Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach

Lin, Yu Cheng; Wang, Yu Cheng; Chen, Tin Chih Toly; Lin, Hai Fen

doi:10.3390/math7111097

Cited by 57 publications

(37 citation statements)

References 46 publications

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“…The proposed methodology begins by selecting membership functions of the Gaussian type, such as those shown in Equation (9). The standard deviation values of the membership functions are obtained from Equation 10and the corresponding levels of the DOE will be used for the mean values of the membership functions, as shown in Equation (11). Since the DOE has four levels for the input variables, as shown in Table 1, four Gaussian functions will be employed, as shown in Equations (9)- (11).…”

Section: Proposal Of a Methods For Obtaining An Adaptive Neuro-fuzzy Imentioning

confidence: 99%

“…On the other hand, Wang et al [10] employed a fuzzy multicriteria decision-making model (MCDM) for raw material supplier selection in the plastic industry. Likewise, Lin et al [11] applied fuzzy collaborative intelligence approach for fall detection in four existing smart technology applications and a methodology for obtaining technological mean roughness (Ra) for the EDM process, Alarifi et al [42] employed genetic algorithms and particle swarm optimization to determine the parameters of an ANFIS model to predict the thermo-physical properties of Al 2 O 3 -MWCNT/thermal oil hybrid nanofluid and an analysis of the PSO implementation in designing parameters of manufacturing processes as well as a benchmark with other optimization techniques can be found in the review study of Sibalija [43]. On the other hand, Alajmi et al [44] used an ANFIS-QPSO to predict the surface roughness of the dry and cryogenic turning process of AISI 304 stainless steel.…”

Section: State Of the Artmentioning

confidence: 96%

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A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.

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Section: Proposal Of a Methods For Obtaining An Adaptive Neuro-fuzzy Imentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 96%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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“…(2) Hidden layer: Many studies have shown that a single hidden layer is sufficient to fit complex nonlinear relationships [42]. The number of nodes in the hidden layer is twice the number of inputs [43,44].…”

Section: Back Propagating Network To Defuzzify the Aggregationmentioning

confidence: 99%

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

Self Cite

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Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.

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“…In addition, a judgment modification mechanism is also incorporated in the FGM-FAHP approach, so that an expert can modify his/her judgment subjectively if that deviates considerably from the consensus, which was not considered in [17] . Further, experts can be of unequal authority levels, so that the decision-making process can be led by an authoritative professional, which is another novelty of this study when compared to earlier studies [17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Assessing factors critical to smart technology applications to mobile health care − the fgm-fahp approach

Chen

2020

Health Policy and Technology

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a b s t r a c tSmart technologies present numerous opportunities for enhancing mobile health care. However, some concerns regarding the viability of smart technology applications must be addressed. This study investigated these concerns by reviewing the current practices of smart technology applications to mobile health care. As a result, five factors critical to the applicability of a smart technology to mobile health care are identified, and the fuzzy geometric mean-fuzzy analytic hierarchy process (FGM-FAHP) approach is proposed to assess the relative importance levels of the identified factors. The experimental results showed that the three most critical factors identified include: (a) the relaxation of the related medical laws; (b) unobtrusiveness; and (c) the precise need and situation of a user. Accordingly, approximately 44%, 26%, and 15% of the budget should be allocated to the realization of the three critical factors, respectively. In addition, the challenges involved and opportunities for enhancing the effectiveness of existing applications are discussed.

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Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach

Cited by 57 publications

References 46 publications

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

Assessing factors critical to smart technology applications to mobile health care − the fgm-fahp approach

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