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Article

Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

1
Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
2
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
3
School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(1), 139; https://doi.org/10.3390/electronics11010139
Submission received: 30 October 2021 / Revised: 20 December 2021 / Accepted: 31 December 2021 / Published: 3 January 2022
(This article belongs to the Special Issue Human Activity Recognition and Machine Learning)

Abstract

Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on ‘Raspberry Pi 3’ and ‘STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%.
Keywords: activity monitoring; machine learning; sensor fusion; SVM; edge-AI computing; energy-accuracy trade-off activity monitoring; machine learning; sensor fusion; SVM; edge-AI computing; energy-accuracy trade-off

Share and Cite

MDPI and ACS Style

Chang, J.; Kang, M.; Park, D. Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices. Electronics 2022, 11, 139. https://doi.org/10.3390/electronics11010139

AMA Style

Chang J, Kang M, Park D. Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices. Electronics. 2022; 11(1):139. https://doi.org/10.3390/electronics11010139

Chicago/Turabian Style

Chang, Juneseo, Myeongjin Kang, and Daejin Park. 2022. "Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices" Electronics 11, no. 1: 139. https://doi.org/10.3390/electronics11010139

APA Style

Chang, J., Kang, M., & Park, D. (2022). Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices. Electronics, 11(1), 139. https://doi.org/10.3390/electronics11010139

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