Signal 1: Aging Population and Predictive Digital Twin Technology
The aging population is a worldwide trend on population growth. According to the United Nations (2015), caused by people’s longer life expectancy and lower fertility rate, the global population aged 60 years or over is expected to increase from 901 million in 2015 to 1.4 billion in 2030.
In order to age healthily and actively, it is important for the elderly to aware of their self-health status, and prevent future disease and health failure. On the other hand, the government needs more expenditures on the elderly health care and protection.
To further balance the cost burdens on the elderly care needs and maintenances, the predictive digital twin technology can help the elderly anticipate the health failure and address them before happening, which minimize the unnecessary health-related costs (e.g., health checks) and improve their awareness on self-health condition.
The predictive digital twin combines the engineering simulation software with sensors, machine learning, and data analytics. This technology can deliver condition-based monitoring on the elderly’s health status and conduct further predictive maintenance. Based on the data from the sensors, the process can create a virtual learning replica of the elderly’s physical asset and structure the data to inform the elderly’s current condition, as well as predictions on future health condition. By using this technology, the costly routine health checks and maintenances can be replaced by the data-based and empowering inspection technology.
Resources:
The United Nations. 2015. World Population Ageing [Department of Economic and Social Affairs]. Available online at http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf
Mark Venables. 2018. Digital Twins Provide A Window Into The Future for Ageing Assets. Available online at https://www.forbes.com/sites/markvenables/2018/08/26/digital-twins-provide-a-window-into-the-future-for-ageing-assets/#274d7bde6993
