Implementation of a SVM on an Embedded System: A Case Study on Fall Detection
Journal
Advances in Soft Computing
Lecture Notes in Computer Science
ISSN
0302-9743
1611-3349
Date Issued
2020
Author(s)
Márquez Ordaz, Luis Eusebio
Type
text::book::book part
Abstract
Edge Computing seeks to bring Machine Learning as close as possible to the source events of interest, providing an almost instant interpretation to data acquired by sensors giving sense to raw data while addressing concerns of particular applications such as latency, privacy and server stress relieve. Due to a lack of research on this particular type of application, we are faced with difficulties both in software and hardware as embedded systems are known to possess serious limitations on its available processing resources. To address this, we make use of the concepts of edge computing and offline programming to accomplish a reliable machine learning model deployment on the microprocessor. By studying real case problem, we can get measurements on the resources required by such an application as well as its performance. In this study, we address the implementation of such an application in an embedded system focusing on the detection of human falls. © 2020, Springer Nature Switzerland AG.
How to cite
Márquez-Ordaz L., Ponce H. (2020) Implementation of a SVM on an Embedded System: A Case Study on Fall Detection. In: Martínez-Villaseñor L., Herrera-Alcántara O., Ponce H., Castro-Espinoza F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science, vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_6