[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116996-en":3,"doc-seo-116996-105":30,"detail-sidebar-cat-0-en-105":96},{"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},116996,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning-based Approaches for Advanced Monitoring of Smart Glasses","Growing demand for productivity, product quality, and effectiveness has accelerated adoption of machine learning services driven by the increasing availability of IoT sensors and the volume of collected data. A key application is Predictive Maintenance, which estimates equipment health and the Remaining Useful Life (RUL). This work investigates predictive maintenance techniques for monitoring Smart Glasses’ battery status, including device description, data collection, exploratory data analysis, unsupervised anomaly detection for partial and unlabeled data, and classical plus deep learning models to estimate RUL. Model interpretation is provided for anomaly detection and RUL estimation.","DIPARTIMENTO  \nDI INGEGNERIA  \nDELL’INFORMAZIONE  \nDIPARTIMENTO DI INGEGNERIA DELL’INFORMAZIONE  \nCORSO DI LAUREA MAGISTRALE  \nIN COMPUTER ENGINEERING  \n“Machine Learning-based Approaches  \nfor Advanced Monitoring of Smart Glasses”  \nRelatore: Prof. / Dott. Susto Gian Antonio  \nLaureando: Corsetti Rocco  \nANNO ACCADEMICO 2022-2023  \n27 Febbraio 2023  \n1  \nAbstract  \nWith today’s growing demand on productivity, product quality and e↵ectiveness, the importance of Machine Learning-based functionalities and services has dramatically increased. Such paradigm shift can be mainly associated with the increasing availability of Internet of Things (IoT) sensors and devices, the increase of data collected in the IoT scenario and the increasing popularity and availability of machine learning approaches. One of the most appealing applications of ML-based solutions is for sure Predictive Maintenance, which aims at improving maintenance management by exploiting the estimation of the health status of a piece of equipment. One of the main formalizations of the PdM problem is the prediction of the Remaining Useful Life (RUL), that is defined as the time/process iterations remaining for a device component to perform its task before it loses functionality. This work investigates a possible application of predictive maintenance techniques for the monitoring of the battery of Smart Glasses. The work starts with the description of the considered devices, the modalities of data collection and the Exploratory Data Analysis for better understanding the task. The first experimental part consists in the application of an unsupervised anomaly detection technique, useful to initially deal with the partial and unlabeled data. The last part of the work contains the results of the application of both classical machine learning and deep learning approaches for the estimation of the RUL of the devices battery. A section for the interpretation of the machine-learning models is included for both the anomaly detection and RUL estimation approaches.  \n3  \nContents  \n1 Introduction 8  \n2 Dataset 12  \n2.1 ISee Dataset ............................ 12  \n2.1.1 Data Recording ...................... 12  \n2.1.2 Data Preprocessing .................... 18  \n2.2 Second Platform ......................... 19  \n3 Exploratory Data Analysis 22  \n3.1 ISee Preliminary Analysis .................... 22  \n3.2 Second Platform Preliminary Analysis ............. 25  \n4 Machine Learning Background 28  \n4.1 Formal Definitions ........................ 29  \n4.1.1 Empirical Risk Minimization ............... 29  \n4.1.2 Probably Approximated Correct Learning ....... 30  \n4.1.3 The No Free Launch Theorem .............. 33  \n4.1.4 Regularization ....................... 35  \n4.2 Learning Approaches and Tasks ................. 36  \n4.3 A simple task: Linear Regression ................ 37  \n4.4 Gradient Based Optimization Techniques ............ 39  \n4.4.1 Gradient Descent ..................... 39  \n4.4.2 Gradient Descent Approximations ............ 40  \n4.5 Neural Networks and Deep Learning .............. 43  \n4.5.1 Basics of Neural Networks ................ 45  \n4.5.2 Deep Learning ....................... 48  \n4.6 Sequential Learning ........................ 51  \n4.6.1 Gated Units ........................ 53  \n4.6.2 Convolution ........................ 55  \n5 Unsupervised Anomaly Detection 58  \n5.1 Isolation Forest .......................... 58  \n5.1.1 Isolation Forest Application ............... 58  \n5.2 Anomaly Interpretation ...................... 61  \n5.2.1 SHAP: SHapley Additive exPlanations ......... 61  \n5.2.2 DIFFI: Depth-based Isolation Forest Feature Importance 64  \n5.3 Conclusions on Anomaly Detection ............... 67  \n6 Remaining Useful Life Prediction: Machine Learning experiments 68  \n6.1 Introduction to RUL prediction ................. 68  \n6.2 Setup: Software and Limitations ................. 69  \n6.2.1 CeRULEo ......................... 69  \n6.3 Algorithms and Results ..............","cbCaib5ZKm78f1l2","https://ap.wps.com/l/cbCaib5ZKm78f1l2","pdf",6140746,1,112,"English","en",105,"# Introduction\n# Dataset\n## ISee Dataset\n## Second Platform\n# Exploratory Data Analysis\n## ISee Preliminary Analysis\n## Second Platform Preliminary Analysis\n# Machine Learning Background\n# Unsupervised Anomaly Detection\n## Isolation Forest\n## Anomaly Interpretation\n# Remaining Useful Life Prediction: Machine Learning experiments\n## Introduction to RUL prediction\n## Setup: Software and Limitations\n## Algorithms and Results\n## Model Interpretation and Feature Selection\n# Advanced Experiments\n## ISee and Deep Learning\n## Second Platform Advanced Experiments\n# Related Works\n# Conclusions","[{\"question\":\"What problem does the document address for Smart Glasses?\",\"answer\":\"It studies predictive maintenance for monitoring the battery of Smart Glasses, focusing on estimating the Remaining Useful Life (RUL) before functionality degrades.\"},{\"question\":\"How is the dataset handled before RUL estimation?\",\"answer\":\"The workflow includes device description, data collection modalities, and exploratory data analysis, then uses an unsupervised anomaly detection method to handle partial and unlabeled data.\"},{\"question\":\"Which modeling approaches are used to estimate RUL?\",\"answer\":\"The document applies both classical machine learning and deep learning approaches, evaluates them with defined metrics, and compares algorithms such as Random Forest and Gradient Tree Boosting.\"},{\"question\":\"How are the machine learning models interpreted?\",\"answer\":\"Interpretation is included for both anomaly detection and RUL estimation, using feature/importance explanation techniques such as SHAP and methods based on isolation-forest feature importance, plus permutation feature importance for RUL models.\"}]","Machine Learning-based Approaches for Advanced Monitoring of Smart Glasses | 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problem does the document address for Smart Glasses?","Question",{"text":76,"@type":77},"It studies predictive maintenance for monitoring the battery of Smart Glasses, focusing on estimating the Remaining Useful Life (RUL) before functionality degrades.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the dataset handled before RUL estimation?",{"text":81,"@type":77},"The workflow includes device description, data collection modalities, and exploratory data analysis, then uses an unsupervised anomaly detection method to handle partial and unlabeled data.",{"name":83,"@type":74,"acceptedAnswer":84},"Which modeling approaches are used to estimate RUL?",{"text":85,"@type":77},"The document applies both classical machine learning and deep learning approaches, evaluates them with defined metrics, and compares algorithms such as Random Forest and Gradient Tree Boosting.",{"name":87,"@type":74,"acceptedAnswer":88},"How are the machine learning models interpreted?",{"text":89,"@type":77},"Interpretation is included for both anomaly detection and RUL estimation, using feature/importance explanation techniques such as SHAP and methods based on isolation-forest feature importance, plus permutation feature importance for RUL models.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & 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