Digital Human Modelling and the Ageing Workforce

Digital human modelling (DHM) has often focused on user populations that could be characterised as able-bodied and in the age group. It is clear however that demographic changes are resulting in older populations in developed countries but is also becoming increasingly true even in developing countries. The economic pressures of increased life expectancy are in demands for workers to remain in employment well past what would previously have been considered a normal age. In many countries legislation is increasing retirement ages for entitlement to state pensions, and enforceable ages are being outlawed. As a consequence older working populations can be expected. Age in the workforce has positive aspects including increased experience, wisdom, loyalty and motivation, but an inevitable consequence of ageing negative effects such as the loss of capabilities in strength, mobility, vision and hearing. The challenge of including older is recognised as an important aspect of Inclusive Design and DHM is recognised as a potentially useful method for its. Today’s highly demanding and competitive working environments require the highest levels of productivity individuals so that overall operational and business objectives can be achieved. DHM-based workplace risk assessment have successfully been used to improve working environments by conducting virtual posture based ergonomic risk. Older workers are significantly different from younger workers in terms of their physical, physiological and cognitive and these capabilities directly or indirectly affect human work performance. This article suggests the use of human data in a virtual environment to explore the level of acceptability of a working strategy based on real capability data (joint mobility in this case) of older workers. A case study shows that the proposed DHM-based inclusive design method is useful working strategies that are acceptable for older workers in terms of work productivity, well-being and safety.