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Tactile Evaluation Feedback System for Multi-Layered Structure Inspired by Human Tactile Perception Mechanism

Tactile sensation is one type of valuable feedback in evaluating a product. Conventionally, sensory evaluation is used to get direct subjective responses from the consumers, in order to improve the product’s quality. However, this method is a time-consuming and costly process. Therefore, this paper...

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Detalles Bibliográficos
Autores principales: Mohamad Hashim, Iza Husna, Kumamoto, Shogo, Takemura, Kenjiro, Maeno, Takashi, Okuda, Shin, Mori, Yukio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712818/
https://www.ncbi.nlm.nih.gov/pubmed/29137128
http://dx.doi.org/10.3390/s17112601
Descripción
Sumario:Tactile sensation is one type of valuable feedback in evaluating a product. Conventionally, sensory evaluation is used to get direct subjective responses from the consumers, in order to improve the product’s quality. However, this method is a time-consuming and costly process. Therefore, this paper proposes a novel tactile evaluation system that can give tactile feedback from a sensor’s output. The main concept of this system is hierarchically layering the tactile sensation, which is inspired by the flow of human perception. The tactile sensation is classified from low-order of tactile sensation (LTS) to high-order of tactile sensation (HTS), and also to preference. Here, LTS will be correlated with physical measures. Furthermore, the physical measures that are used to correlate with LTS are selected based on four main aspects of haptic information (roughness, compliance, coldness, and slipperiness), which are perceived through human tactile sensors. By using statistical analysis, the correlation between each hierarchy was obtained, and the preference was derived in terms of physical measures. A verification test was conducted by using unknown samples to determine the reliability of the system. The results showed that the system developed was capable of estimating preference with an accuracy of approximately 80%.