Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Tracking daily activity using smart phones
Proceedings of the 6th International Conference on Rehabilitation Engineering & Assistive Technology
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At last year conference (i-CREATe 2012), we presented the android application that classified our daily physical and commuting activities. It was shown that the classification resulted in good accuracy (higher than 95% on average) and with reasonable battery consumption. We extend our previous work and focus on physical activity detection. We modify in this current work our application to classify into activities: sleep, rest, walk and run. Then we convert the activity to energy expenditure using MET data published in the compendium of physical activities tracking guide. We also provide the following relevant information: duration of each activity, step counts and distance obtained with walk and run activities. This tool can be used to automatically provide information on user's daily pattern of physical activity. We perform several tests of performance and show that although the application depends on several factors, it works very well in most situations.