Pub Date : 2023-01-01DOI: 10.5220/0011676300003414
Haruka Kamachi, Sae Ohkubo, Anna Yokokubo, G. Lopez
: Obesity may cause lifestyle diseases such as diabetes and high blood pressure. Eating slowly and chewing well are essential to prevent obesity. This research aims to improve the consciousness of dietary behavior based on eating habits by quantifying eating behavior. It proposes “ChewReminder,” a smartphone application software that detects eating activities in real-time under a natural meal environment and gives feedback based on detected activity. ChewReminder detects four activities: chewing, swallowing, talking, and other.The smartwatch gives feedback using vibration depend on chewing count per one bite which information was linked from the smartphone. Also, the total feedback about the meal was displayed on the smartphone after finishing the meal. The chewing count for 70% subjects and chewing pace for more than half subjects was improved with using ChewReminder by the result of total chewing count, average of chewing count per bite and chewing pace. ChewReminder is effective especially people who are aware of fast eating. Also, the result of long-term experiment indicated that feedback displayed on a smartphone was effective to improve consciousness of eating activity. Therefore, the result of both experiment shows that ChewReminder is a valid system to improve consciousness of eating activity especially chewing activity.
{"title":"Eating Habit Improvement System Using Dietary Sound","authors":"Haruka Kamachi, Sae Ohkubo, Anna Yokokubo, G. Lopez","doi":"10.5220/0011676300003414","DOIUrl":"https://doi.org/10.5220/0011676300003414","url":null,"abstract":": Obesity may cause lifestyle diseases such as diabetes and high blood pressure. Eating slowly and chewing well are essential to prevent obesity. This research aims to improve the consciousness of dietary behavior based on eating habits by quantifying eating behavior. It proposes “ChewReminder,” a smartphone application software that detects eating activities in real-time under a natural meal environment and gives feedback based on detected activity. ChewReminder detects four activities: chewing, swallowing, talking, and other.The smartwatch gives feedback using vibration depend on chewing count per one bite which information was linked from the smartphone. Also, the total feedback about the meal was displayed on the smartphone after finishing the meal. The chewing count for 70% subjects and chewing pace for more than half subjects was improved with using ChewReminder by the result of total chewing count, average of chewing count per bite and chewing pace. ChewReminder is effective especially people who are aware of fast eating. Also, the result of long-term experiment indicated that feedback displayed on a smartphone was effective to improve consciousness of eating activity. Therefore, the result of both experiment shows that ChewReminder is a valid system to improve consciousness of eating activity especially chewing activity.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76782380","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}
Pub Date : 2023-01-01DOI: 10.5220/0011690300003414
Flannagán Noonan, Juncal Nogales, Ciaran Doyle, Eilish Broderick, Joseph Walsh
: The costs of supporting hospitals are rising, bed numbers are falling and a growing population living longer will require more hospital visits over their lifetime. Thus there is a global focus on increasing the efficiency of patient throughput in a hospital. Bed management systems are still commonly paper-based and are effectively memory-less from the hospital point of view. The hospital information systems are typically billing and ordering systems with minimal information on patient movement along the patient pathway. The literature suggests that technology and shared information allow for shared views to model and predict usage to better manage finite resources. Paper-based systems work against this. This paper presents the design considerations for a bed management application developed in conjunction with a local private hospital. The application developed, provides a hospital-wide view of patient and bed status by recording and capturing touchpoints, that is patient-hospital interactions. Furthermore, it captures data electronically such that the data can be used for analysing patient presentation and bed moving with a view to improve bed management and patient throughput.
