Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors
Joint Authors
He, Jie
Sun, Jian
Fu, Yongling
Li, Shengguang
Xu, Cheng
Tan, Lin
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods.
These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition.
With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency.
Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime.
All of these unique advantages make it superior to other HAR algorithms.
We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge.
Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%.
When compared with neural network using BP algorithm, testing time reduced by 38%.
American Psychological Association (APA)
Sun, Jian& Fu, Yongling& Li, Shengguang& He, Jie& Xu, Cheng& Tan, Lin. 2018. Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors. Journal of Sensors،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1202192
Modern Language Association (MLA)
Sun, Jian…[et al.]. Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors. Journal of Sensors No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1202192
American Medical Association (AMA)
Sun, Jian& Fu, Yongling& Li, Shengguang& He, Jie& Xu, Cheng& Tan, Lin. Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1202192
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202192