Inferring Destinations and Activity Types of Older Adults from GPS Data: Proof of Concept
ABSTRACT
Background:
Outdoor mobility is an important aspect of older adults’ functional status. Global positioning systems (GPS) have been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multi-dimensional construct, and there is as of yet no classification algorithm that groups and characterizes older adult’s outdoor mobility based on its semantic aspects (mobility intentions and motivations) by integrating geographic and domain knowledge.
Objective:
This study assesses the feasibility of using GPS to determine semantic dimensions of older adults’ outdoor mobility including destinations and activity types.
Methods:
Five healthy individuals aged 65 years or older carried a GPS device when traveling outside their homes for 4 weeks. The participants were also given a travel diary to record details describing all excursions from their home, including time and destination information. We first designed and implemented an algorithm to extract destinations and infer activity types (e.g. food, shopping, and sport) from the GPS data. We then evaluated the performance of the GPS-derived destination and activity information against the traditional diary method.
Results:
Our results detected the stop location of older adults from their GPS data with a F1 score of 87%. On average, the extracted home locations were within a 40.18 ±1.18 m distance of the actual home locations. For the activity inference algorithm, our results reached a F1 score of 86% for all participants, suggesting a reasonable accuracy against the travel diary recordings. Our results also suggest that the activity inference’s accuracy measure differed by neighborhood characteristics (i.e. walk score).
Conclusions:
We conclude that the GPS technology is accurate for determining semantic dimensions of outdoor mobility. However, further improvements may be needed to develop a robust application of this method that is suitable for clinical practices.
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