{"title":"Bed Management System Development","authors":"Flannagán Noonan, Juncal Nogales, Ciaran Doyle, Eilish Broderick, Joseph Walsh","doi":"10.5220/0011690300003414","DOIUrl":"https://doi.org/10.5220/0011690300003414","url":null,"abstract":": The costs of supporting hospitals are rising, bed numbers are falling and a growing population living longer will require more hospital visits over their lifetime. Thus there is a global focus on increasing the efficiency of patient throughput in a hospital. Bed management systems are still commonly paper-based and are effectively memory-less from the hospital point of view. The hospital information systems are typically billing and ordering systems with minimal information on patient movement along the patient pathway. The literature suggests that technology and shared information allow for shared views to model and predict usage to better manage finite resources. Paper-based systems work against this. This paper presents the design considerations for a bed management application developed in conjunction with a local private hospital. The application developed, provides a hospital-wide view of patient and bed status by recording and capturing touchpoints, that is patient-hospital interactions. Furthermore, it captures data electronically such that the data can be used for analysing patient presentation and bed moving with a view to improve bed management and patient throughput.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81305344","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}
Pub Date : 2023-01-01DOI: 10.5220/0011611200003414
Chetanya Puri, Stijn Keyaerts, Maxwell Szymanski, L. Godderis, K. Verbert, Stijn Luca, B. Vanrumste
: Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe. Long term impacts of MSDs include “Pain chronification” which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person’s future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain.
{"title":"Daily Pain Prediction in Workplace Using Gaussian Processes","authors":"Chetanya Puri, Stijn Keyaerts, Maxwell Szymanski, L. Godderis, K. Verbert, Stijn Luca, B. Vanrumste","doi":"10.5220/0011611200003414","DOIUrl":"https://doi.org/10.5220/0011611200003414","url":null,"abstract":": Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe. Long term impacts of MSDs include “Pain chronification” which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person’s future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74599881","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}
Pub Date : 2023-01-01DOI: 10.5220/0011783600003414
Huyen Hoang Nhung, Zilu Liang
: Prior studies have suggested potential associations between poor sleep and glucose dysregulation among diabetic patients. However, little is known about the relationship between sleep and glucose regulation in healthy populations. In this study, we proposed a data mining pipeline based on contrast set mining to identify significant associations between sleep and glucose in a dataset collected from a normoglycemic population in free-living environments. Unlike traditional correlation analysis, our approach does not assume a linear relationship between sleep and glucose and can potentially discover associations when a pair of metrics fall within certain value ranges. The data mining result highlights the total sleep time as an important sleep metric associated with glucose regulation the next day, which is characterised by rules with high lift and confidence. Furthermore, the result suggests that having a higher time ratio in normal glucose range was associated with better sleep continuity at night. These results may provide insights that people can immediately act on for better sleep and better glucose control. Future research may leverage the proposed data mining protocol to develop healthy behaviour recommender systems.
{"title":"Contrast Set Mining for Actionable Insights into Associations Between Sleep and Glucose in a Normoglycemic Population","authors":"Huyen Hoang Nhung, Zilu Liang","doi":"10.5220/0011783600003414","DOIUrl":"https://doi.org/10.5220/0011783600003414","url":null,"abstract":": Prior studies have suggested potential associations between poor sleep and glucose dysregulation among diabetic patients. However, little is known about the relationship between sleep and glucose regulation in healthy populations. In this study, we proposed a data mining pipeline based on contrast set mining to identify significant associations between sleep and glucose in a dataset collected from a normoglycemic population in free-living environments. Unlike traditional correlation analysis, our approach does not assume a linear relationship between sleep and glucose and can potentially discover associations when a pair of metrics fall within certain value ranges. The data mining result highlights the total sleep time as an important sleep metric associated with glucose regulation the next day, which is characterised by rules with high lift and confidence. Furthermore, the result suggests that having a higher time ratio in normal glucose range was associated with better sleep continuity at night. These results may provide insights that people can immediately act on for better sleep and better glucose control. Future research may leverage the proposed data mining protocol to develop healthy behaviour recommender systems.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85926721","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}
Pub Date : 2023-01-01DOI: 10.5220/0011924900003414
João Petersen, Vítor H. Carvalho, J. T. Oliveira, Eva Oliveira
: This paper addresses the development of the serious game PHOBOS, a virtual reality exposure therapy game for the treatment of blood-injection-injury phobia, also known as hemophobia. The virtual reality game which incorporates biometric sensors was upgraded from a 2018 version to perform usability tests to get the game ready for clinical trials. With this project we expect to contribute to the development of a framework that can be used by physiologists in the treatment of their patients with hemophobia.
