{"title":"Proposal for a framework for optimizing artificial environments based on physiological feedback.","authors":"Hideyuki Takagi, Shangfei Wang, Shota Nakano","doi":"10.2114/jpa.24.77","DOIUrl":null,"url":null,"abstract":"<p><p>We propose and then evaluate a new framework for finding the physical parameters of an artificial environment which give rise to given target physiological characteristics. We assume that a human is a system that takes as inputs the physical parameters of an artificial environment and outputs physiological parameters in response. We define our task as the inverse problem; we must find the best inputs from given target outputs. Our proposed framework solves the inverse problem using evolutionary computation techniques to optimize an artificial environment. We evaluate this framework using a simulation with a vibration environment and verify that it works.</p>","PeriodicalId":80293,"journal":{"name":"Journal of physiological anthropology and applied human science","volume":"24 1","pages":"77-80"},"PeriodicalIF":0.0000,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2114/jpa.24.77","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of physiological anthropology and applied human science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2114/jpa.24.77","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
We propose and then evaluate a new framework for finding the physical parameters of an artificial environment which give rise to given target physiological characteristics. We assume that a human is a system that takes as inputs the physical parameters of an artificial environment and outputs physiological parameters in response. We define our task as the inverse problem; we must find the best inputs from given target outputs. Our proposed framework solves the inverse problem using evolutionary computation techniques to optimize an artificial environment. We evaluate this framework using a simulation with a vibration environment and verify that it works.