[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121594-en":3,"doc-seo-121594-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121594,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",7,"Healthcare","Autonomous collection of voiding events for sound uroflowmetries with machine learning - AutoFlow acoustic platform","We present AutoFlow, a Raspberry Pi-based acoustic platform that autonomously detects and records voiding events using machine learning. Uroflowmetry is a noninvasive diagnostic test for urinary tract function, but conventional clinic-based procedures are distressing, costly, and unsuitable for continuous home monitoring. AutoFlow addresses these limits with a low-cost solution integrated into daily routines. Using a home bathroom acoustic dataset, five models are trained and evaluated; Gradient Boost on a Raspberry Pi Zero 2 W reaches 95.63% accuracy with 0.15-second inference, supporting at-home personalized healthcare and areas with limited specialist access.","Biomedical Signal Processing and Control 105 (2025) 107556  \n| Autonomous collection of voiding events for sound uroflowmetries with machine learning\u003Cbr>Laura Arjona a ,∗, Sergio Hernández a, Girish Narayanswamyb, Alfonso Bahillo c, Shwetak Patel b\u003Cbr>a Faculty of Engineering, University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain\u003Cbr>b Paul G. Allen School of Computer Science and Engineering and the Department of Electrical and Computer Engineering, University of Washington, 185 E Stevens Way NE, Seattle, 98195-2350, WA, United States\u003Cbr>c Department of Signal Theory and Communications, University of Valladolid, P. ◦ de Belén, 7, Valladolid, 47011, Spain |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Acoustics\u003Cbr>Sound sensing\u003Cbr>IoT\u003Cbr>Sound-based uroflowmetry\u003Cbr>Edge computing\u003Cbr>Machine learning |  | We present AutoFlow, a Raspberry Pi-based acoustic platform that uses machine learning to autonomously detect and record voiding events. Uroflowmetry, a noninvasive diagnostic test for urinary tract function. Current uroflowmetry tests are not suitable for continuous health monitoring in a nonclinical environment because they are often distressing, costly, and burdensome for the public. To address these limitations, we developed a low-cost platform easily integrated into daily home routines. Using an acoustic dataset of home bathroom sounds, we trained and evaluated five machine learning models. The Gradient Boost model on a Raspberry Pi Zero 2 W achieved 95.63% accuracy and 0.15-second inference time. AutoFlow aims to enhance personalized healthcare at home and in areas with limited specialist access. |  |\n\n1. Introduction  \nOne of the problems frequently associated with ageing is that related to the urinary system. Voiding dysfunction is highly prevalent and has a major impact on the quality of life of many people (more than 60% of men over 60 years of age) [1]. Lower Urinary Track Symptoms (LUTS) are those that affect the filling and emptying of the bladder and post-voiding. They lead to a significant decrease in personal quality of life and considerable expenditure in healthcare resources. Considering that the prevalence of voiding pathologies increases with age and that the global population is ageing, it is expected that the number of males who will need medical treatment for LUTS will increase significantly in the next 20 years.  \nUroflowmetry is an important screening test that can aid in the diagnosis, prognostication and follow-up of urological diseases. This test tracks how fast urine flows, how much urine flows out, and how long it takes. Current uroflowmetry tests are not suitable for continuous health monitoring in a nonclinical environment because they are often distressing, costly, and burdensome for the public. It is carried out on an outpatient basis at specified procedure areas and involves having the person urinating into an uroflowmeter. This process is unnatural and requires ‘‘on-demand’’ voiding, often with either low or very high bladder filling. This leads to significant test-to-test variability because the situational stress of the patient can affect the flow rate, corresponding  \nto non-representative results [2]. Therefore, it has been recommended that the uroflowmetry test should be performed more than once, which requires time-consuming and costly repeated clinic visits. Obtaining uroflowmetry data in the home setting has the potential for increased data on voiding patterns to inform clinical decision-making [3].  \nThis is the reason why the demand for smaller, more versatile devices has grown and led to the emergence of dedicated portable uroflowmeter. Nevertheless, these devices have not been fully adopted into routine practise, because they are costly and difficult to operate. Therefore, the envisioned platform for uroflowmetry targeted by this work should be cost-effective, easily transportable, and capable of conducting consiste","cbCaigBzskkY6v99","https://ap.wps.com/l/cbCaigBzskkY6v99","pdf",1738781,1,"English","en",105,"# Introduction\n## Uroflowmetry and its limitations\n## Motivation for home-based sound uroflowmetry\n## Proposed autonomous platform concept","[{\"question\":\"What problem does AutoFlow aim to solve in uroflowmetry?\",\"answer\":\"AutoFlow targets the lack of continuous, nonclinical uroflowmetry monitoring because existing tests are distressing, costly, and require outpatient visits and special equipment.\"},{\"question\":\"How does AutoFlow detect voiding events?\",\"answer\":\"AutoFlow uses acoustic sensing with a machine learning model running on a Raspberry Pi to autonomously detect voiding events and start recording.\"},{\"question\":\"What performance was achieved by the best model on Raspberry Pi?\",\"answer\":\"The Gradient Boost model on a Raspberry Pi Zero 2 W achieved 95.63% accuracy with 0.15-second inference time.\"}]","Autonomous collection of voiding events for sound uroflowmetries with machine learning - AutoFlow acoustic platform | PDF",1785736398,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"autonomous-collection-of-voiding-events-for-sound-uroflowmetries-with-machine-learning-autoflow-acoustic-platform","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/autonomous-collection-of-voiding-events-for-sound-uroflowmetries-with-machine-learning-autoflow-acoustic-platform/121594/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does AutoFlow aim to solve in uroflowmetry?","Question",{"text":74,"@type":75},"AutoFlow targets the lack of continuous, nonclinical uroflowmetry monitoring because existing tests are distressing, costly, and require outpatient visits and special equipment.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does AutoFlow detect voiding events?",{"text":79,"@type":75},"AutoFlow uses acoustic sensing with a machine learning model running on a Raspberry Pi to autonomously detect voiding events and start recording.",{"name":81,"@type":72,"acceptedAnswer":82},"What performance was achieved by the best model on Raspberry Pi?",{"text":83,"@type":75},"The Gradient Boost model on a Raspberry Pi Zero 2 W achieved 95.63% accuracy with 0.15-second inference time.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]