[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123938-en":3,"doc-seo-123938-105":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":4,"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},123938,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",6,"Technology","Generating Data in an Intelligent Environment and Systematically Predicting User Activity via Bluetooth Low Energy Beacons and Machine Learning","Intelligent Environments are growing and are enabled by Smart Spaces technologies that generate user-specific data. This work focuses on the Smart Spaces Lab at Middlesex University London, using Bluetooth Low Energy (BLE) beacons to support user data generation, user insight generation, and activity recognition. The study describes the system configuration architecture, the data collection setup across lab rooms and sensors, and the feature space used for machine learning modeling. Received signal strength (RSSI)-based location inference follows a prior approach, while the investigation concentrates on the final location output and subsequent activity recognition.","158 Intelligent Environments 2024: Combined Proceedings of Workshops and Demos & Videos Session  \nM.J. Hornos et al. (Eds.)© 2024 The Authors.  \nThis article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).  \ndoi:10.3233/AISE240030  \nGenerating Data in an Intelligent Environment and Systematically Predicting User Activity via Bluetooth Low Energy Beacons and Machine Learning  \nStefan Michael Anthony BENEDICT, Juan Carlos AUGUSTO and Omer KACAR Department of Computer Science  \nMiddlesex University London, United Kingdom ORCiD ID: Juan Carlos Augusto [https://orcid.org/0000-0002-0321-9150](https://orcid.org/0000-0002-0321-9150)  \nAbstract. The field of Intelligent Environments is experiencing an upward growth trajectory. This investigation delves into the state-of-the-art technology associated with the Smart Spaces Lab at Middlesex University London, specifically its Bluetooth Low Energy beacons. This enables User Data Generation enabling applications including User insight generation and Activity Recognition via  \nMachine Learning algorithms.  \nKeywords. intelligent environments, machine learning, activity recognition, data  \ngeneration  \n1. Introduction  \nIntelligent Environment(s) (IE) is a growing industry associated with domains including Ambient Assisted Living and Smart Homes (SHs) . Smart Homes (i.e., a form of IE), equipped with smart technology, allow customers to experience customised services. However, enabling User-specific customised services within a multiuser IE is driven on the identification of each User and their specific location within the IE [1] . An ancillary benefit of solving the challenge is the copious quantity of generated Data, giving insight into the User’s behaviour (e.g., eating habit, sleeping pattern, activity pattern, etc.) . This Data can provide valuable insights into the health (i.e., progression of diseases) of individuals with special needs, the energy utilisation patterns of the residents, enabling the optimisation of consumption [2,3] .  \nThe study was carried out at Middlesex University’s Smart Spaces Lab utlises Bluetooth Low Energy (BLE) technology building on the approach described in [1], This study generated User Data, enabling User Insight generation and Activity Recognition (AR) via Machine Learning (ML) . The remainder of the paper is segmented into three sections. Section 2 focusses on the System Configuration Methodology, Section 3 that is related to Data Collection while Section 4 is focused on AR and ML.  \nS.M.A. Benedict et al. / Generating Data in an IE and Systematically Predicting User Activity 159  \n2. System Configuration Methodology  \nThis investigation is built upon by leveraging the well-established Software (i.e., Web Servers, Databases, an Android application, etc.) and Hardware architecture (i.e., Smart sensors, smart switches, BLE Beacons, etc.) of the Smart Spaces Lab of Middlesex University London as described in [1] . The system configuration is displayed in Figure 1. Further, the computational hardware utilised in this segment of the investigation consisted of an 8th generation Intel Core i5 processor, 16 GB RAM and an 8 GB GPU.  \nFigure 1. The system architecture of the Smart Spaces Lab.  \n3. Data Collection  \nAll locations, except for Room 04, Room 05, and the Shower, are used for Data Collection. The Data that can be generated in the Smart Spaces Lab utilising its technology, as displayed in Figure 2. The technology within the Lab includes a multitude of sensors which include but is not limited to motion sensors, proximity door sensors as well as BLE Beacons to determine User/ Resident location. It should be noted that the“Multi Sensor”(s) provides temperature readings within the SH, but this was excluded from the investigation due to functionality issues. Table 1 illustrates a sample of the typeof Data used/final feature space within this resea","cbCaijRJvRdLI0aE","https://ap.wps.com/l/cbCaijRJvRdLI0aE","pdf",712757,1,"English","en",105,"# Introduction\n# System Configuration Methodology\n# Data Collection\n# Activity Recognition and Machine Learning","[{\"question\":\"What is the main goal of this research in intelligent environments?\",\"answer\":\"To generate user-related data in an intelligent environment and use it to systematically predict user activity through machine learning, supported by BLE beacons.\"},{\"question\":\"How does the system determine user location?\",\"answer\":\"It uses Bluetooth Low Energy beacon signals and an RSSI-based algorithm (based on a referenced approach) to infer user location, and the study considers the final location output.\"},{\"question\":\"What kinds of data are collected in the Smart Spaces Lab?\",\"answer\":\"The lab collects sensor-based readings such as timestamps, day type, user activity and location, and binary signals from bed, motion, door, light switch, and energy sensors, alongside BLE beacon measurements.\"}]","Generating Data in an Intelligent Environment and Systematically Predicting User Activity via Bluetooth Low Energy Beacons and Machine Learning | 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is the main goal of this research in intelligent environments?","Question",{"text":74,"@type":75},"To generate user-related data in an intelligent environment and use it to systematically predict user activity through machine learning, supported by BLE beacons.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the system determine user location?",{"text":79,"@type":75},"It uses Bluetooth Low Energy beacon signals and an RSSI-based algorithm (based on a referenced approach) to infer user location, and the study considers the final location output.",{"name":81,"@type":72,"acceptedAnswer":82},"What kinds of data are collected in the Smart Spaces Lab?",{"text":83,"@type":75},"The lab collects sensor-based readings such as timestamps, day type, user activity and location, and binary signals from bed, motion, door, light switch, and energy sensors, alongside BLE beacon 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