Reference number : GC-2017-Q1-02574, amendment number : , agreement type : G, recipient legal name : Morita, Plinio (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : With the wide expansion and uptake of Internet of Things (IoT), devices collecting our personal data and monitoring our every single move are now generating huge amounts of information. Current forecasts indicate between 26 and 75 billion IoT connected devices by 2020, with the IoT smart home market estimated to be at US$ 121.73 billion by 2022, and as much as 69% of North Americans expected to buy at least one in-home or wearable IoT device by 2019. However, all the data generated by these technologies are still siloed within manufacturers&rsquo; cloud services and require proprietary algorithms to be processed, with limited effort towards providing users with meaningful and actionable knowledge. Users are discontinuing the use of IoT and wearable technology due to limited usefulness of the data and information overload. 
In this research program, we aim to advance knowledge of Human Factors Engineering in the area of IoT and wearable technology by: (1) exploring how different types of users (novice/experts, young/elderly, users/super users) have been processing and integrating this data, the impact of trust in the connected technology, how this data influences decision making (DM), and the effects of information overload; (2) creating data integration frameworks and data visualizations to generate meaningful use for this data, and (3) creating encapsulations and new ubiquitous sensors fully integrated into a smart homes to reduce barriers towards data collection. 
A mixed-methods approach will be used to explore how different types of users manage the information overload and integrate the data from IoT devices into actionable knowledge. In-situ observations combined with interviews will provide insights on how different types of users handle the overload in their home settings, providing important contextual information for the design of new technology. In-lab controlled studies will be used to demonstrate the impact of data on DM and trust in the technology. Data integration frameworks, visualizations, and new encapsulations will be generated and tested using well established Human Factors Engineering methodologies, replicating cognitive processes used by experienced users to provide everyday users with the same level of actionable information. 
This research program will provide IoT technology users, researchers, and innovators with: (1) advanced knowledge of the impact of IoT and wearable data on DM, trust in the technology, and information overload; and (2) resources to deal with information overload (data integration frameworks, data visualizations, and improved sensors) to provide actionable and useful information, increase situation awareness, and improve trust in the technology. The knowledge generated in this research program will benefit everyday use, military, emergency services, transportation, and healthcare applications of IoT and wearable sensory technology., program purpose french : With the wide expansion and uptake of Internet of Things (IoT), devices collecting our personal data and monitoring our every single move are now generating huge amounts of information. Current forecasts indicate between 26 and 75 billion IoT connected devices by 2020, with the IoT smart home market estimated to be at US$ 121.73 billion by 2022, and as much as 69% of North Americans expected to buy at least one in-home or wearable IoT device by 2019. However, all the data generated by these technologies are still siloed within manufacturers&rsquo; cloud services and require proprietary algorithms to be processed, with limited effort towards providing users with meaningful and actionable knowledge. Users are discontinuing the use of IoT and wearable technology due to limited usefulness of the data and information overload. 
In this research program, we aim to advance knowledge of Human Factors Engineering in the area of IoT and wearable technology by: (1) exploring how different types of users (novice/experts, young/elderly, users/super users) have been processing and integrating this data, the impact of trust in the connected technology, how this data influences decision making (DM), and the effects of information overload; (2) creating data integration frameworks and data visualizations to generate meaningful use for this data, and (3) creating encapsulations and new ubiquitous sensors fully integrated into a smart homes to reduce barriers towards data collection. 
A mixed-methods approach will be used to explore how different types of users manage the information overload and integrate the data from IoT devices into actionable knowledge. In-situ observations combined with interviews will provide insights on how different types of users handle the overload in their home settings, providing important contextual information for the design of new technology. In-lab controlled studies will be used to demonstrate the impact of data on DM and trust in the technology. Data integration frameworks, visualizations, and new encapsulations will be generated and tested using well established Human Factors Engineering methodologies, replicating cognitive processes used by experienced users to provide everyday users with the same level of actionable information. 
This research program will provide IoT technology users, researchers, and innovators with: (1) advanced knowledge of the impact of IoT and wearable data on DM, trust in the technology, and information overload; and (2) resources to deal with information overload (data integration frameworks, data visualizations, and improved sensors) to provide actionable and useful information, increase situation awareness, and improve trust in the technology. The knowledge generated in this research program will benefit everyday use, military, emergency services, transportation, and healthcare applications of IoT and wearable sensory technology., agreement title english : Real-Time Data from IoT Devices and Their Influence on Decision Making, agreement title french : Real-Time Data from IoT Devices and Their Influence on Decision Making, agreement number : RGPIN, agreement value : 110000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada