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

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

doi:10.1080/02533839.2014.981212

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…Additionally, they did not consider the intrinsic and extrinsic predisposing factors to produce recommendations or alerts that are customized according to the current state of the patient. In fact, 14 of the excluded studies focused on the classification of body postures or movements (e.g., [19][20][21][22][23][24][25][26]), which represents a very important subject in the monitoring of PU. Four of the excluded studies (e.g., [27][28][29][30]) addressed wound image analysis to characterize or classify PU.…”

Section: Discussion and Findingsmentioning

confidence: 99%

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.

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Section: Discussion and Findingsmentioning

confidence: 99%

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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“…Mostly, it included simple processing steps of the numerical raw data obtained from the sensors, but, mainly in the case of the pressure sensors, some effort was made by various authors to obtain higher level features from the original data matrix (e.g., [15,16]). There were different techniques applied, but the most common seems to be some the fuzzy logic-based technique to transform the raw data into more informative features [15,22,23]. From an overall analysis of the results provided by the articles for lying position classification, it does not appear to exist a meaningful difference in results that is correlated with the level of preprocessing.…”

Section: Discussion and Findingsmentioning

confidence: 99%

“…The first stage is in the preprocessing of data to produce higher level features. As stated in the previous paragraph, in this stage the processing was usually relatively simple, and only in a few cases more sophisticated algorithms (e.g., based in fuzzy logic) were used (e.g., [15,22,23]). The second stage, which was the one where the most significant effort was clearly placed in the analyzed articles, was the lying position classification stage.…”

Section: Discussion and Findingsmentioning

confidence: 99%

“…In article [23], an approach is presented in which, from pressure images gathered from a developed sensor pad system, an activity scoring mechanism was applied for differentiating rest and movement periods. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest with high pressure for pressure ulcer prevention.…”

Section: Data Extraction and Article Synthesismentioning

confidence: 99%

“…Visualization component-A major component of the information to be displayed in the visualization component is the pressure map returned by the sensor system (e.g., [20,22,23]). The lying position and possible alarms raised by the sensor system should also be visualized.…”

Section: A General Architecture For An Intelligent Pu Prevention Systemmentioning

confidence: 99%

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A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention

et al. 2021

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Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.

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In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

et al. 2022

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

Cited by 10 publications

References 21 publications

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention

In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

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