Satoru Fujita, Donghao Wang, K. Ookawara, Junichi Hoshino
In many customer service industries, a hospitable reception is important to increasing customer satisfaction (CS). This is especially the case when handling complaints, due to psychological pressures not usually experienced elsewhere. In conventional training methods, such as on the job training (OJT), it is difficult to cover the variety of situations that may occur rarely. In this paper, we propose a multimodal conversational Virtual Reality (VR) training system that provides complaint handling training in various customer service scenarios. Claims situations are reproduced using a 3D customer agent with an emotional voice and gestures. Complaint handling skills and psychological resistance are compared through interpersonal role play with and without VR training. User study experiments show that psychological resistance can be reduced through repeat VR system training, leading to improvements in complaint handling skills.
{"title":"Complaint Handling Training VR System using Customer Agent","authors":"Satoru Fujita, Donghao Wang, K. Ookawara, Junichi Hoshino","doi":"10.1145/3375923.3375951","DOIUrl":"https://doi.org/10.1145/3375923.3375951","url":null,"abstract":"In many customer service industries, a hospitable reception is important to increasing customer satisfaction (CS). This is especially the case when handling complaints, due to psychological pressures not usually experienced elsewhere. In conventional training methods, such as on the job training (OJT), it is difficult to cover the variety of situations that may occur rarely. In this paper, we propose a multimodal conversational Virtual Reality (VR) training system that provides complaint handling training in various customer service scenarios. Claims situations are reproduced using a 3D customer agent with an emotional voice and gestures. Complaint handling skills and psychological resistance are compared through interpersonal role play with and without VR training. User study experiments show that psychological resistance can be reduced through repeat VR system training, leading to improvements in complaint handling skills.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88104099","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}
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.
{"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":null,"pages":null},"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}
Zahra Mungloo-Dilmohamud, Y. Jaufeerally-Fakim, C. Peña-Reyes
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.
{"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":null,"pages":null},"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}
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