[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118246-en":3,"doc-seo-118246-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118246,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing Babies’ Sleep Schedule Prediction through Machine Learning - Master Thesis - June 2024","Master of Science (Tech) thesis in Health Technology focuses on enhancing babies’ sleep schedule prediction using machine learning. Establishing consistent bedtime routines early is framed as essential for health benefits and for reducing later sleep-related difficulties, while acknowledging parents’ practical challenges. The work integrates data analytics and efficient processing, emphasizing data cleanliness and feature analysis. Multiple regression models—KNN, XGBoost, Random Forests, LSTM, and RNN—are trained and evaluated. Results indicate meaningful potential to improve forecasted sleep schedules with performance near baseline expectations.","Enhancing Babies’ Sleep Schedule Prediction through Machine Learning  \nUniversity of Turku Department of Computing Master of Science (Tech) Thesis Health Technology  \nJune 2024  \nAnna Fernandez-Rajal i Sabala  \nSupervisors:  \nAcademic Ph.D. Tapio Pahikkala  \nM.S. Yuning Wang  \nCompany Alexander Brokking  \nThe originality of this thesis has been checked in accordance with the University of Turku quality assurance system using the Turnitin OriginalityCheck service.  \nThis Master Thesis has been done within a double degree at EIT Digital School, KTH Royal Institute of Technology and University of Turku. It has been conducted at the company Napper [1] .  \na  \nUNIVERSITY OF TURKU Department of Computing  \nAnna Fernandez-Rajal i Sabala: Enhancing Babies’ Sleep Schedule Prediction through Machine Learning  \nMaster of Science (Tech) Thesis, 136p.  \nHealth Technology June 2024  \nIn recent years, there has been a growing interest in improving sleep quality and understanding sleep patterns. This thesis will focus on enhancing sleeping schedules for babies through machine learning. Establishing a consistent bedtime routine ata young age is crucial, as it offers numerous health benefits and can prevent sleeprelated issues later in life. Despite this, many parents still find it challenging to manage their babies’ sleep schedules effectively.  \nThis master’s thesis explores the integration of machine learning algorithms into babies’ sleep schedule predictions to provide more accurate and personalized recommendations. It focuses on the integration of advanced data analytics and processing techniques to improve sleep forecasts. Recognizing the importance of data cleanliness and processing efficiency, the research delves into different steps for preparing and analyzing the dataset on sleep and baby tracking information. With a strong focus also on feature analysis, it later dives into various machine learning models and assesses their effectiveness and performance. The regression task with machine learning models includes K-Nearest Neighbors (KNN), XGBoost, Random Forests (RF), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN) .  \nThe project offers a methodical approach that includes background information, relevant literature, dataset specifics, suggested techniques, findings, and conclusions. The results demonstrate a clear potential to improve the current sleep schedules with machine learning to achieve the desired goals, with performance metrics showing proximity to baseline expectations. This thesis contributes to the field by advancing the methodology of baby sleep tracking, ultimately aiming to enhance the well-being of infants and ease the challenges faced by parents in managing their babies’ sleep routines.  \nKeywords: machine learning, data analytics, baby sleep schedules, feature analysis  \nContents  \n1 Introduction 1  \n1.1 Problem statement ............................ 2  \n1.2 Research questions ............................ 2  \n1.3 Contributions ............................... 3  \n1.4 Delimitations ............................... 3  \n1.5 Structure ................................. 4  \n2 Background 5  \n2.1 Babies’ sleeping habits .......................... 5  \n2.2 Preprocessing techniques ......................... 7  \n2.3 Machine Learning Models ........................ 8  \n2.3.1 Regression ............................. 9  \n2.4 Evaluation metrics ............................ 14  \n3 Related Work 17  \n3.1 Sleep pattern understanding and prediction ............... 17  \n3.1.1 Machine learning-based sleep analysis .............. 18  \n3.1.2 Wearable technology ....................... 19  \n3.1.3 Individualized sleep scheduling .................. 20  \n3.2 Data adherence and generative models ................. 21  \n4 Dataset 24  \n4.1 Data collection .............................. 24  \n4.2 Data cleaning ............................... 26  \n5 Proposed Method 29  \n5.1 Proposed model architecture .......................","cbCailpcr0uv7m7T","https://ap.wps.com/l/cbCailpcr0uv7m7T","pdf",13288185,1,136,"English","en",105,"# Introduction\n## Problem statement\n## Research questions\n## Contributions\n## Delimitations\n## Structure\n# Background\n## Babies’ sleeping habits\n## Preprocessing techniques\n## Machine Learning Models\n## Regression\n## Evaluation metrics\n# Related Work\n## Sleep pattern understanding and prediction\n## Machine learning-based sleep analysis\n## Wearable technology\n## Individualized sleep scheduling\n## Data adherence and generative models\n# Dataset\n## Data collection\n## Data cleaning\n# Proposed Method\n## Proposed model architecture\n## Feature engineering\n## Quality checked dataset\n## Feature extraction\n## Feature selection\n## Feature scaling\n## Model training\n## Train-test split\n## Used models\n# Results and Discussion\n## Performance evaluation\n## Model 1: Number of naps per day\n## Model 2: Duration and Start time of naps\n## Discussion\n# Conclusions\n## Future work","[{\"question\":\"What is the thesis goal regarding babies’ sleep routines?\",\"answer\":\"To improve babies’ sleep schedule prediction by using machine learning, enabling more accurate and personalized recommendations for bedtime and nap patterns.\"},{\"question\":\"Which machine learning models are evaluated for regression-based sleep prediction?\",\"answer\":\"K-Nearest Neighbors (KNN), XGBoost, Random Forests, Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN) are used for regression tasks.\"},{\"question\":\"What are the key steps taken to prepare the dataset before modeling?\",\"answer\":\"The research covers data collection, data cleaning, and feature engineering steps including quality checks, feature extraction, feature selection, and feature scaling.\"}]","Enhancing Babies’ Sleep Schedule Prediction through Machine Learning - Master Thesis - June 2024 | PDF",1785682625,343,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-babies-sleep-schedule-prediction-through-machine-learning-master-thesis-june-2024","",{"@graph":36,"@context":85},[37,54,68],{"@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/enhancing-babies-sleep-schedule-prediction-through-machine-learning-master-thesis-june-2024/118246/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the thesis goal regarding babies’ sleep routines?","Question",{"text":75,"@type":76},"To improve babies’ sleep schedule prediction by using machine learning, enabling more accurate and personalized recommendations for bedtime and nap patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated for regression-based sleep prediction?",{"text":80,"@type":76},"K-Nearest Neighbors (KNN), XGBoost, Random Forests, Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN) are used for regression tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key steps taken to prepare the dataset before modeling?",{"text":84,"@type":76},"The research covers data collection, data cleaning, and feature engineering steps including quality checks, feature extraction, feature selection, and feature scaling.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]