Human activity classification using long short-term memory network
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Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network.
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