Bed posture classification based on artificial neural network using fuzzy c-means and latent semantic analysis

Yu Wei Hung, Yu Hsien Chiu, Yeong Chin Jou, Wei Hao Chen, Kuo Sheng Cheng

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Observation of physical activities and movement patterns is crucial in clinical practice. Current clinical protocols are based on periodic and subjective observations and self-reports. This paper aims to present an efficient monitoring framework for recognizing lying posture and monitoring on-bed activities to assist charting in order to improve patient safety and caregiving efficacy. From pressure images gathered from a developed sensor pad system, an activity scoring mechanism was applied for segmenting rest and movement periods. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest (ROI) with high pressure for pressure ulcer prevention. Latent semantic analysis (LSA) extracted the significant features from the transformed ROI images in order to develop an artificial neural network model for posture recognition. Several objective evaluations and a case study were performed to investigate performance. Experimental results show that the average posture recognition rate was 95.89% when the pressure distributions were divided into four clusters. FCM with LSA transformation improved the recognition rate and could be used to locate the corresponding risk regions of bony prominences. The prototype system also revealed encouraging potential in the production of continuous and quantitative information for assisted living charting nursing care.

Original languageEnglish
Pages (from-to)415-425
Number of pages11
JournalJournal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
Volume38
Issue number4
DOIs
Publication statusPublished - 2015 May 19

Fingerprint

Semantics
Neural networks
Nursing
Monitoring
Pressure distribution
Sensors

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

@article{3ff4e3bab4f44be293a43ebf7453234d,
title = "Bed posture classification based on artificial neural network using fuzzy c-means and latent semantic analysis",
abstract = "Observation of physical activities and movement patterns is crucial in clinical practice. Current clinical protocols are based on periodic and subjective observations and self-reports. This paper aims to present an efficient monitoring framework for recognizing lying posture and monitoring on-bed activities to assist charting in order to improve patient safety and caregiving efficacy. From pressure images gathered from a developed sensor pad system, an activity scoring mechanism was applied for segmenting rest and movement periods. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest (ROI) with high pressure for pressure ulcer prevention. Latent semantic analysis (LSA) extracted the significant features from the transformed ROI images in order to develop an artificial neural network model for posture recognition. Several objective evaluations and a case study were performed to investigate performance. Experimental results show that the average posture recognition rate was 95.89{\%} when the pressure distributions were divided into four clusters. FCM with LSA transformation improved the recognition rate and could be used to locate the corresponding risk regions of bony prominences. The prototype system also revealed encouraging potential in the production of continuous and quantitative information for assisted living charting nursing care.",
author = "Hung, {Yu Wei} and Chiu, {Yu Hsien} and Jou, {Yeong Chin} and Chen, {Wei Hao} and Cheng, {Kuo Sheng}",
year = "2015",
month = "5",
day = "19",
doi = "10.1080/02533839.2014.981212",
language = "English",
volume = "38",
pages = "415--425",
journal = "Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers",
issn = "0253-3839",
publisher = "Chinese Institute of Engineers",
number = "4",

}

TY - JOUR

T1 - Bed posture classification based on artificial neural network using fuzzy c-means and latent semantic analysis

AU - Hung, Yu Wei

AU - Chiu, Yu Hsien

AU - Jou, Yeong Chin

AU - Chen, Wei Hao

AU - Cheng, Kuo Sheng

PY - 2015/5/19

Y1 - 2015/5/19

N2 - Observation of physical activities and movement patterns is crucial in clinical practice. Current clinical protocols are based on periodic and subjective observations and self-reports. This paper aims to present an efficient monitoring framework for recognizing lying posture and monitoring on-bed activities to assist charting in order to improve patient safety and caregiving efficacy. From pressure images gathered from a developed sensor pad system, an activity scoring mechanism was applied for segmenting rest and movement periods. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest (ROI) with high pressure for pressure ulcer prevention. Latent semantic analysis (LSA) extracted the significant features from the transformed ROI images in order to develop an artificial neural network model for posture recognition. Several objective evaluations and a case study were performed to investigate performance. Experimental results show that the average posture recognition rate was 95.89% when the pressure distributions were divided into four clusters. FCM with LSA transformation improved the recognition rate and could be used to locate the corresponding risk regions of bony prominences. The prototype system also revealed encouraging potential in the production of continuous and quantitative information for assisted living charting nursing care.

AB - Observation of physical activities and movement patterns is crucial in clinical practice. Current clinical protocols are based on periodic and subjective observations and self-reports. This paper aims to present an efficient monitoring framework for recognizing lying posture and monitoring on-bed activities to assist charting in order to improve patient safety and caregiving efficacy. From pressure images gathered from a developed sensor pad system, an activity scoring mechanism was applied for segmenting rest and movement periods. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest (ROI) with high pressure for pressure ulcer prevention. Latent semantic analysis (LSA) extracted the significant features from the transformed ROI images in order to develop an artificial neural network model for posture recognition. Several objective evaluations and a case study were performed to investigate performance. Experimental results show that the average posture recognition rate was 95.89% when the pressure distributions were divided into four clusters. FCM with LSA transformation improved the recognition rate and could be used to locate the corresponding risk regions of bony prominences. The prototype system also revealed encouraging potential in the production of continuous and quantitative information for assisted living charting nursing care.

UR - http://www.scopus.com/inward/record.url?scp=84929282754&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929282754&partnerID=8YFLogxK

U2 - 10.1080/02533839.2014.981212

DO - 10.1080/02533839.2014.981212

M3 - Article

AN - SCOPUS:84929282754

VL - 38

SP - 415

EP - 425

JO - Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers

JF - Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers

SN - 0253-3839

IS - 4

ER -