Thanks to the increase in processing power and in the number of sensors present in today's mobile devices, context-aware applications have gained a renewed interest. This paper focuses on a particular type of context, the transportation mode used by a person for carbon footprint estimation and it summarizes a method for automatically classifying different transportation modes with a smartphone. The method was evaluated with real data presenting promising results: a performance of around 94% was obtained when classifying 7 different classes with a random forest followed by a Discrete Hidden Markov Model (DHMM) filtering and accelerometer and magnetometer based features while the addition of the GPS improved the performance up to 96%.


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    Title :

    Transportation mode recognition based on smartphone embedded sensors for carbon footprint estimation


    Contributors:


    Publication date :

    2016-11-01


    Size :

    517044 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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