With the rapidly aging population and the rising number of people living with dementia (PLWD), there is an urgent need for programming and activities that can promote the health and wellbeing of PLWD. Due to staffing and budgetary constraints, there is considerable interest in using technology to support this effort. Serious games for dementia have become a very active research area. However, much of the work is being done without a strong theoretical basis. We incorporate a Montessori approach with highly tactile interactions. We have developed a person-centered design framework for serious games for dementia with initial design recommendations. This framework has the potential to facilitate future strategic design and development in the field of serious games for dementia.
In today’s digital economy, the Internet of Things (IoT) has connected devices, humans, and everyday objects to each other in ways that were unimaginable before. Vast amounts of data are collected everywhere and disrupting how we design systems and products. Data science and emerging technologies offer challenges and opportunities for early-career human factors professionals who are looking to grow their careers and their human factors practice. In this paper, we report on a survey to assess the perspectives of students currently studying human factors. The survey items examined shortfalls in current human factors education with re-spect to relevance to industry trends. The survey results show that students see a need to include more relevant subjects in data science, as well as opportunities to learn trending industry problems, hands-on experience with real-life projects, prior to graduation.
Human Factors Engineering (HFE) is an applied discipline that uses a wide range of methodologies to better the design of systems and devices for human use. Underpinning all human factors design is the maxim to fit the human to the task/machine/system rather than vice versa. While some HFE methods such as task analysis and anthropometrics remain relatively fixed over time, areas such as human-technology interaction are strongly influenced by the fast-evolving technological trend. In times of big data, human factors engineers need to have a good understanding of topics like machine learning, advanced data analytics, and data visualization so that they can design data-driven products that involve big data sets. There is a natural lag between industrial trends and HFE curricula, leading to gaps between what people are taught and what they will need to know. In this paper, we present the results of a survey involving HFE practitioners (N=101) and we demonstrate the need for including data science and machine learning components in HFE curricula.
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