Sensor-Based Activity Recognition Using Frequency Band Enhancement Filters and Model Ensembles
Author:
Tsutsumi Hyuga1, Kondo Kei1, Takenaka Koki1, Hasegawa Tatsuhito1
Affiliation:
1. Graduate School of Engineering, University of Fukui, Fukui 910-8507, Japan
Abstract
Deep learning methods are widely used in sensor-based activity recognition, contributing to improved recognition accuracy. Accelerometer and gyroscope data are mainly used as input to the models. Accelerometer data are sometimes converted to a frequency spectrum. However, data augmentation based on frequency characteristics has not been thoroughly investigated. This study proposes an activity recognition method that uses ensemble learning and filters that emphasize the frequency that is important for recognizing a certain activity. To realize the proposed method, we experimentally identified the important frequency of various activities by masking some frequency bands in the accelerometer data and comparing the accuracy using the masked data. To demonstrate the effectiveness of the proposed method, we compared its accuracy with and without enhancement filters during training and testing and with and without ensemble learning. The results showed that applying a frequency band enhancement filter during training and testing and ensemble learning achieved the highest recognition accuracy. In order to demonstrate the robustness of the proposed method, we used four different datasets and compared the recognition accuracy between a single model and a model using ensemble learning. As a result, in three of the four datasets, the proposed method showed the highest recognition accuracy, indicating the robustness of the proposed method.
Funder
Japan Society for the Promotion of Science
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference32 articles.
1. Prasad, A., Tyagi, A.K., Althobaiti, M.M., Almulihi, A., Mansour, R.F., and Mahmoud, A.M. (2021). Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network. Appl. Sci., 11. 2. Zhou, B., Yang, J., and Li, Q. (2019). Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network. Sensors, 19. 3. Robben, S., Pol, M., and Kröse, B. (2014, January 13–17). Longitudinal ambient sensor monitoring for functional health assessments: A case study. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp ‘14 Adjunct), Seattle, WA, USA. 4. Fridriksdottir, E., and Bonomi, A.G. (2020). Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network. Sensors, 20. 5. Haider, F., Salim, F.A., Postma, D.B.W., van Delden, R., Reidsma, D., van Beijnum, B.-J., and Luz, S. (2020). A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors. Multimodal Technol. Interact., 4.
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