[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125800-en":3,"doc-seo-125800-105":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},125800,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","INDOOR WELLNESS - A MACHINE LEARNING TECHNIQUE FOR INTERNET OF THINGS SENSOR DATA - Thesis","This thesis investigates how indoor environmental conditions influence human wellness and how machine learning can predict emotional state and satisfaction. It uses two Taunito IoT sensors deployed in different rooms at the University of Bologna-Cesena Campus to collect environmental data. The workflow applies multiple machine learning models to assess correlations between wellness and environmental parameters, classify comfortable versus non-comfortable rooms, predict overall satisfaction, and estimate the optimal temperature for maximum satisfaction.","ALMA MATER STUDIORUM-UNIVERSIT`A DI BOLOGNA  \nCESENA CAMPUS  \nDEPARTMENT OF ELECTRICAL, ELECTRONIC, AND INFORMATION  \nENGINEERING  \n“GUGLIELMO MARCONI”  \nSECOND CYCLE DEGREE IN BIOMEDICAL ENGINEERING  \nClass: LM-21  \nTHESIS TITLE  \nINDOOR WELLNESS: A MACHINE LEARNING TECHNIQUE FOR INTERNET OF THINGS SENSOR DATA  \nGraduation thesis in  \nSENSORS AND NANOTECHNOLOGY  \nSupervisor Candidate  \nProf. Marco Tartagni Gaetano Ingenito  \nCo-Supervisor  \nProf. Aldo Romani  \nPhD Oumaima Afif  \nAcademic Year 2022/2023  \nContents  \nIntroduction 4  \n1 PRIOR KNOWLEDGE 5  \n1.1 Wellness concept ................................ 5  \n1.2 Epigenetics ................................... 9  \n1.3 IoT ........................................ 11  \n1.3.1 Taunito sensor ............................. 14  \n1.4 State of art .................................... 18  \n2 METHODS 19  \n2.1 Machine learning approaches .......................... 19  \n2.1.1 Principal Component Analysis (PCA) ................. 22  \n2.1.2 Soft Independent Modelling of Class Analogies (SIMCA) ...... 27  \n2.1.3 Multiple Linear Regression (MLR) .................. 29  \n2.1.4 Principal Component Regression (PCR) ................ 31  \n2.1.5 Projection to Latent Structures (PLS) ................. 33  \n2.1.6 Locally Weighted Regression (LWR) ................. 36  \n2.1.7 Understanding scores and loadings plots ................ 38  \n2.2 Satisfaction Test ................................. 40  \n3 EXPERIMENTAL SETUP AND ANALYSIS 42  \n3.1 Protocol ..................................... 42  \n3.2 Analysis Setup ................................. 45  \n4 RESULTS AND DISCUSSION 53  \n4.1 Results ...................................... 53  \n4.2 Discussion .................................... 59  \nBibliography 63  \nIntroduction  \nThe increasing levels of stress affects both the physical and mental wellbeing of the people [1], this is mostly reflected in people working in closed spaces where almost 90% of employees have suffered from burnout syndrome in their lifetime.  \nAs defined by the WHO, the quality of life is: ”an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns”  \n[2] . Different aspects of someone’s life can affect their well being, namely the social situation, physical and health status, financial and occupational condition and environmental surroundings. The latter one is also referred as environmental wellness and is influenced by the set of physical and chemical components of the environment.  \nIt is a known fact that the psychological state of an individual affects its productivity and the associated risk of committing errors, in the biomedical field this has an higher weight due to the elevated chance of impacting in a negative way on the patient’s health.  \nIn this paper is analyzed how the environment affects the wellness of an individual and is shown how machine learning can help predict the emotional state of a person. The project involves the use of two Taunito sensors, courtesy of TAUA s.r.l., in different rooms of the University of Bologna-Cesena Campus. The devices collect the data which is analyzed through machine learning methods to assess the correlation between wellness and environmental parameters, to classify between comfortable rooms and non, to build a model able to predict the overall satisfaction level of the people in the room and to build a model able to predict the optimal temperature to keep in a room for which the satisfaction is maximized.  \nChapter 1  \nPRIOR KNOWLEDGE  \nIn this chapter are described the general concepts useful to better understand the scope of the project and the state of art technologies.  \n1.1 Wellness concept  \nWellness is a broad concept used to describe a state beyond the absence of illness, but rather aims to optimize well-being [3] . In the 1970s a model was developed by Bill Hettler [4], a doctor at the University ","cbCaisgl53kQ9BDj","https://ap.wps.com/l/cbCaisgl53kQ9BDj","pdf",2278717,1,64,"English","en",105,"# Introduction\n# Prior Knowledge\n## Wellness concept\n## Epigenetics\n## IoT\n## State of art\n# Methods\n## Machine learning approaches\n## Satisfaction Test\n# Experimental setup and analysis\n## Protocol\n## Analysis Setup\n# Results and discussion\n## Results\n## Discussion","[{\"question\":\"What problem does the thesis address about indoor environments and wellness?\",\"answer\":\"It studies how stress and indoor environmental conditions relate to wellbeing, showing how machine learning can help predict a person’s emotional state and comfort-related outcomes.\"},{\"question\":\"What sensors and data source are used in the project?\",\"answer\":\"The project uses two Taunito IoT sensors provided by TAUA s.r.l., installed in different rooms of the University of Bologna-Cesena Campus to collect environmental data.\"},{\"question\":\"Which machine learning tasks are built using the collected sensor data?\",\"answer\":\"The work assesses correlations between wellness and environmental parameters, classifies comfortable versus non-comfortable rooms, predicts overall satisfaction level, and estimates the optimal temperature that maximizes satisfaction.\"}]","INDOOR WELLNESS - A MACHINE LEARNING TECHNIQUE FOR INTERNET OF THINGS SENSOR DATA - Thesis | PDF",1785901279,161,{"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},"indoor-wellness-a-machine-learning-technique-for-internet-of-things-sensor-data-thesis","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/indoor-wellness-a-machine-learning-technique-for-internet-of-things-sensor-data-thesis/125800/",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","2026-08-05",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 problem does the thesis address about indoor environments and wellness?","Question",{"text":75,"@type":76},"It studies how stress and indoor environmental conditions relate to wellbeing, showing how machine learning can help predict a person’s emotional state and comfort-related outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What sensors and data source are used in the project?",{"text":80,"@type":76},"The project uses two Taunito IoT sensors provided by TAUA s.r.l., installed in different rooms of the University of Bologna-Cesena Campus to collect environmental data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning tasks are built using the collected sensor data?",{"text":84,"@type":76},"The work assesses correlations between wellness and environmental parameters, classifies comfortable versus non-comfortable rooms, predicts overall satisfaction level, and estimates the optimal temperature that maximizes satisfaction.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]