Human activity recognition with smart watch based on H-SVM

Tao Tang, Lingxiang Zheng, Shaolin Weng, Ao Peng, Huiru Zheng

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.

    LanguageEnglish
    Title of host publicationFrontier Computing - Theory, Technologies and Applications, FC 2016
    Pages179-186
    Number of pages8
    Volume422
    DOIs
    Publication statusE-pub ahead of print - 27 Sep 2017
    Event 5th International Conference on Frontier Computing, FC 2016 - Tokyo, Japan
    Duration: 13 Jul 201615 Jul 2016

    Publication series

    NameLecture Notes in Electrical Engineering
    Volume422
    ISSN (Print)1876-1100
    ISSN (Electronic)1876-1119

    Conference

    Conference 5th International Conference on Frontier Computing, FC 2016
    CountryJapan
    CityTokyo
    Period13/07/1615/07/16

    Fingerprint

    Watches
    Support vector machines
    Experiments
    Accelerometers
    Energy conservation
    Classifiers
    Sampling
    Sensors

    Keywords

    • H-SVM
    • Human activity recognition
    • Smart watch

    Cite this

    Tang, T., Zheng, L., Weng, S., Peng, A., & Zheng, H. (2017). Human activity recognition with smart watch based on H-SVM. In Frontier Computing - Theory, Technologies and Applications, FC 2016 (Vol. 422, pp. 179-186). (Lecture Notes in Electrical Engineering; Vol. 422). https://doi.org/10.1007/978-981-10-3187-8_19
    Tang, Tao ; Zheng, Lingxiang ; Weng, Shaolin ; Peng, Ao ; Zheng, Huiru. / Human activity recognition with smart watch based on H-SVM. Frontier Computing - Theory, Technologies and Applications, FC 2016. Vol. 422 2017. pp. 179-186 (Lecture Notes in Electrical Engineering).
    @inproceedings{18fb613bf17f4c0eb360243ee6b975f6,
    title = "Human activity recognition with smart watch based on H-SVM",
    abstract = "Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99{\%}.",
    keywords = "H-SVM, Human activity recognition, Smart watch",
    author = "Tao Tang and Lingxiang Zheng and Shaolin Weng and Ao Peng and Huiru Zheng",
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    Tang, T, Zheng, L, Weng, S, Peng, A & Zheng, H 2017, Human activity recognition with smart watch based on H-SVM. in Frontier Computing - Theory, Technologies and Applications, FC 2016. vol. 422, Lecture Notes in Electrical Engineering, vol. 422, pp. 179-186, 5th International Conference on Frontier Computing, FC 2016, Tokyo, Japan, 13/07/16. https://doi.org/10.1007/978-981-10-3187-8_19

    Human activity recognition with smart watch based on H-SVM. / Tang, Tao; Zheng, Lingxiang; Weng, Shaolin; Peng, Ao; Zheng, Huiru.

    Frontier Computing - Theory, Technologies and Applications, FC 2016. Vol. 422 2017. p. 179-186 (Lecture Notes in Electrical Engineering; Vol. 422).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AB - Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.

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    Tang T, Zheng L, Weng S, Peng A, Zheng H. Human activity recognition with smart watch based on H-SVM. In Frontier Computing - Theory, Technologies and Applications, FC 2016. Vol. 422. 2017. p. 179-186. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-10-3187-8_19