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Serpush, Fatemeh, Keyvanpour, Mohammadreza, Menhaj, Mohammad bagher. (1402). Remote elderly healthcare: a robust deep learning approach for wearable sensors-based complex activities recognition. نشریه مهندسی عمران امیرکبیر, 55(1), 109-126. doi: 10.22060/miscj.2023.21984.5308
Fatemeh Serpush; Mohammadreza Keyvanpour; Mohammad bagher Menhaj. "Remote elderly healthcare: a robust deep learning approach for wearable sensors-based complex activities recognition". نشریه مهندسی عمران امیرکبیر, 55, 1, 1402, 109-126. doi: 10.22060/miscj.2023.21984.5308
Serpush, Fatemeh, Keyvanpour, Mohammadreza, Menhaj, Mohammad bagher. (1402). 'Remote elderly healthcare: a robust deep learning approach for wearable sensors-based complex activities recognition', نشریه مهندسی عمران امیرکبیر, 55(1), pp. 109-126. doi: 10.22060/miscj.2023.21984.5308
Serpush, Fatemeh, Keyvanpour, Mohammadreza, Menhaj, Mohammad bagher. Remote elderly healthcare: a robust deep learning approach for wearable sensors-based complex activities recognition. نشریه مهندسی عمران امیرکبیر, 1402; 55(1): 109-126. doi: 10.22060/miscj.2023.21984.5308

Remote elderly healthcare: a robust deep learning approach for wearable sensors-based complex activities recognition

AUT Journal of Modeling and Simulation
دوره 55، شماره 1، شهریور 2023، صفحه 109-126    اصل مقاله (1017.91 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2023.21984.5308
نویسندگان
Fatemeh Serpush1؛ Mohammadreza Keyvanpour* 2؛ Mohammad bagher Menhaj3
1Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
3Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
چکیده
Human activity recognition system (HARS) is critical for monitoring individuals' health and movements, especially the elderly and the disabled, in different environments. Wearable sensors are valuable tools for information acquisition due to the mobility of people. Researchers have proposed different methods for sensor-based HAR, which face challenges. This paper uses deep learning (DL), which combines the deep convolution neural network (CNN) and long short-term memory (DeepConvLSTM) to extract and select features to recognize high-performance activity. Then, the Softmax-Support Vector Machine (SofSVM) performs the classification and recognition operations. This paper uses a comprehensive nested pipe and filter (NPF) architecture. Filters are usually independent, but some filters can be bidirectional to improve the performance of complex activity recognition. There is two-way communication between convolutional layers and long short-term memory (LSTM). The Opportunity dataset is public and includes complex activities. The results with this dataset display that the proposed work improves the weighting F-measure of HAR. This paper has also shown other experiments; that involve comparing the number of sensors, max-pooling size, and convolutional filter size. According to this dataset, the proposed method weighting F-measure is 0.929. The proposed approach is indeed effective in activity recognition, and the NPF architecture covers all the components of the activity recognition process.
کلیدواژه‌ها
Complex human activity؛ convolutional neural networks؛ Long Short-term Memory؛ Wearable Sensors؛ Support Vector Machine
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