Challenges in Developing Applications for Aging Populations

Elderly individuals can greatly benefit from the use of computer applications, which can assist in monitoring health conditions, staying in contact with friends and family, and even learning new things. However, developing accessible applications for an elderly user can be a daunting task for developers. Since the advent of the personal computer, the benefits and challenges of developing applications for older adults have been a hot topic of discussion. In this chapter, the authors discuss the various challenges developers who wish to create applications for the elderly computer user face, including age-related impairments, generational differences in computer use, and the hardware constraints mobile devices pose for application developers. Although these challenges are concerning, each can be overcome after being properly identified

Conversational Sensing

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Context-Aware Industrial Internet of Things Data Logging

PURPOSE: In many parts of today’s manufacturing process, a product is removed from one manufacturing line and moved to another. Resulting from this move data collection, such as the environmental condition is often collected for the whole process line for a given time range, and not the direct product going through that line at a given moment. Moreover, variable product mix can affect an entire line’s environmental characteristics. Understanding this variability, it would seem logical to measure environmental data at the product level, rather than the process level to ensure product-level compliance within prescribed environmental tolerances. This project encompasses the creation of an Industrial Internet of Things (IIoT) device that monitors, logs and transmits environmental data to a cloud-based database service. The device also keeps track of the product in which it is monitoring, with minimal user interaction. It is a portable device suitable for continued use in a 24x7 manufacturing environment. PROCEDURES: Research was conducted in three categories; software development, electronics and additive manufacturing. The research was conducted to attain the simplest and most efficient design to measure from multiple sensors in a custom enclosure that was portable. ANALYSES: Datalogging is used widely in industry today, however the data is typically concentrated on a specific process or process line, rarely at the product level. Additionally, the data stored in traditional loggers require operators to download the data after the process has completed, minimizing the opportunity to catch deviations in the moment. Lastly commercially available data loggers lack the ability to contextualize to the product in which they are measuring and usually only measure one or two senses, such as temperature and humidity. CONCLUSION: Adding specific part-context to a stream of data that is transmitted live from the assembly line can help identify manufacturing tolerance deviations in real-time. Moreover, the collected data can be analyzed using industry standard tools to further understand patterns to prevent similar defects from occurring