Resource consumption analysis of online activity recognition on mobile phones and smartwatches

M. Shoaib, O. Durmaz, Paul J.M. Havinga, J. Scholten

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    Abstract

    Most of the studies on human activity recognition using smartphones and smartwatches are performed in an offline manner. In such studies, collected data is analyzed in machine learning tools with less focus on the resource consumption of these devices for running an activity recognition system. In this paper, we analyze the resource consumption of human activity recognition on both smartphones and smartwatches, considering six different classifiers, three
    different sensors, different sampling rates and window sizes. We study the CPU, memory and battery usage with different parameters, where the smartphone is used to recognize seven physical activities and the smartwatch is used to recognize smoking activity. As a result of this analysis, we report that
    classification function takes a very small amount of CPU time out of total app’s CPU time while sensing and feature calculation consume most of it. When an additional sensor is used besides an accelerometer, such as gyroscope, CPU
    usage increases significantly. Analysis results also show that increasing the window size reduces the resource consumption more than reducing the sampling rate. As a final remark, we observe that a more complex model using only the accelerometer is a better option than using a simple model with both accelerometer and gyroscope when resource usage is to be reduced.
    Original languageEnglish
    Publication statusPublished - 11 Dec 2017
    Event36th IEEE -- International Performance Computing and Communications Conference - Bahia Resort Hotel, San Diego, United States
    Duration: 10 Dec 201712 Dec 2017

    Conference

    Conference36th IEEE -- International Performance Computing and Communications Conference
    Abbreviated titleIPCCC 2017
    CountryUnited States
    CitySan Diego
    Period10/12/1712/12/17

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    Keywords

    • Mobile
    • performance analysis
    • Activity recognition
    • sensors
    • smartwatch sensors

    Cite this

    Shoaib, M., Durmaz, O., Havinga, P. J. M., & Scholten, J. (2017). Resource consumption analysis of online activity recognition on mobile phones and smartwatches. Paper presented at 36th IEEE -- International Performance Computing and Communications Conference, San Diego, United States.