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Nine activities were recorded while subjects wore an iPhone 8 Plus carrying acceleration and gyroscope signals sampled at 50 Hz.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Analytics © AASTU Press  \nJCSDA, Vol. 1, No. 2, 20–33  \nDOI: 10.69660/jcsda.01022402  \nISSN 2959-6912  \nHuman Activity recognition using machine learning  \nRediet Desalegn  \nCenter of Biomedical Engineering,  \nAddis Ababa Institute of Technology, Addis Ababa University,  \nAddis Ababa, Ethiopia  \nHana Mekonen  \nHaramaya University, Institute of Technology, Haramaye, Ethiopia  \nCorresponding author: [hanamekonen19@gmail.com](hanamekonen19@gmail.com)  \nMelkamu Hunegnaw  \nCenter of Biomedical Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia  \nHuman activity recognition is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data. It can improve people’swell-being by automatically assessing and summarizing their daily activities. In this study an automatic detection of nine different human activities from body sensor data was developed. The human activity data was collected from 30 between the age of  \n12 and 55 to provide data. from those 17 participants were male and 13 participants were female. Participants completed nine activities (sitting, standing, walking, running, upstairs, downstairs, sit-up, jumping and cycling) while wearing an I-phone eight plus in the pocket. Records from 18 individuals used for training, 6 participants data was used for validation and the remaining 6 participants records were used for testing. The human activity data is captured by via phone’s integrated acceleration and gyroscope sensors.  \nHence a total of nine-dimensional data (triaxial accelerometer data (linear acceleration and body acceleration) and triaxial gyroscope data) was acquired using these two sensors.  \nIn all case the sample rate used to capture the data was 50Hz. In this study both classical machine learning and deep learning methods were exported. In classical techniques four machine learning classifiers (SVM, KNN, logistic regression and decision tree) have been used to categorize the data. For this to work 18 set of features were carefully computed.  \nThree deep learning models (CNN, LSTM and hybrid CNN-LSTM) have been also trained in the accurate detection of human activity classification.  \nKeywords: human activity recognition, classical machine learning, deep learning.  \n1. Introduction  \nHuman activity recognition is concerned with identifying the specific movement or action of a person based on sensor data [1] . It has gained much attention in the current decade due to the wide range of application [2] . It provides an estimation of the energy consumption during everyday life this help to balance the number of calories consumed from food and beverage with the number of calories the body used for activity and this used to prevent overweight and obesity. Worldwide over  \n2.8 million people dying each year as a result of being overweight or obese in 2021 [3] . When a person’s body mass index is over 25 is consider as overweight and over 30 is  \n20  \nJournal of Computational Science & Data Analytics © AASTU Press  \nHuman Activity recognition using machine learning 21  \nobese. Worldwide 1.9 billion adults are overweight and 650 million obese [4] . Once considered the problem of the developed nation, the problem is slowly creeping into developing countries too. For instance, in Ethiopia, according to a recent Ethiopian study, the prevalence of central obesity was estimated to be 37.31% at the national level [5] . according to world cancer research international study by year 2025 170 million adults worldwide may have a body mass index of over 35kg/m2 [6] .  \nAccording to CDC’s study people, who are overweight or obese compared to those with healthy weight, are at increased risk for many series diseases and health conditions [7] . These include causes for death (mortality), high blood pressure, high LDL (low density lipoproteins) cholesterol, low HDL (high density lipoproteins) cholesterol ","cbCaioZik20gGIuj","https://ap.wps.com/l/cbCaioZik20gGIuj","pdf",402005,14,"English","# Introduction\n## Human activity recognition and applications\n## Health and obesity risk context","[{\"question\":\"What is human activity recognition based on in this study?\",\"answer\":\"It identifies specific movements or actions using sensor data captured from the phone’s integrated acceleration and gyroscope sensors.\"},{\"question\":\"Which human activities were included in the automatic detection?\",\"answer\":\"The system targets nine activities: sitting, standing, walking, running, upstairs, downstairs, sit-up, jumping, and cycling.\"},{\"question\":\"How was the dataset organized for training, validation, and testing?\",\"answer\":\"Records from 18 individuals were used for training, 6 participants for validation, and the remaining 6 participants’ records for testing.\"}]","Human Activity recognition using machine learning | PDF",35]