Fuzzy membership functions based on point-to-polygon distance evaluation

Liparulo, Luca; Proietti, Andrea; Panella, Massimo

doi:10.1109/fuzz-ieee.2013.6622449

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…In [24], an early application of this approach has been investigated, considering the Min-Max clustering algorithm with the aim to verify that the proposed MF might be a useful alternative to the Min-Max original one. The results proved that the suggested MF evaluation is worthy and useful when dealing with clustering tasks.…”

Section: The Adopted Membership Functionsmentioning

confidence: 99%

Improved online fuzzy clustering based on unconstrained kernels

Liparulo

Proietti

Panella

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation. Since the performance of online algorithms suffers from the pattern presentation order, we also consider the problem of cluster validity aiming at proving the minimal dependence and the robustness with respect to the initialization of inner parameters in the proposed algorithm. The numerical results reported in the paper prove that the proposed approach is able to improve the performances of well-known algorithms on some reference benchmarks.

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Section: The Adopted Membership Functionsmentioning

confidence: 99%

Improved online fuzzy clustering based on unconstrained kernels

Liparulo

Proietti

Panella

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Classification is a supervised modelling problem that, in many different applications, tries to determine the class of a new observed element by using a set of a priori established class labels [43,44]. Classification generally requires selecting a set of features representing the data, building up a model representing the data space (features space) and the different regions of decision, determining a suited metric for evaluating the similarity/dissimilarity amongst patterns [45].…”

Section: The Dust Classification Approachmentioning

confidence: 99%

Dust detection and analysis in museum environment based on pattern recognition

et al. 2015

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“…The proposed classifier establishes a set of fuzzy MFs to associate the patterns of each motion to the corresponding class. The used MFs, based on the geometrical representation of the data and point-to-polygon distance evaluation, have been presented in [34]. These MFs are constructed by taking regular polygons which cover all the patterns of each class and H kernel functions on both the vertices and the centroid of the corresponding polygon.…”

Section: The Proposed Fuzzy Motion Classifiermentioning

confidence: 99%

“…The system utilizes the geometrically unconstrained fuzzy membership functions previously discussed in [34]- [36]. The flexible shape of the membership function can effectively manage the motion class overlapping problem which is aggravated by stroke patients' irregular performance.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation

Zhang

Liparulo

Panella

et al. 2016

IEEE J. Biomed. Health Inform.

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Autonomous poststroke rehabilitation systems which can be deployed outside hospital with no or reduced supervision have attracted increasing amount of research attentions due to the high expenditure associated with the current inpatient stroke rehabilitation systems. To realize an autonomous systems, a reliable patient monitoring technique which can automatically record and classify patient's motion during training sessions is essential. In order to minimize the cost and operational complexity, the combination of nonvisual-based inertia sensing devices and pattern recognition algorithms are often considered more suitable in such applications. However, the high motion irregularity due to stroke patients' body function impairment has significantly increased the classification difficulty. A novel fuzzy kernel motion classifier specifically designed for stroke patient's rehabilitation training motion classification is presented in this paper. The proposed classifier utilizes geometrically unconstrained fuzzy membership functions to address the motion class overlapping issue, and thus, it can achieve highly accurate motion classification even with poorly performed motion samples. In order to validate the performance of the classifier, experiments have been conducted using real motion data sampled from stroke patients with a wide range of impairment level and the results have demonstrated that the proposed classifier is superior in terms of error rate compared to other popular algorithms.

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Fuzzy membership functions based on point-to-polygon distance evaluation

Cited by 11 publications

References 20 publications

Improved online fuzzy clustering based on unconstrained kernels

Improved online fuzzy clustering based on unconstrained kernels

Dust detection and analysis in museum environment based on pattern recognition

A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation

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