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Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm

Title
Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm
Author
김기범
Keywords
Human activity analysis; Medical fitness; Mel-frequency cepstral coefficient; Sensor technologies
Issue Date
2019-08
Publisher
IEEE
Citation
2019 International Conference on Applied and Engineering Mathematics (ICAEM), Article no. 8853770, Page. 145-150
Abstract
The rapid growth of wearable sensors have increased the importance of human activity analysis in different areas of information technologies. Motion artifacts often degrade the performance of wearable sensors. Several wearable sensors have been used since the last decades in order to recognize physical activity detection. The wearable sensors could have numerous applications in medical and daily life routine activities like human gait analysis, health care, fitness, etc. In this paper, accelerometer and gyroscope sensors dataset has been used to propose an efficient model for physical activity detection. We designed a new feature extraction algorithm, Mel-frequency cepstral coefficient and statistical features to extract valuable features. Then, classification of different daily life activities is performed via Particle Swarm Optimization (PSO) together with SVM algorithm over bench mark motion-sense dataset. The results of our model shows that pre-classifier as PSO and SVM along with feature extraction module excel in term of accuracy and efficiency. Our experimental results have shown accuracy of 87.50% over motion-sense dataset. This model is recommended for the system associating in physical activity detection, especially in medical fitness field.
URI
https://ieeexplore.ieee.org/document/8853770https://repository.hanyang.ac.kr/handle/20.500.11754/125308
ISBN
978-172812353-0
DOI
10.1109/ICAEM.2019.8853770
Appears in Collections:
COLLEGE OF COMPUTING[E] > MEDIA, CULTURE, AND DESIGN TECHNOLOGY(ICT융합학부) > Articles
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