dc.contributor.author |
Thu, Nguyen Thi |
|
dc.contributor.author |
Dao, To-Hieu |
|
dc.contributor.author |
Quoc, Bao Bo |
|
dc.contributor.author |
Tran, Duc-Nghia |
|
dc.contributor.author |
Thanh, Pham Van |
|
dc.contributor.author |
Tran, Duc-Tan |
|
dc.date.accessioned |
2022-03-09T12:39:03Z |
|
dc.date.available |
2022-03-09T12:39:03Z |
|
dc.date.issued |
2022-03-09 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/4608 |
|
dc.description.abstract |
Classification of activities from body-worn accelerometer data to help monitor and take care of health attracts much attention from the research community. This paper proposed to design a real-time monitoring device that can identify people's actions from the accelerometer's data worn on the waist with five activities, including lying, sitting, standing, walking, and jogging. From the collected acceleration data, it is necessary to extract suitable features for real-time classification with high performance. These features are trained with machine learning algorithms that improve the efficiency of action classification. Consequently, a decision tree algorithm was embedded in the microcontroller. This programmed waist-mounted device was connected to the monitoring system via WiFi protocol. Users could monitor activities and managed data on a computer, a website, or a smartphone. The results were optimistic when the overall accuracy for the activities dataset reached 99.3% when training and classifying the activities on the computer. When experimenting with real-time wearable devices, the overall accuracy when classifying activities decreased but was still very good, reaching over 90%. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Human activity |
en_US |
dc.subject |
classification |
en_US |
dc.subject |
machine learning |
en_US |
dc.subject |
decision tree |
en_US |
dc.subject |
webserver |
en_US |
dc.subject |
accelerometer |
en_US |
dc.title |
Real-Time Wearable-Device Based Activity recognition Using
Machine Learning Methods |
en_US |
dc.identifier.doi |
https://dx.doi.org/10.12785/ijcds/120126 |
|
dc.volume |
11 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
XXXX |
en_US |
dc.pageend |
XXXX |
en_US |
dc.contributor.authoraffiliation |
Hanoi University of Industry, Ha Noi 11307, Vietnam |
en_US |
dc.contributor.authoraffiliation |
Thai Nguyen University, University of Information And Communication Technology, Thai Nguyen 24119, Viet Nam |
en_US |
dc.contributor.authoraffiliation |
Vietnam Academy of Science and Technology, Ha Noi 11307, Vietnam |
en_US |
dc.contributor.authoraffiliation |
Institute of Information Technology, Vietnam Academy of Science and Technology, Ha Noi 11307, Viet Nam |
en_US |
dc.contributor.authoraffiliation |
University of Fire Prevention and Fighting, Hanoi, Vietnam |
en_US |
dc.contributor.authoraffiliation |
Faculty of Electrical and Electronic Engineering, Phenikaa University, Ha Noi 12116, Viet Nam |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |