Human Activity Recognition from Body Sensor Data using Deep Learning.
In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart hom...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 6; pp. 1 - 2 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129928830&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129928830 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2018 vid: 42 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129928830 129928830 129928830 10.1007/s10916-018-0948-z 129928830 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Human Activity Recognition from Body Sensor Data using Deep Learning. aug: au: Hassan, Mohammad Mehedi Huda, Shamsul Uddin, Md Zia Almogren, Ahmad Alrubaian, Majed affil: Chia of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia sug: subj: Wearable Sensors Physical Activity Machine Learning Human Accelerometers Neural Networks (Computer) Factor Analysis Pearson's Correlation Coefficient Funding Source ab: In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|