Abstract
The expeditious market transformation to smart portable devices has created an opportunity to support activity recognition using the embedded sensors of these devices. Over the last decade, many activity recognition approaches have been proposed for various activities in different settings. The motion mode recognition or transition in modes of the device is needed in many technological domains. This approach detects a variety of motion modes for a human using a portable device. The approach includes many aspects: usability, mounting and data acquisition, sensors used, signal processing, methods employed, features extracted, and classification techniques. This chapter sums up with a comparison of the performance of several motion mode recognition techniques. In this research, multiple behaviors were distinguished using embedded inertial sensors in portable smart devices. In our experiments, we selected four types of human activity, which are walking, standing, sitting, and running. A combination of one of the embedded mobile sensors and machine learning techniques have been proposed in order to do this kind of classification. The proposed system relies on accelerometer data to classify user activities. The results show that using SVM classifier showed better accuracy for detection compared to the outcomes of the other classifiers like KNN and ensemble classifiers. For future work, classification of other human activities like cycling, driving, and swimming will be investigated.
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Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
In addition, all the human figures provided are for me “Omar Sheishaa” while performing the experiments.
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Sheishaa, O., Tamazin, M., Morsi, I. (2020). A Context-Aware Motion Mode Recognition System Using Embedded Inertial Sensors in Portable Smart Devices. In: Farouk, M., Hassanein, M. (eds) Recent Advances in Engineering Mathematics and Physics. Springer, Cham. https://doi.org/10.1007/978-3-030-39847-7_23
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DOI: https://doi.org/10.1007/978-3-030-39847-7_23
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