{"title":"A Serious Game Development and Usability Test for Blood Phobia Treatment - PHOBOS","authors":"João Petersen, Vítor H. Carvalho, J. T. Oliveira, Eva Oliveira","doi":"10.5220/0011924900003414","DOIUrl":"https://doi.org/10.5220/0011924900003414","url":null,"abstract":": This paper addresses the development of the serious game PHOBOS, a virtual reality exposure therapy game for the treatment of blood-injection-injury phobia, also known as hemophobia. The virtual reality game which incorporates biometric sensors was upgraded from a 2018 version to perform usability tests to get the game ready for clinical trials. With this project we expect to contribute to the development of a framework that can be used by physiologists in the treatment of their patients with hemophobia.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90283745","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}
Pub Date : 2023-01-01DOI: 10.5220/0011939800003414
K. Dineva, Ivan Kitanovski, I. Dimitrovski, S. Loskovska, Alzheimer's Disease Neuroimaging Initiative
: In this research, we evaluate medical case retrieval for AD on the bases of descriptors generated by combining different modalities (Magnetic Resonance Imaging (MRI) markers, Fluorodeoxy-glucose Positron Emission Tomography (FDG-PET) based measures, Cerebrospinal Fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype and age as risk factors). We investigated whether they would provide complementary information aiming to improve medical case retrieval for AD. According to the obtained results, we concluded that this approach outperformed the retrieval results in the current reported research by gaining MAP value of 0.98 yet providing an efficient medical case retrieval for AD and keeping low dimensional feature vector.
{"title":"A Multi-Modality Approach to Medical Case Retrieval for Alzheimer's Disease","authors":"K. Dineva, Ivan Kitanovski, I. Dimitrovski, S. Loskovska, Alzheimer's Disease Neuroimaging Initiative","doi":"10.5220/0011939800003414","DOIUrl":"https://doi.org/10.5220/0011939800003414","url":null,"abstract":": In this research, we evaluate medical case retrieval for AD on the bases of descriptors generated by combining different modalities (Magnetic Resonance Imaging (MRI) markers, Fluorodeoxy-glucose Positron Emission Tomography (FDG-PET) based measures, Cerebrospinal Fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype and age as risk factors). We investigated whether they would provide complementary information aiming to improve medical case retrieval for AD. According to the obtained results, we concluded that this approach outperformed the retrieval results in the current reported research by gaining MAP value of 0.98 yet providing an efficient medical case retrieval for AD and keeping low dimensional feature vector.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81964288","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}
Pub Date : 2023-01-01DOI: 10.5220/0011669000003414
Nils Frohwitter, Alessa Hering, Ralf Möller, Mattis Hartwig
{"title":"Evaluating the Effects of a Priori Deep Learning Image Synthesis on Multi-Modal MR-to-CT Image Registration Performance","authors":"Nils Frohwitter, Alessa Hering, Ralf Möller, Mattis Hartwig","doi":"10.5220/0011669000003414","DOIUrl":"https://doi.org/10.5220/0011669000003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75749103","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}
Pub Date : 2023-01-01DOI: 10.5220/0011944500003414
Tahir Hameed, H. Khan, Saad Khan, Mutahira Khalid, Asim Abbas, S. Bukhari
{"title":"An NLP-Enhanced Approach to Test Comorbidities Risk Scoring Based on Unstructured Health Data for Hospital Readmissions Prediction","authors":"Tahir Hameed, H. Khan, Saad Khan, Mutahira Khalid, Asim Abbas, S. Bukhari","doi":"10.5220/0011944500003414","DOIUrl":"https://doi.org/10.5220/0011944500003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72840377","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}
Pub Date : 2023-01-01DOI: 10.5220/0011749800003414
G. Rao, D. Savage, V. Mago, P. Lingras
{"title":"A Survey on Technologies Used During out of Hospital Cardiac Arrest","authors":"G. Rao, D. Savage, V. Mago, P. Lingras","doi":"10.5220/0011749800003414","DOIUrl":"https://doi.org/10.5220/0011749800003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73128276","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}
Pub Date : 2023-01-01DOI: 10.5220/0011772500003414
Kotone Sakiyama, Yukie Majima, Seiko Masuda
{"title":"Development of Learning System to Support for Passing Steps of Wheelchair","authors":"Kotone Sakiyama, Yukie Majima, Seiko Masuda","doi":"10.5220/0011772500003414","DOIUrl":"https://doi.org/10.5220/0011772500003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72561953","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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