# Human Cognitive and Motor Abilities in the Aging Workforce: An Information-Based Model
## 1 Introduction  
The phenomenon of population aging will become a serious issue in the next few years. Statistical projections state that in the next years the global average age expectation will increase to 76 years in 2050 and 81 years in 2100 [1]. The potential support ratio indicator, which is defined as the ratio of the number of people of the group 16-64 years over the number of 65 or more years people, is expected to decline from 4.9 to 1.9 in 2100 in the United States and from 2.9 to 1.4 in 2100 in Germany [2]. In Europe, the oldest age group (55-64 years) is expected to expand by 16.2% (9.9 million) between 2010 and 2030, with a decreasing trend of the other age groups [3]. Furthermore, 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. In 2050, the presence of older workers in production and operative roles will have an impact on economic growth and manufacturing efficiency [9; 10]. The decreasing of worker's ability happens in workers involved both in cognitive and physical tasks [11]. The age range of 45-50 years has often been used as the base criterion to refer to an \"aging worker\". The reduction of cognitive and physical abilities is a crucial point for workers aged from 45 to 64 [3]. Changes in mental functions affect the human sensory perception and information processing speed and accuracy. The key enabling technologies introduced by the new production paradigm of \"Industry 4.0\" can support aged operators [12; 13];nevertheless, in the Industry 4.0 environment, the operators are asked to perform complex cognitive tasks that require high cognitive efforts [14]. Fast information processing [15] in continuous learning processes is required in spite of a performance decrease of aged workers [16; 17; 18]. An increasing gap between cognitive tasks in production systems and cognitive capacities of aged operators is expected to become a serious issue as humans have limited information-processing capacity [16; 17; 18; 19]. A recent wide literature review on workforce aging in manufacturing systems as well as a research agenda is proposed in [20]. Here, a comprehensive analysis of modelling of aging workers' functional capacities in industrial systems is provided. Due to limits of human information processing, a general theoretical framework to assess information content of tasks is required. To this concerns, conceptual models available in the information and communication theory represent a challenging reference to be followed [21; 22]. In this paper, the authors propose an information-based model to estimate performance time of differently aged and sexed subjects in processing information during the execution of motor or cognitive tasks. Despite of the fact that a \"pure\" cognitive or motor task are rarely observed in reality, in the paper we refer to \"motor\" task as a task with prevalent motor content and low cognitive content; similarly, we refer to \"cognitive\" task as a task with low motor content and prevalent cognitive content. The paper is organized as follows: In Section 2, the phenomenon of aging and its influence on the cognitive abilities of individuals are discussed. In Section 3, field tests available in the scientific literature that allow to evaluate the influence of aging on human motor and cognitive abilities are considered and information theory models based on Shannon entropy measures are adopted to evaluate information content of test runs; related cognitive processing rate of individuals subjected to test runs are evaluated. Regression analyses of tests data allowed the authors to model the effects of the aging phenomenon on human physical and cognitive abilities. The operator's cognitive and motor performance time have been combined in a holistic information-based model detailed in Section 4.