Next Article in Journal
Numerical Investigation of the Effects of Red Blood Cell Cytoplasmic Viscosity Contrasts on Single Cell and Bulk Transport Behaviour
Next Article in Special Issue
Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features
Previous Article in Journal
Notch Effect on the Fatigue Behavior of a TC21 Titanium Alloy in Very High Cycle Regime
Previous Article in Special Issue
Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(9), 1615; https://doi.org/10.3390/app8091615

Pattern Recognition of Human Postures Using the Data Density Functional Method

1
Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 32001, Taiwan
2
Chronic Disease Research Center, National Central University, Taoyuan City 32001, Taiwan
*
Author to whom correspondence should be addressed.
Received: 18 August 2018 / Revised: 8 September 2018 / Accepted: 9 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
Full-Text   |   PDF [2991 KB, uploaded 11 September 2018]   |  

Abstract

In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring. View Full-Text
Keywords: cluster number; cluster boundary; data density functional; posture recognition; tri-axial gravitational sensor cluster number; cluster boundary; data density functional; posture recognition; tri-axial gravitational sensor
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Huang, S.-J.; Wu, C.-J.; Chen, C.-C. Pattern Recognition of Human Postures Using the Data Density Functional Method. Appl. Sci. 2018, 8, 1615.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top