Sergio Staab, Johannes Luderschmidt, Ludger Martin
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Consultation with several care teams that work with dementia patients on a daily basis revealed that many dementia patients wear watches.In this project, data from the aforementioned sensors is sent to the database at 20 data packets per second via a socket. DecisionTreeClassifier, KNeighborsClassifier, Logistic Regression, Fast Forest, Support Vector Machine, and Multilayer Perceptron classification algorithms are used to gain knowledge about locating, providing, and documenting motor skills during the course of dementia. The performance of the aforementioned algorithms for three similar activities of the dementia patients – writing, drinking and eating – will be investigated. The aim is to show the performance with which the activities can be recognized and how this knowledge can be used to support dementia documentation by nursing staff.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Recognition of Usual Similar Activities of Dementia Patients via Smartwatches Using Supervised Learning\",\"authors\":\"Sergio Staab, Johannes Luderschmidt, Ludger Martin\",\"doi\":\"10.1109/PIC53636.2021.9687025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, about 46.8 million people worldwide have dementia. More than 7.7 million new cases occur every year. Causes and triggers of the disease are currently unknown and a cure is not available. 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The performance of the aforementioned algorithms for three similar activities of the dementia patients – writing, drinking and eating – will be investigated. 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引用次数: 6
摘要
目前,全世界约有4680万人患有痴呆症。每年新发病例超过770万例。该疾病的病因和触发因素目前尚不清楚,也无法治愈。这使得痴呆症和癌症一起成为世界上最危险的疾病之一。在痴呆症护理领域,这项工作试图使用机器学习对痴呆症患者的活动进行分类,以便跟踪和分析疾病进展,并尽早发现与疾病相关的变化。与两个护理社区合作,使用Apple Watch Series 6测量运动数据。与几个每天与痴呆症患者打交道的护理团队进行磋商后发现,许多痴呆症患者都戴手表。在本项目中,上述传感器的数据通过套接字以每秒20个数据包的速度发送到数据库。决策树分类器、KNeighborsClassifier、逻辑回归、快速森林、支持向量机和多层感知器分类算法被用于获取关于痴呆过程中运动技能的定位、提供和记录的知识。将研究上述算法在痴呆症患者的三种类似活动(写作、饮酒和饮食)中的表现。其目的是展示可以识别活动的表现,以及如何利用这些知识来支持护理人员记录痴呆症。
Recognition of Usual Similar Activities of Dementia Patients via Smartwatches Using Supervised Learning
Currently, about 46.8 million people worldwide have dementia. More than 7.7 million new cases occur every year. Causes and triggers of the disease are currently unknown and a cure is not available. This makes dementia, along with cancer, one of the most dangerous diseases in the world. In the field of dementia care, this work attempts to use machine learning to classify the activities of individuals with dementia in order to track and analyze disease progression and detect disease-related changes as early as possible. In collaboration with two care communities, exercise data is measured using the Apple Watch Series 6. Consultation with several care teams that work with dementia patients on a daily basis revealed that many dementia patients wear watches.In this project, data from the aforementioned sensors is sent to the database at 20 data packets per second via a socket. DecisionTreeClassifier, KNeighborsClassifier, Logistic Regression, Fast Forest, Support Vector Machine, and Multilayer Perceptron classification algorithms are used to gain knowledge about locating, providing, and documenting motor skills during the course of dementia. The performance of the aforementioned algorithms for three similar activities of the dementia patients – writing, drinking and eating – will be investigated. The aim is to show the performance with which the activities can be recognized and how this knowledge can be used to support dementia documentation by nursing staff.