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Activity trackers have become ubiquitous nowadays, but are primarily designed for young adults. Older adults, specifically those with reduced mobility, are unable to take advantage of these tracker due to differing gait patterns. The purpose of this project is to design an activity tracker that would track when older adults are walking with their walker, and relay the information back to them in a way that is motivating.
The design is split into three components:
- Data Collection: A portable device that can be used by an older adult and measure measurements of interest and store them for later use. Built using an Arduino, Accelerometer and PSR Sensors.
- Processing (ML Classification Model): The model and data processing pipeline must take these measurements and develop a classifier to determine how long the older adult was walking using their walker during the duration of the measurement.
- Data Presentation: The information about the activity is passed on to the application, which uses the output of the model and showcases it in a way that is easy to understand by the older adult and acts as a positive reinforcement to being active.
The prototype was able to collect data, save it, transfer it wirelessly to an iPhone app, albeit slowly, and classify it to a high level of confidence. This classification was successfully relayed to the end user using an iPhone app, optimized for older adults.
Won the award for 'Best Overall Project' at the 2019 Capstone Design Symposium for Systems Design Engineering at University of Waterloo.