English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone

He, Y., & Li, Y. (2013). Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone. International Journal of Distributed Sensor Networks, 2013: 481580, pp. 1-10. doi:10.1155/2013/481580.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
He, Yi1, Author              
Li, Y, Author
Affiliations:
1The Key Lab for Health Informatics of Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Physical activity (PA) recognition has recently become important in activity monitoring for the public healthcare. Although body-worn sensors are well suited to collect data on activity patterns for long periods of time, users may forget to wear special microsensors. On the contrary, more and more people take smartphone with them almost anytime. At present, most popular smartphones have three built-in kinematic sensors (triaccelerometer, gyroscope, and magnetic sensor) which could be utilized to recognize PA. This study utilized three built-in kinematic sensors in a smartphone to recognize PA and found out which features derived from the three sensors were significant to different PA. We used a combined algorithm of Fisher's discriminant ratio criterion and J3 criterion for feature selection. A hierarchical classifiers system including fourteen classifiers was proposed and employed to recognize fifteen activities. The optimal features derived from the built-in kinematic sensors of the smartphone were selected from 140 features. The results indicated that the accelerometer was significant to PA recognition, while gyroscope and orientation sensor were effective to recognize the change of body posture and detect falls, respectively. The total classification accuracy of 95.03% demonstrated the feasibility of utilizing the built-in kinematic sensors of the smartphone to recognize PA.

Details

show
hide
Language(s):
 Dates: 2013-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1155/2013/481580
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: International Journal of Distributed Sensor Networks
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 2013 Sequence Number: 481580 Start / End Page: 1 - 10 Identifier: -