Faculty
Computer Science
Supervisor Name
Dr. Michael Bauer
Keywords
Data Visualization, Python, Healthcare, Polar Plot, Weighted Random Sample, Reservoir Sampling, Data Science, Data Insights, Behavior Recognition, Motion Sensors, Senior Care
Description
In Canada, approximately 18 percent (6.6 million) of the total population are age 65 or older, and 88 percent of people over age 65 want to stay in their residence for as long as possible. This older demographic is a group that is dependent on proactive and preventative healthcare. Using motion sensor data collected from a local company providing home-care services to this demographic, a data visualization was constructed to assist users in observing patient behavior and improving their quality of life while maintaining their independence. However, since the collected data is time-based, it results in a dataset that is too large to plot and determine behavior from. The goal of this project was to take an existing polar plot visualization created from the data over a period of one month and scale it to display data over a larger time frame of at least 6 months, allowing us to determine patient behavioral changes over time. This was achieved by using a weighted random sampling method and implemented using Python, Pandas, PlotlyExpress and Dash. As a result of this project, weighted random sampling enabled the viewer to determine behavioral changes and abnormalities using the polar plot visualization over a period of 6 months.
Acknowledgements
I would like to thank Dr. Michael Bauer for taking me on as a research intern this summer. His expertise and experience were valuable in the development of this project as well as my own personal development. I would also like to thank the department of science and Dr. Bauer again for providing funding for this opportunity as a USRI student.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Document Type
Poster
Previous Versions
Included in
Monitoring at-home care patients through a scalar polar plot visualization of motion sensor data
In Canada, approximately 18 percent (6.6 million) of the total population are age 65 or older, and 88 percent of people over age 65 want to stay in their residence for as long as possible. This older demographic is a group that is dependent on proactive and preventative healthcare. Using motion sensor data collected from a local company providing home-care services to this demographic, a data visualization was constructed to assist users in observing patient behavior and improving their quality of life while maintaining their independence. However, since the collected data is time-based, it results in a dataset that is too large to plot and determine behavior from. The goal of this project was to take an existing polar plot visualization created from the data over a period of one month and scale it to display data over a larger time frame of at least 6 months, allowing us to determine patient behavioral changes over time. This was achieved by using a weighted random sampling method and implemented using Python, Pandas, PlotlyExpress and Dash. As a result of this project, weighted random sampling enabled the viewer to determine behavioral changes and abnormalities using the polar plot visualization over a period of 6 months.