Abstract
In the digital society, individuals are in charge of performing tasks based on the information gathered by huge amount of data and effectively use them to manifest their cognitive and motor abilities. In this paper, on the basis of experimental studies available in literature concerning lab tests on motor or cognitive abilities of differently aged subjects, an information-based theoretical model is proposed. The model allows to quantify the information content of a motor or a cognitive task and provides estimates of information processing time of individuals of different age and sex in accomplishing tasks with prevalent motor or cognitive nature, in spite of the fact that a “pure” cognitive or a “pure” motor task are rarely observed in practical cases. The model is then applied to a case study from automotive industry in which workforce aging phenomenon is experienced. Potential applications of the model go beyond the case study developed. Quantifying the information content of a general motor-cognitive task paves the way to new understanding and modelling of movements and performance time of both natural and artificial systems with applications in industrial robotics (e.g., human-robot cooperation), biomechanics, and neurorehabilitation.
Highlights
The phenomenon of population aging will become a serious issue in the few years
The model has been applied to a case study from automotive industry in order to identify the optimal assignment of operators with different age and sex to workstations of an assembly line
The results of this study indicate that the information processing model developed can be adopted to assess the performance time of differently aged and sexed individuals accomplishing cognitive or motor tasks
Summary
The phenomenon of population aging will become a serious issue in the few years. People tend to work until later ages due to multiple social and economic reasons such as delayed retirement [4,5,6,7,8] as long as their cognitive and physical health allows it [4]. This issue will bring to many implications in multiple fields and a better awareness of the workforce aging phenomenon is needed
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