Research Article Elderly People Activity Recognition in Smart Grid Monitoring Environment Anusha Ganesan , 1 Anand Paul , 1 and HyunCheol Seo 2 1 School of Computer Science and Engineering Kyungpook National University, Daegu 41566, Republic of Korea 2 School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, Republic of Korea CorrespondenceshouldbeaddressedtoHyunCheolSeo;charles@knu.ac.kr Received 3 February 2022; Accepted 7 March 2022; Published 22 March 2022 AcademicEditor:RaviSamikannu Copyright©2022AnushaGanesanetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited. Elderlypeopleactivityrecognitionhasbecomeavitalnecessityinmanycountries,becausemostoftheelderlypeoplelivealone andarevulnerable.us,moreresearchtoadvanceinthemonitoringsystemsusedtorecognizetheactivitiesofelderlypeopleis required.Manyresearchershaveproposeddifferentmonitoringsystemsforactivityrecognitionusingwiredandwirelesswearable sensingdevices.However,theactivityclassificationaccuracyachievedsofarshouldbeimprovedtomeetthechallengesofmore preciseactivitymonitoring.OurstudyproposesasmartHumanActivityRecognitionsystemarchitectureutilizinganopensource dataset generated by wireless, batteryless sensors used by 14 healthy aged persons and unsupervised and supervised machine learning algorithms. In this paper, we also propose using a smart grid for checking regularly the wearable sensing device operational status to address the well-known reliability challenges of these devices, such as wireless charging and data trust- worthiness.Asthedatafromthesensingdeviceisverynoisy,weemploytheK-means++clusteringtoidentifyoutliersanduse advancedensembleclassificationtechniques,suchasthestackingclassifierforwhichametamodelbuiltusingtherandomforest algorithmgavebetterresultsthanallotherbasemodelsconsidered.Wealsoemployabaggingclassifier,whichisanensemble meta-estimatorfittingthepredictionoutputsofthebaseclassifiersandaggregatingthemtoproducetheensembleoutput.ebest classificationaccuracyof99.81wasachievedbythestackingclassifierintrainingand99.78%intesting,respectively.Comparisons forfindingthebestmodelwereconductedusingtherecall,F1score,andprecisionvalues. 1. Introduction Several countries in the world currently have a vast elderly population. is situation entails additional challenges in providing quality healthcare services and facilities for this demographic. Elderly people require more physical and psychological support and assistance. Most of the elderly peoplecurrentlyliveontheirownastheirchildrenworkin the different geographic locations. is leads to a lesser likelihoodofchildrentakingcareoftheirparents.ismakes theelderlyparentsveryvulnerabletoriskswithnoimmediate assistanceavailable.However,thisproblemcanbemitigated with the technological advancements in activity monitoring systemsforelderlypeople.Datarequiredbythesesystemsare acquired by wearable sensing devices and then analyzed to understandandforecasttheindividual’shealthconditionand requiredsupport.esemonitoringsystemsarereferredtoas Human Activity Recognition (HAR) systems. HAR systems play important roles in several domains such as healthcare, security, and smart environment deployment [1]. e op- erationofthesesystemsinvolvesfivemajorsteps,whichare illustratedinFigure1forfourbasicactivitiessuchassleeping, walking, standing, and sitting. However, to successfully implement these steps, ap- propriate devices and sensors are required to ensure the efficiency of the entire system. Hence, the development of such system relies on wireless networks, machine learning (ML), data processing, and classification methods. A HAR systemcandetectandmonitortheactivitiesaswellasthe hazardsthatcanaffecttheelderlypeople.Sincethedevices Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 9540033, 12 pages https://doi.org/10.1155/2022/9540033