Human Activity Recognition Based on Convolutional Neural Network

8Citations
Citations of this article
27Readers
Mendeley users who have this article in their library.
Get full text

Abstract

It is increasingly essential to monitor clinical signs and physical activities of elderly, looking for early warning signs or to recognize abnormal situations, such as a fall. In recent years, the usage of wearable sensors has increased significantly. Data from wearable devices can be used to recognize human movement patterns while performing various activities. Accelerometers have been widely used in human activity recognition systems, however, instead of traditional techniques used for feature extraction, the scientific community is currently developing classifiers based on deep learning techniques, seeking better performance and lower computational cost. Convolutional neural networks (CNN) are the main deep learning technique used in this context. These networks adjust filter coefficients that are applied to small regions of the data, extracting local patterns and their variations. This paper presents a human activity recognition system based on convolutional neural networks to classify six activities—walking, running, walking upstairs, walking downstairs, standing and sitting—from accelerometer data. Results demonstrate the ability of the proposed CNN-based model to obtain a state-of-art performance, with accuracy of 94.89% and precision of 95.78% for the best configuration.

Cite

CITATION STYLE

APA

Coelho, Y., Rangel, L., dos Santos, F., Frizera-Neto, A., & Bastos-Filho, T. (2019). Human Activity Recognition Based on Convolutional Neural Network. In IFMBE Proceedings (Vol. 70, pp. 247–252). Springer. https://doi.org/10.1007/978-981-13-2517-5_38

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free