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L. Fadrique



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    EP01 - e-Poster Session 1 (ID 52)

    • Event: e-Health 2018 Virtual Meeting
    • Type: e-Poster Session
    • Track: Clinical Delivery
    • Presentations: 1
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      EP01.07 - Data Processing for Vital Signs Detection Using a Functional Bedsheet (ID 303)

      L. Fadrique, School of Public Health and Health Systems, University of Waterloo; Waterloo/CA

      • Abstract
      • Slides

      Purpose/Objectives: Describe the development of a functional bedsheet using a non-invasive method for monitoring respiration patterns and changes in respiratory rate. The functional bedsheet captures millions of data points across a range of fabric pressure sensors, providing rich insights on individualsÂ’ health, sleep patterns, apnea, respiratory rate, among others. In this study, we will validate the vital signs detection of the functional bed technology through a clinical study using user-centered evaluation (UCE).

      Methodology/Approach: Our research team will conduct a clinical study evaluating the participants' interaction with the functional bedsheet, their movement in the bed, comfort, and the accuracy of data collection during such movement in bed. Another important aspect of this study is the validation of the functional bedsheet in parallel with other monitoring equipment, thus understanding possible interactions or interferences. The functional bedsheet consists of multiple sensors that cover a wide area that can determine pressure variation, the system is designed as an ecosystem where each sensor acts independently, yet is dependent of the neighbouring sensors. The functional bedsheet will be validated against several other clinical-grade sensors (SpO2, ECG, etc.) Our research team will use the data collected to develop a machine-learning algorithm for detection of respiration changes and apnea patterns, helping translate raw sensor data into useful and meaningful insights for patients and clinicians.

      Finding/Results: The expected results are the creation of a product that requires minimal interaction by the user (zero-effort technology), which is an important characteristic for our final user-population (older adults living independently). Additionally, our research program aims to use the data collected in the study to train the system to identify breathing patterns and diagnose apnea using machine learning. The technology is being designed for rapid diagnosis, prevention of associated diseases and reduction of visits to physicians.

      Conclusion/Implications/Recommendations: Finding ways to improve the care and monitoring of patients in your own home is a necessary solution to reduce costs and increase the wellness of the user through uncomplicated equipment and easy access. The functional bedsheet aims to assist in this goal by making it possible to monitor vital signs and apnea continuously in the comfort of your bed.

      140 Character Summary: Vital signs detection using a functional bedsheet and use of machine learning to identify respiratory patterns and apnea.

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    OS15 - Propelling Clinical Care via Standardization (ID 24)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 1
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      OS15.03 - Policies, Guidelines, and Standards for Ambient Assisted Living Data Exchange (ID 273)

      L. Fadrique, School of Public Health and Health Systems, University of Waterloo; Waterloo/CA

      • Abstract
      • Slides

      Purpose/Objectives: The primary objective of this study was to understand what Ambient Assisted Living (AAL) technology companies are using as policy guidelines and standards in the creation of their products and services. Ultimately, this study will highlight the gap between what is currently available for innovators in terms of data security, privacy and encryption, and what should be developed to ensure that AAL technology is designed ensuring benefits to patients and the healthcare system through safe technologies. The long-term goal of this project is the development of requirements for an infrastructure to enable IoT, mHealth, and wearable data integration, focusing on describing the design, data exchange requirements, policy and governance guidelines, and general standards for implementing this infrastructure.

      Methodology/Approach: This study will explore human factors methods (user-centered design) to guide the development of standards and the data sharing infrastructure. The project is divided into the following phases: 1. Review the existing ALL technology literature and technology market in Canada. 2. Interview key policymakers, innovators, researchers, stakeholders, and leaders in the AAL landscape to support the development of policy governance guidelines, standards, and identifying the requirements of a data integration infrastructure for AAL technology. 3. Use ethnographic and UCD methods focusing on gathering key stakeholders' perspective on the issue.

      Finding/Results: In this study, we will identify existing standards and guidelines that are currently used to guide the development of AAL and IoT technologies by the current technology industry. This study will lead to recommendations for standards to be created to support innovators in the development of AAL in Canada. The guidelines and standards will focus on security, privacy, and encryption around data sharing from wearable, IoT, and AAL technology. In parallel, we will initiate the development of an AAL data exchange infrastructure modelling that will identify: (1) the data sharing requirements for such platform, (2) how we could develop this platform in the upcoming years, and (3) the necessary infrastructure needed to collect and aggregate this data from IoT and wearable technology. Ultimately, we would establish a platform to centralize AAL, IoT, mHealth, and wearable data for population-level and remote patient monitoring.

      Conclusion/Implications/Recommendations: IoT and AAL technologies are here to help and support the aging population in Canada. These technologies enable continuous and unobtrusive data collection at home, empowering the healthcare system to monitor patients remotely. Sharing this data will be relevant for the development of precision medicine models and aging related health research. In this project, we aim at developing a better understanding of how we can make Canada a leader in the IoT+ mHealth data integration by creating a roadmap for the development of this data-sharing infrastructure and the affiliated standards to guide the process while ensuring data encryption, security and privacy are kept in mind.

      140 Character Summary: Explore how Ambient Assisted Living technologies share data generated by sensors, suggesting future policies, guidelines, and standards to support innovators.

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