Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering最新文献

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Complaint Handling Training VR System using Customer Agent 使用客户代理的投诉处理培训VR系统

Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering Pub Date : 2019-11-13 DOI: 10.1145/3375923.3375951

Satoru Fujita, Donghao Wang, K. Ookawara, Junichi Hoshino

引用次数: 0

Estimation of Gait Parameters from 3D Pose for Elderly Care 基于三维姿态的老年人护理步态参数估计

Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering Pub Date : 2019-11-13 DOI: 10.1145/3375923.3375943

Jyothsna Kondragunta, Ankit Jaiswal, G. Hirtz

{"title":"Estimation of Gait Parameters from 3D Pose for Elderly Care","authors":"Jyothsna Kondragunta, Ankit Jaiswal, G. Hirtz","doi":"10.1145/3375923.3375943","DOIUrl":"https://doi.org/10.1145/3375923.3375943","url":null,"abstract":"For elderly people, walking, standing up from a chair, turning and leaning are necessary for independent mobility. These mobilities such as gait depends on a complex interplay of major parts of the nervous, musculoskeletal and cardiorespiratory systems. Every individuals gait pattern is influenced by age, personality, mood, sociocultural factors and predominantly the persons health condition. In order to understand the health condition of an elderly person, analysis of gait patterns became an important aspect. Gait parameters such as cadence, step length, step duration etc. analyzed out of gait patterns proved as an important factor in estimation of the healthy daily living. For this purpose, gait data of several elderly individuals is collected many times over a period of time using Kinect sensor. The acquired data consist of RGB image sequences and depth data. From this data, 3D pose of the individual is identified. These 3D poses are used to extract the necessary gait parameters of the individual. The extracted gait parameters will be used in future to assess the health condition of the individual.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83755857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Exploring the Stability of Feature Selection Methods across a Palette of Gene Expression Datasets 探索跨基因表达数据集调色板特征选择方法的稳定性

Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering Pub Date : 2019-11-13 DOI: 10.1145/3375923.3375938

Zahra Mungloo-Dilmohamud, Y. Jaufeerally-Fakim, C. Peña-Reyes

{"title":"Exploring the Stability of Feature Selection Methods across a Palette of Gene Expression Datasets","authors":"Zahra Mungloo-Dilmohamud, Y. Jaufeerally-Fakim, C. Peña-Reyes","doi":"10.1145/3375923.3375938","DOIUrl":"https://doi.org/10.1145/3375923.3375938","url":null,"abstract":"Gene expression data often need to be classified into classes or grouped into clusters for further analysis, using different machine learning techniques and an important pre-processing step is feature selection (FS). The aim of this study is to investigate the stability of some diverse FS methods on a plethora of microarray gene expression data. This experimental work is broken into three parts. Step 1 involves running some FS methods on one gene expression dataset to have a preliminary assessment on the similarity, or dissimilarity, of the resulting feature subsets across methods. Step 2 involves running two of these methods on a large number of different datasets to investigate whether the results produced by the methods are dependent on the features of the dataset: binary, multiclass, small or large dataset. The final step explores how the similarity of selected feature subsets between pairs of methods evolves as the size of the subsets are increased. Results show that the studied methods display a high amount of variability in terms of the resulting selected features. The feature subsets differed both inter- and intra- methods for different datasets. The reason behind this is not clear yet and is being further investigated. The final objective of the research, that is to define how to select a FS method, is an ongoing work whose initial findings are reported herein.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81488699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

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