Simple and Efficient Home Health Monitoring using Spatiotemporal Data
Mentor 1
Dennis Tomashek
Location
Union Wisconsin Room
Start Date
5-4-2019 1:30 PM
End Date
5-4-2019 3:30 PM
Description
With recent technological advances, there are many new opportunities for home monitoring technologies. Some activity tracking methods that have been tested include: embedding sensors in the home to create a “smart-home” environment, activity logs, and asking individuals to wear body tracking sensors. While these home monitoring technologies exist, they tend to be invasive, high cost, and require significant technological setup in the home. We aim to create a prototype system to track activity in the home using simple time and location (spatiotemporal) data. This prototype will use a smartphone platform, be unobtrusive, be low cost, and have respect for personal privacy. Methods for this project include producing simulated daily schedules that track a person’s day in real-time to be used in a computer learning algorithm, creating a protocol for gathering daily time use data from real people using interviews, and work with computer scientists to create algorithms to identify the spatiotemporal variables of activities including sequence, frequency, and duration and location. We hope to confirm the success of our prototype in the lab and in real homes as well as use smartphone technology and personal inquiry to confirm the deductions of the spatiotemporal algorithm, conduct in-home usability and operational testing, and administer clinician and patient focus groups to confirm interpretations of the data.
Simple and Efficient Home Health Monitoring using Spatiotemporal Data
Union Wisconsin Room
With recent technological advances, there are many new opportunities for home monitoring technologies. Some activity tracking methods that have been tested include: embedding sensors in the home to create a “smart-home” environment, activity logs, and asking individuals to wear body tracking sensors. While these home monitoring technologies exist, they tend to be invasive, high cost, and require significant technological setup in the home. We aim to create a prototype system to track activity in the home using simple time and location (spatiotemporal) data. This prototype will use a smartphone platform, be unobtrusive, be low cost, and have respect for personal privacy. Methods for this project include producing simulated daily schedules that track a person’s day in real-time to be used in a computer learning algorithm, creating a protocol for gathering daily time use data from real people using interviews, and work with computer scientists to create algorithms to identify the spatiotemporal variables of activities including sequence, frequency, and duration and location. We hope to confirm the success of our prototype in the lab and in real homes as well as use smartphone technology and personal inquiry to confirm the deductions of the spatiotemporal algorithm, conduct in-home usability and operational testing, and administer clinician and patient focus groups to confirm interpretations of the data.
