indicator Method was developed to meet the requirements of the load handling regulation and to be able to organize the activities of the employees in a way that is suitable for humans.


EAWS and the digital Key Indicator Method: Fixed Guidelines

The digital Key Indicator Method enables an evaluation of the risk in the case of physical stress while manual movements such as lifting or carrying loads, pulling and pushing loads or similar are carried out in the work process.

Finally, the evaluation of the individual sub-activities is carried out from the assessment of the determined key characteristics on the basis of a characteristic value and the determination of the weightings of the key characteristics, after the final multiplication with the time weighting.

Hidden optimization potentials are uncovered within the scope of Motion-Mining® ergonomics analyses. The solution approach allows an evaluation of manual work, specifically the posture, and movement of employees, using wearables and a deep learning algorithm, better known as artificial intelligence. Work processes are automatically and anonymously recorded, processed by artificial intelligence, and converted into key performance indicators. Currently, we distinguish between more than 60 different movement sequences in our ergonomics analyses. Critical movements such as bending from the back, carrying, lifting, holding, overhead activities are considered in the ergonomics analysis. These movements are recorded in movement intervals, during the ergonomics analysis. In addition to the typical movements, vibrations and repetitions in particular can also be detected. Based on the data from the ergonomics analysis, overloads and permanent stresses can be identified and measures to avoid them can be derived. Methods such as EAWS are used as part of these analyses.


EAWS and the digital Key Indicator Method: Gender Differences.

On average, the physical resilience of men is about 2/3 that of women. This difference between women and men is primarily due to a wide variety of factors, including different body proportions. In order that, for example, these factors can also be included in physically demanding activities for preventive health reasons, the weighting of loads in the manual lifting, holding and carrying of loads (LMM-HHT) Key Indicator Method is surveyed separately for men and women according to the corresponding LMM table; with the same load weighting, women receive a higher rating. On the other hand, different multipliers apply to the various leading characteristic methods to compensate for the given differences, e.g. LMM-ZS: the intermediate score for men, is multiplied by a factor of 1.3 for women.


EAWS and the digital Key Indicator Method: Digitizing the Methods (EAWS).

The digital Key Indicator Method for the ergonomic assessment and evaluation of workplaces and processes makes it possible to provide a precise, generally understandable explanation of the strain on employees at specific workplaces with comparatively little effort. The digital Key Indicator Method offers improvement approaches that can be derived directly from the collected data in order to integrate them into one's own work planning at an early stage.

These methods, although initially conceived as analog versions, can now also be carried out and used in digital form (digital Key Indicator Method). Implemented with the latest software development tools, EAWS-digital, for example, can record and evaluate all the information required for the process in a simple and intuitive way.

Human simulation (digital human models) is also used in planning for the digital factory to clarify and scrutinize manual activities in 3D as early as the planning phase. In planning, simulation is used to assess manual tasks both in terms of time economy (MTM process modules) and ergonomics (EAWS process). Human simulation for the investigation and optimization of assembly processes, time expenditure, and ergonomics is thus increasingly finding its way into everyday operations.

For example, increasing economic competition worldwide and declining population growth in Europe is forcing the economic design of workplaces and process flows to include human performance. A corresponding ergonomic instrument is EAWS (Ergonomic Assessment Worksheet), which includes all stages of the life cycle of goods (developed by MTM and IAD). In cooperation with well-known European companies as users of EAWS, a continuous improvement of the ergonomic assessment takes place. EAWS can be used to optimize the ergonomics assessment with regard to posture, handling forces, manual load handling, load frequencies for the upper limb areas, e.g. in the form of the EAWS quick analysis or the automatic determination of movement data and forces (motion capturing). EAWS ensures precise determination of the holistic stress at the workplace to achieve ergonomic design quality for permanently healthy and productive work.

The demands on the industry are increasing due to progressive globalization and increasing digitalization. In order for the industry to keep up and hold its own in global and national competition in the future, it needs innovative approaches to solutions, such as the EAWS method, and continuous adaptation.

By using EAWS, the physical loads on the body as well as the upper extremities at the respective workplace can be determined. Based on the understanding gained through the EAWS method, workplaces can be ergonomically designed as a whole.

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