Abstract
Data generated by human locomotion activities is a process that involves the analysis of hundreds or thousands of data in a reduced time, due to the very nature of the signals generated and the techniques for implementing classification models and event detection. It is advisable to try to reduce the number of characteristics or select the most important elements of the captured signals showing, in this paper, the use of principal component analysis (PCA) techniques. The use of machine learning techniques for reduced hardware devices in intelligent environments, allows generating a solution for the non-invasive supervision of activities, complementing the use of PCA with other classification algorithms suitable for the treatment of data with a high number of characteristics such as support vector machines (SVM). Therefore, the evaluation of PCA processes and SVM algorithms is shown, selecting the one that has the best performance during its implementation in IoT devices with low hardware resources. Finally, it is considered that the memory space consumed in the IoT device and the execution time of the processes are critical elements to make the comparison and contrast of the PCA models, allowing to select and develop a reliable and efficient model in small devices of IoT.
Original language | English |
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Title of host publication | Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665444453 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Peru Duration: 27 Oct 2021 → 29 Oct 2021 |
Publication series
Name | Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021 |
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Conference
Conference | 2nd IEEE Engineering International Research Conference, EIRCON 2021 |
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Country/Territory | Peru |
City | Virtual, Lima |
Period | 27/10/21 → 29/10/21 |
Bibliographical note
Funding Information:This research was developed in the laboratories of INICTELUNI (National Institute of Research and Training in Telecommunications - National University of Engineering) and as part of Doctoral studies at the Faculty of Systems Engineering and Informatics in UNMSM (Universidad Nacional Mayor de San Marcos)
Publisher Copyright:
© 2021 IEEE.
Keywords
- Edge computing
- Embedded System
- Internet of Thing
- Principal Component Analysis
- Support Vector Machine
- Tiny Machine Learning