SENSOR BASED REAL-TIME HUMAN ACTIVITY RECOGNITION IN WIRELESS MULTIMEDIA SENSOR NETWORK

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dc.contributor.authorAlmakhan, Serik
dc.date.accessioned2021-07-30T09:43:06Z
dc.date.available2021-07-30T09:43:06Z
dc.date.issued2021-07
dc.description.abstractHuman Activity Recognition (HAR) can be widely used in medicine and military applications. Primarily, it can be used as an assistive technology for the healthcare of older people. In general, HAR can be performed using the data collected from the various type of sensors: body, object and ambient sensors. There are two types of sensors: external and internal. A typical example of applications, which use external sensors, is intelligent home implementations that contain many sensors, such as temperature, pressure, light, ultrasonic sensors, and cameras. In this study, data from internal sensors were used for HAR of activities including walking, walking up or downstairs, sitting, standing, and falling from the collected data of smartphones. In addition, all the HAR models experimented with by Wireless Multimedia Sensor Network (WMSN) with the real-time collected dataset. For the recognition, we use various neural network algorithms as CNN, LSTM and traditional Machine learning classification algorithms such as SVM, KNN, and Random Forest Classifier. In addition, we implemented dimensionality reduction to decrease the number of features, which helped reduce computational time and energy consumption, and transfer learning with different scenarios to increase accuracy, and all these functions are implemented and compared in two WMSN architectures.en_US
dc.identifier.citationAlmakhan, S. (2021). Sensor based Real-time Human Activity Recognition in Wireless Multimedia Sensor Network (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstanen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/5629
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectWMSNen_US
dc.subjectHARen_US
dc.subjectType of access: Gated Accessen_US
dc.subjectHuman Activity Recognitionen_US
dc.subjectKNNen_US
dc.titleSENSOR BASED REAL-TIME HUMAN ACTIVITY RECOGNITION IN WIRELESS MULTIMEDIA SENSOR NETWORKen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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