To simulate human motions in DHM tools, using techniques which are based on real human data is one promising solution. We have presented a solution in this study to connect motion databases with DHM tools. We have showed that using a motion database with MTM-based annotations is a promising way in order to synthesize natural looking motions. A platform consists of a Motion Database, a Motion Generator, and a DHM tool was introduced and tested. The results showed successful application of the presented platform in the designed test case. © 2013 Springer-Verlag.

A unified database platform capable of storing both motion captured data and information about these motions (metadata) is described. The platform stores large motion captured data in order to be used by different applications for searching, comparing, analyzing and updating existing motions. The platform is intended to be used to choose a realistic motion in simulation of production lines. It is capable of supporting and handling different motion formats, various skeleton types and distinctive body regions in a uniform data model. Extended annotating system is also introduced to mark the captured data not only in the time domain (temporal) but also on different body regions (spatial). To utilize the platform, sample tests are performed to prove the functionality. Several motion captured data is uploaded to the database while MATLAB is used to access the data, ergonomically analyze the motions based on OWAS standard, and add the results to the database by automatic tagging of the postures.

Digital human modelling (DHM) allows ergonomic risk assessment to be performed at early stages of design and development. Such assessment is typically based on observational methods, which do not take advantage of the potential of DHM tools to provide precise posture and motion data. This paper describes and illustrates an alternative assessment approach employing DHM tools, inspired by risk assessment based on direct measurements. A literature survey established a reference database of epidemiological associations between exposure and wrist-related disorders. This approach is illustrated by a DHM simulation of a car assembly task. Wrist posture and motion were simulated and compared to the database, predicting the prevalence of work-related musculoskeletal disorders on the basis of direct measurements