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While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization. 

Generating paths of a mobile device in indoor space by sensing its Bluetooth RSSI value is challenging but has real-world applications. Although Bluetooth RSSI suffers from different factors that limit its usability, this research shows that it can still be used to detect mobility and, over a duration of time, can be used to form paths. This poster presents algorithms that can create a path of a moving mobile device by sensing its RSSI values over time and then presents early results of the algorithm's effectiveness while tracking health practitioners' movement within a community care clinic setting. 

Effective gamification can only be based on understanding the relationship between learner motivation and the game elements which are used to gamify learning activities. Although frequently mentioned, Virtual Currency (VC) remains underused and scarcely studied in educational gamification. As a motivational affordance, VC can be thought of as supporting different types of motivation, but currently, there is a lack of empirical studies which investigate this. Recognizing this gap, the purpose of our study was to empirically investigate whether and how gamifying learning activities with virtual currency can engender motivation for out-of-class practicing and what type of motivation. In the limited research others have conducted, VC has been studied largely in combination with other game elements, which does not allow reaching reliable conclusions about the impact of the individual elements. For this reason, we studied the effects of VC in a gamified Discrete Math course isolated from other game elements. The study showed that using VC to gamify practicing increased students’ practicing activity, which resulted in improved academic performance. The study also revealed that while gamified practicing did not increase students’ intrinsic motivation, it supported internalization of motivation towards this learning activity.