Hiroki Kojima, M. Kitano, K. Yokota, Sho Ooi, Mutsuo Sano
{"title":"Cleaning Behavior Estimation for Self-Supported Cognitive Rehabilitation System","authors":"Hiroki Kojima, M. Kitano, K. Yokota, Sho Ooi, Mutsuo Sano","doi":"10.1109/IICAIET.2018.8638470","DOIUrl":null,"url":null,"abstract":"People suffering from brain injury can present complications such as attention disorder, memory disorder, functional disorder, and aphasia, which limit their capacity to perform activities of daily living. We aim to implement a system for cognitive rehabilitation based on daily routines. For instance, we previously reported the benefits of cognitive rehabilitation through cooking. In this paper, we focus on cognitive rehabilitation based on cleaning tasks, which are as important as cooking for daily living. We tested the system with the help of a patient from the Osaka Prefecture Self-Reliance Center for People with Disabilities on November 9, 16 and 30, 2017. We propose and test a system that detects some cleaning tools and a human skeleton to assess the patients' behavior while cleaning, and rehabilitation is guided using indoor navigation. Object recognition allowed to integrate information about the cleaning tools to determine different cleaning behaviors. In addition, we tracked the patient to determine her state from a library of five states for the recognized skeleton in different cleaning areas. By detecting the cleaning tools and person's state, we estimated the cleaning behavior. As state estimation may retrieve a high rate of misidentification of the person's state, we implemented an error correction algorithm for estimating the cleaning behavior based on previous states, thus improving the system accuracy. We evaluated the university environment before testing the system, and determined the accuracy of both object recognition and person's state for estimating the cleaning behavior.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICAIET.2018.8638470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
People suffering from brain injury can present complications such as attention disorder, memory disorder, functional disorder, and aphasia, which limit their capacity to perform activities of daily living. We aim to implement a system for cognitive rehabilitation based on daily routines. For instance, we previously reported the benefits of cognitive rehabilitation through cooking. In this paper, we focus on cognitive rehabilitation based on cleaning tasks, which are as important as cooking for daily living. We tested the system with the help of a patient from the Osaka Prefecture Self-Reliance Center for People with Disabilities on November 9, 16 and 30, 2017. We propose and test a system that detects some cleaning tools and a human skeleton to assess the patients' behavior while cleaning, and rehabilitation is guided using indoor navigation. Object recognition allowed to integrate information about the cleaning tools to determine different cleaning behaviors. In addition, we tracked the patient to determine her state from a library of five states for the recognized skeleton in different cleaning areas. By detecting the cleaning tools and person's state, we estimated the cleaning behavior. As state estimation may retrieve a high rate of misidentification of the person's state, we implemented an error correction algorithm for estimating the cleaning behavior based on previous states, thus improving the system accuracy. We evaluated the university environment before testing the system, and determined the accuracy of both object recognition and person's state for estimating the cleaning behavior.