[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119881-en":3,"doc-seo-119881-105":30,"detail-sidebar-cat-0-en-105":92},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119881,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Prediction of stroke patients’ bedroom-stay duration - machine-learning approach using wearable sensor data","Stroke rehabilitation recognizes the need to remain physically active and avoid prolonged bed rest, yet many inpatients spend most of the day immobile in or near their bedrooms with limited chances for activity beyond that space. Recording bedroom-stay duration can be burdensome for clinicians, and installing facility access points for wearable-based tracking is costly. This study evaluates machine learning to estimate bedroom-stay duration from wearable-recorded activity signals and demographic information, aiming to reduce cost while improving practicality and privacy in future measurements.","TYPE Original Research PUBLISHED 03 January 2024  \nDOI 10.3389/fbioe.2023.1285945  \nOPEN ACCESS  \nEDITED BY  \nGuozhen Liu,  \nThe Chinese University of Hong Kong, China  \nREVIEWED BY  \nShitharth S,  \nKebri Dehar University, Ethiopia Moein Enayati,  \nMayo Clinic, United States  \n*CORRESPONDENCE  \nTakayuki Ogasawara,  \n [takayuki.ogasawara@ntt.com](takayuki.ogasawara@ntt.com)  \nRECEIVED 30 August 2023  \nACCEPTED 11 December 2023  \nPUBLISHED 03 January 2024  \nCITATION  \nOgasawara T, Mukaino M, Matsunaga K, Wada Y, Suzuki T, Aoshima Y, Furuzawa S, Kono Y, Saitoh E, Yamaguchi M, Otaka Y and Tsukada S (2024), Prediction of stroke patients ’ bedroom-stay duration:  \nmachine-learning approach using wearable sensor data.  \nFront. Bioeng. Biotechnol. 11:1285945 .  \ndoi: 10.3389/fbioe.2023.1285945  \nCOPYRIGHT  \n© 2024 Ogasawara, Mukaino, Matsunaga, Wada, Suzuki, Aoshima, Furuzawa, Kono, Saitoh, Yamaguchi, Otaka and Tsukada. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPrediction of stroke patients’bedroom-stay duration:  \nmachine-learning approach using wearable sensor data  \nTakayuki Ogasawara 1,2*, Masahiko Mukaino 2,3,  \nKenichi Matsunaga 4, Yoshitaka Wada 2, Takuya Suzuki 5, Yasushi Aoshima 5, Shotaro Furuzawa 5, Yuji Kono 2,5, Eiichi Saitoh 2, Masumi Yamaguchi 1, Yohei Otaka 2 and Shingo Tsukada 1  \n1NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan, 2Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan, 3Department of Rehabilitation Medicine, Hokkaido University Hospital, Sapporo, Japan, 4NTT Device Innovation Center, NTT Corporation, Atsugi, Japan, 5Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan  \nBackground: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.  \nMethod: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.  \nResults: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duratio","cbCaiqtA7r0T9iAy","https://ap.wps.com/l/cbCaiqtA7r0T9iAy","pdf",3133588,1,14,"English","en",105,"# Background\n## Research aim\n# Method\n## Study design and data collection\n## Predictive modeling\n# Results\n## Data acquisition using Bluetooth Low Energy\n## Correlations with explanatory variables\n## Prediction performance\n# Conclusion","[{\"question\":\"Why is bedroom-stay duration important in stroke rehabilitation?\",\"answer\":\"Physical activity and avoiding prolonged bed rest support better recovery outcomes, but stroke inpatients often remain inactive in or near their bedrooms. Measuring bedroom time helps quantify this behavior pattern and its rehabilitation relevance.\"},{\"question\":\"What data sources were used to estimate bedroom-stay duration?\",\"answer\":\"The study used wearable-device activity recordings, including electrocardiograms and chest acceleration, along with the recorded access point location name and participant demographic information.\"},{\"question\":\"How accurate were the machine-learning predictions?\",\"answer\":\"After training, the model predicted bedroom-stay duration with a correlation coefficient (R) of 0.72 and p \\u003c 0.001, indicating feasible prediction from activity data and demographics without continuous location tracking.\"}]","Prediction of stroke patients’ bedroom-stay duration - machine-learning approach using wearable sensor data | PDF",1785726810,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prediction-of-stroke-patients-bedroom-stay-duration-machine-learning-approach-using-wearable-sensor-data","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-stroke-patients-bedroom-stay-duration-machine-learning-approach-using-wearable-sensor-data/119881/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is bedroom-stay duration important in stroke rehabilitation?","Question",{"text":76,"@type":77},"Physical activity and avoiding prolonged bed rest support better recovery outcomes, but stroke inpatients often remain inactive in or near their bedrooms. Measuring bedroom time helps quantify this behavior pattern and its rehabilitation relevance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources were used to estimate bedroom-stay duration?",{"text":81,"@type":77},"The study used wearable-device activity recordings, including electrocardiograms and chest acceleration, along with the recorded access point location name and participant demographic information.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate were the machine-learning predictions?",{"text":85,"@type":77},"After training, the model predicted bedroom-stay duration with a correlation coefficient (R) of 0.72 and p \u003C 0.001, indicating feasible prediction from activity data and demographics without continuous location tracking.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]