Track-Me-Fit: Inferring User Activity through Mobile Phones’ Data
Fitness activity tracking mobile applications have become increasingly popular over the past decade, offering the end user various insights into their habits, fitness levels and progress towards goals. While many mobile devices constantly track steps and heart rate, there is no established means to infer the exact fitness activity a user was performing at any given moment, or an application dedicated to identifying fitness activities with no user intervention. This paper discusses a means to infer user fitness activities. Rather than relying on wearable devices, the Track-Me-Fit application uses data readily available on a iOS mobile phone. The application offers a very convenient and accurate method to tracking fitness activities including walking, running and cycling.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Barrett, T. J., and Rattadilok, P. (2017) Track-Me-Fit: Inferring User’s Activities from iOS Data. International Conference on Internet of Things and Machine Learning.
Peer Reviewed : Yes