In recent years, we have witnessed a growing adoption of serious games in telerehabilitation by taking advantage of advanced multimedia technologies such as motion capture and virtual reality devices. Current serious game solutions for telerehabilitation suffer form lack of personalization and adaptiveness to patients’ needs and performance. This paper introduces “RehaBot”, a framework for adaptive generation of personalized serious games in the context of remote rehabilitation, using 3D motion tracking and virtual reality environments. A personalized and versatile gaming platform with embedded virtual assistants, called “Rehab bots”, is created. Utilizing these rehab bots, all workout session scenes will include a guide with various sets of motions to direct patients towards performing the prescribed exercises correctly. Furthermore, the rehab bots employ a robust technique to adjust the workout difficulty level in real-time to match the patients’ performance. This technique correlates and matches the patterns of the precalculated motions with patients’ motions to produce a highly engaging gamified workout experience. Moreover, multimodal insights are passed to the users pointing out the joints that did not perform as anticipated along with suggestions to improve the current performance. A clinical study was conducted on patients dealing with chronic neck pain to prove the usability and effectiveness of our adjunctive online physiotherapy solution. Ten participants used the serious gaming platform, while four participants performed the traditional procedure with an active program for neck pain relief, for two weeks (10 min, 10 sessions/2 weeks). Feasibility and user experience measures were collected, and the results of experiments show that patients found our game-based adaptive solution engaging and effective, and most of them could achieve high accuracy in performing the personalized prescribed therapies.

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Protecting patient geo-privacy while allowing for valid geographic analyses of the data is a major challenge [1]. As a consequence, a variety of methods have been introduced to mask patients' locational information, also called geo-masking methods [2]. This study assessed the five main geo-masking methods as cited by [3] in terms of re-identification risk and performance. These five methods are Random Direction and Fixed Radius, Random Perturbation within a Circle, Gaussian Displacement, Donut Masking, and Bimodal Gaussian Displacement. Based on the assessment, the study highlighted two major gaps in the design of these geo-masking methods. First, all five geo-masking methods used only population density in calculating the displacement distances between the original locations of points and their new locations. However, other criteria that might be as important as population density were not considered in designing these five methods. These include data sensitivity, research types, quasi-indicator availability, previously generated maps availability, end-users' types, and the possibility of temporal synergy of data. Second, the Donut Masking and the Bimodal Gaussian Displacement methods were found to be superior in terms of minimizing re-identifying risks, but they were also found to be consuming much more processing power compared to the other three geo-masking methods. To address these gaps, this study proposed a new geo-masking method, called the "Triangular Displacement". The primary design, development, and evaluation of the Triangular Displacement method were based on the Design Science Research (DSR) Process Model [4], also known as DSRM. The expected next step is to implement the resultant geomasking method as a tool to help healthcare data guardians deidentify large sets of PHR automatically. A pilot study with a large Southern Californian healthcare provider has been outlined to examine the efficacy of the developed solution.