Using big data analytics to improve decision making of system-on-module based solutions

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Wearable devices are becoming increasingly popular and are estimated to reach 245 million units and be worth 25 billion dollars by 2019. To enable the rapid advancement of wearable devices for a variety of applications, system-on-module (SoM) solutions have been proposed. As SoM solutions become more powerful and prominent, they are playing a more intelligent role, and are being used in various applications such as sports training and rehab/therapy. Hence, SoM stakeholders are in need of analytics to improve the decisions made based on their solutions.To advance the state-of-the-art and keep Canadian manufacturers of SoM technology in the forefront, we propose the use of big data analytics for SoM-based solutions. The proposal aims to 1) apply techniques to enable effective data collection and analytics, 2) build SoM-specific analytics, and models that leverage these analytics, to enable the effective detection of user activities and 3) provide interface and user experience design for seamless feedback of extracted analytics. The proposed project aims to target a largely under-researched area, which is how to best use big data analytics and HCI to improve decision making for SoM applications. The potential outcome of the completed research will have impact on both industry and research. For industry, our research will lead to better SoM technology that is not only driven by hardware advances, but also driven by data and intelligent analytics. For the research community, our proposed research will spearhead the area of big data analytics for SoM, which to the best of our knowledge remains largely unexplored. In addition to being original, the proposed research is timely since it is projected that wearable technology will transform the future of many fields, including athletics, medicine, navigation and even shopping.