Show simple item record Chowdhury, Atiqul Islam Ashraf, Mohsena Islam, Ashraful Ahmed, Eshtiak Jaman, Md. Saroar Rahman, Mohammad Masudur 2022-01-16T05:18:21Z 2022-01-16T05:18:21Z 2020
dc.description.abstract Human activity recognition (HAR) is considered as one of the most difficult and challenging issues now a days. Many experiments are now in progress regarding this problem. Among many human activities, mostly six are considered for research in this area. This activity recognition issue can be measured with the help of smartphones and smartphone sensors, along with the connection of Internet of Things (IoT) devices. In this research, an improved deep learning scheme is proposed for the recognition of human activities. A customized Neural Network (NN) model was designed and tested for the research. The proposed model obtained 96.47% accuracy on the HAR with smartphones dataset that is better than most other analyzed models. Sensors such as accelerometer, gyroscope are focused on the data analysis portion of this research work. This article will give a clear idea of the dataset, Machine Learning algorithms, and the effect of the proposed algorithm. en_US
dc.language.iso en_US en_US
dc.publisher Scopus en_US
dc.subject Human activity recognition en_US
dc.subject Smartphone en_US
dc.subject Machine learning models en_US
dc.subject MLP en_US
dc.subject Neural network en_US
dc.subject Activities en_US
dc.title hActNET en_US
dc.title.alternative An Improved Neural Network based Method in Recognizing Human Activities en_US
dc.type Article en_US

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