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dc.contributor.authorOguntala, George A.
dc.contributor.authorHu, Yim Fun
dc.contributor.authorAlabdullah, Ali A.S.
dc.contributor.authorAbd-Alhameed, Raed A.
dc.contributor.authorAli, Muhammad
dc.contributor.authorLuong, D.K.
dc.date.accessioned2021-03-23T18:09:31Z
dc.date.accessioned2021-04-01T13:58:41Z
dc.date.available2021-03-23T18:09:31Z
dc.date.available2021-04-01T13:58:41Z
dc.date.issued2021
dc.identifier.citationOguntala GA, Hu YF, Alabdullah AAS et al (2021) Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living. IEEE Internet of Things Journal. Accepted for publication.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18418
dc.descriptionYesen_US
dc.description.abstractIEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://ieeexplore.ieee.org/document/9328564en_US
dc.rights© 2021 IEEE. Reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectActivity recognitionen_US
dc.subjectAmbient assisted livingen_US
dc.subjectComputational complexityen_US
dc.subjectComputational modelingen_US
dc.subjectComputer architectureen_US
dc.subjectData modelsen_US
dc.subjectLSTMen_US
dc.subjectRecurrent neural networken_US
dc.subjectRecurrent neural networksen_US
dc.subjectRFIDen_US
dc.subjectSenior citizensen_US
dc.subjectSensorsen_US
dc.subjectSmart homesen_US
dc.titlePassive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Livingen_US
dc.status.refereedYesen_US
dc.date.Accepted2021-01-19
dc.date.application2021-01-19
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2021-03-23T18:09:35Z
refterms.dateFOA2021-04-01T13:59:19Z
dc.openaccess.statusGreenen_US


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