Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device
0
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (25)
- Klevmark B, "Natural pressure-volume curves and conventional cystometry", Scand J Urol Nephrol Suppl, 201, pp.1~4 (1999)
- McGuire EJ, "Videourodynamic studies", Urol Clin North Am, 23, pp.309~321 (1996)
- Abrams P, "The standardisation of terminology of lower urinary tract function : report from the Standardisation Sub-committee of the International Continence Society", Neurourol Urodyn, 21, pp.167~178 (2002)
- Jarvis GJ, "An assessment of urodynamic examination in incontinent women", Br J Obstet Gynaecol, 87, pp.893~896 (1980)
- Webb RJ, "Electromyographic abnormalities in the urethral and anal sphincters of women with idiopathic retention of urine", Br J Urol, 70, pp.22~25 (1992)
- Kim SH, "Voiding diary might serve as a useful tool to understand differences between bladder pain syndrome", interstitial cystitis and overactive bladder, 2014, pp.21, 179~183
- Haykin S, "Neural networks : a comprehensive foundation" (1994), MacMillan
- Christof Karmonik, "Data-Driven Machine-Learning Quantifies Differences in the Voiding Initiation Network in Neurogenic Voiding Dysfunction in Women With Multiple Sclerosis", International Neurourology Journal, 23(3), pp.195~204 (2019), 대한배뇨장애요실금학회[DOI]
- 김진욱, "Past, Present, and Future in the Study of Neural Control of the Lower Urinary Tract", International Neurourology Journal, 24(3), pp.191~199 (2020), 대한배뇨장애요실금학회[DOI]
- Jussi Nikkola, "Repetitive Transcranial Magnetic Stimulation for Chronic Prostatitis", Chronic Pelvic Pain Syndrome: A Prospective Pilot Study, 2020, pp.24(2), 144~149[DOI]
- Appaswamy Thirumal Prabhakar, "Neural Correlates of Urinary Retention in Lateral Medullary Infarction", International Neurourology Journal, 23(3), pp.205~210 (2019), 대한배뇨장애요실금학회[DOI]
- Fraley C, "Model-based clustering, discriminant analysis, and density estimation", J Am Stat Assoc, 97, pp.611~631 (2002)
- 문자운, "Current Pharmacologic Strategies for Treatment of Intractable Epilepsy in Children", International Neurourology Journal, 25, pp.8~18 (2021), 대한배뇨장애요실금학회[DOI]
- Aykut Baser, "Clinical Manifestations of Overactive Bladder With Migraine as a Comorbidity: A Prospective Cross-Sectional Study", International Neurourology Journal, 24(4), pp.375~381 (2020), 대한배뇨장애요실금학회[DOI]
- Berndt DJ, "Using dynamic time warping to find patterns in time series", Proceeding AAAIWS’94 Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp.359~370 (1994)
- Murphy KP, "Dynamic bayesian networks: representation, inference and learning", pp.2002
- Sonnhammer EL, "A hidden Markov model for predicting transmembrane helices in protein sequences", Proc Int Conf Intell Syst Mol Biol, 6, pp.175~182 (1998)
- LeCun Y, "Deep learning", Nature, 521, pp.436~444 (2015)
- Hochreiter S, "Long short-term memory", Neural Comput, 9, pp.1735~1780 (1997)
- 황보택근, "Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band", International Neurourology Journal, 22, pp.91~100 (2018), 대한배뇨장애요실금학회
- 은성종, "Development of Personalized Urination Recognition Technology Using Smart Bands", International Neurourology Journal, 21, pp.76~83 (2017), 대한배뇨장애요실금학회
- Simard PY, "Best practices for convolutional neural networks applied to visual document analysis", Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003), pp.958~962 (2003)
- Valente F, "Combination of acoustic classifiers based on Dempster-Shafer theory of evidence", 2007 IEEE International Conference on Acoustics, Speech and Signal Processing -ICASSP, pp.1129~1132 (2007)
- Liwicki M, "A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks", ICDAR 2007 Proceedings of the Ninth International Conference on Document Analysis and Recognition (2007), IEEE Computer Society
- Vapnik V, "Support vector method for multivariate density estimation", Adv Neural Inf Process Syst, pp.659~665 (2000)