Fuzzy Logic Decision Making for an Intelligent Home Healthcare System

Gatton, Thomas M.; Lee, Malrey

doi:10.1109/futuretech.2010.5482667

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…While in other systems the threshold is adapted from the fuzzificaiton process and can be dynamically changes throughout the course of the fuzzification process, so the resulting sets are considered to be of type-2 fuzzy sets [3]. Due to the huge amounts of data and the wide variations of their values and sources, recent researchers focus on adopting type-2 sets in the fuzzy logic process, as in the work in [5], where type-2 fuzzy sets were used in an automated decision making system for home health care of diabetes management of home treated patients.…”

Section: Related Workmentioning

confidence: 99%

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

Al-Ars

Al-Bakry

2019

TELKOMNIKA

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This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score.

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Section: Related Workmentioning

confidence: 99%

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

Al-Ars

Al-Bakry

2019

TELKOMNIKA

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“…Steps involved in supervised learning based on fuzzy logic approach [2] are : Determining the statistical parameters to select and measure the input variables; Linguistic levels or quantization levels are identified for each activity; Linguistic levels are represented using membership function; Membership function can be represented in triangular, Gaussian and trapezoidal functions; Rules are framed from the rule base matrix; The rule matrix is obtained by setting the ranges for membership function; The linguistic variables and rules are trained through neural network by adjusting the weights; Output state is achieved by applying fuzzy rules on inputs; Defuzzification is used for getting the crisp value.…”

Section: A a Neuro-fuzzy Logic Based Classificationmentioning

confidence: 99%

“…But only the classification has been done and no abnormal conditions are reported. Fuzzy-logic based decision-making system is proposed by Thomas M. Gatton et al, [2] at smart home for the patients suffering from diabetes. Fuzzy-logic based decisions are made more accurate by integrating the current health condition, the expected activities and behavior of the patients with the sensors in home environment.…”

Section: Introductionmentioning

confidence: 99%

Sensor based decision making inference system for remote health monitoring

Poorani

Ganapathy

Vaidehi

2012

2012 International Conference on Recent Trends in Information Technology

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Fall occurring in older people is of major concern in medical environment as they are more prone to unexpected and unpredictable falls. Such falls often leads to injury and death in elderly. Hence, an automated fall detection mechanism using multiple sensing and event detection methods are required to analyze the activities of elderly. This paper presents a system considering 3-axis accelerometer sensor to detect the fall in home environment. Intelligent modeling technique such as ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier is employed in this paper to detect the fall with reduced computational complexity and more accuracy. Using ANFIS, the data obtained from 3-axis accelerometer is classified under one of the five states (standing, sitting, walking, falling and lying) and backpropogation method is used for weight updation. Weighted average method which provides crisp value is used for de-fuzzification process. Features such as mean, median and standard deviation are considered for training the neural network. When the activity is recognized as fall, the patient heart rate and ECG are examined to detect abnormality and alarm is raised.

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Ambient Assisted Living: Systematic Review

Queirós

Rocha

2018

Human–Computer Interaction Series

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Fuzzy Logic Decision Making for an Intelligent Home Healthcare System

Cited by 11 publications

References 3 publications

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

Sensor based decision making inference system for remote health monitoring

Ambient Assisted Living: Systematic Review

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