Abstract
Human activity recognition is the most important field for supporting the everyday lives of elderly people and sometimes finding out suffering to several cognitive dysfunctions like Parkinson's disease and dementia. There are several techniques for studying human behavior, such as recent advancements in artificial intelligence and machine learning (ML) are also taken place. In this paper, a combination of NN with GRU-LSTM-CNN is proposed that enables the efficiency of their integration in daily recognition after classifying data automatically. Since LSTM is another integration of recurrent neural network (RNN), so it can be easily applied over temporal sequences. On the dataset (UCI-HAR) global average pooling layer (GAP), Flatten is applied over the connected layer in order to reduce down the model parameters. The datasets contain data from 18 everyday sensor-based activities as well as takes into account 6 actions within the proposed system. This paper proposed a hybrid model's gated recurrent unit, long short-term memory, convolutional neural network (GRU-LSTM-CNN) have been analyzed on the wearable sensors built-in for smartphones in order to recognize the human activities so to better understand the human behavior, respectively. The proposed technique is successful in providing a better accuracy level of 93.43% comparatively to the previous one.
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Gaurav, K., Roy, B., Bharti, J. (2022). A Hybrid Deep Learning Model for Human Activity Recognition Using Wearable Sensors. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_16
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DOI: https://doi.org/10.1007/978-981-16-9650-3_16
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