Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 은성종 | - |
dc.contributor.author | 이준영 | - |
dc.contributor.author | 정한 | - |
dc.contributor.author | 김계환 | - |
dc.date.accessioned | 2021-12-08T01:40:53Z | - |
dc.date.available | 2021-12-08T01:40:53Z | - |
dc.date.created | 2021-10-05 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 2093-4777 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82886 | - |
dc.description.abstract | Purpose: In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients. Methods: We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance. Results: Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm. Conclusions: This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 대한배뇨장애요실금학회 | - |
dc.relation.isPartOf | International Neurourology Journal | - |
dc.title | Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000724730700007 | - |
dc.identifier.doi | 10.5213/inj.2142276.138 | - |
dc.identifier.bibliographicCitation | International Neurourology Journal, v.25, no.3, pp.229 - 235 | - |
dc.identifier.kciid | ART002761961 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85117240195 | - |
dc.citation.endPage | 235 | - |
dc.citation.startPage | 229 | - |
dc.citation.title | International Neurourology Journal | - |
dc.citation.volume | 25 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | 정한 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Urinary patient | - |
dc.subject.keywordAuthor | Urination recognition | - |
dc.subject.keywordAuthor | Urination management system | - |
dc.subject.keywordAuthor | Mobile voiding chart | - |
dc.subject.keywordAuthor | Long short-term memory | - |
dc.subject.keywordAuthor | Recurrent neural network | - |
dc.subject.keywordPlus | RETENTION | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordPlus | WOMEN | - |
dc.relation.journalResearchArea | Urology & Nephrology | - |
dc.relation.journalWebOfScienceCategory | Urology & Nephrology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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