HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUM

Randa Allafi, Fadwa Alrowais, Mohammed Aljebreen, Noha Negm, Ahmed S. Salama and Radwa Marzouk
Additional contact information
Randa Allafi: Department of Computer Science, College of Sciences, Northern Border University, Arar, Saudi Arabia
Fadwa Alrowais: ��Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammed Aljebreen: ��Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia
Noha Negm: �Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia
Ahmed S. Salama: �Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
Radwa Marzouk: ��Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-20

Abstract: Human activity recognition (HAR) employs a broad range of sensors that generate massive volumes of data. Traditional server-based and cloud computing methods require all sensor data to be sent to servers or clouds for processing, which leads to high latency and bandwidth costs. The long-term data transfer between servers and sensors maximizes the cost of latency and bandwidth. Real-time processing is, nevertheless, highly required for human action identification. By bringing processing and quick data storage to the sensors instead of depending on a central database, edge computing is rapidly emerging as a solution to this issue. Artificial intelligence is responsible for most HAR, which demands a lot of processing power and calculation. Artificial intelligence (AI) needs more computation which is not allowed by edge computing. So Edge intelligence, which allows AI to operate at the network edge for actual-time applications, has been made possible by the advent of binarized neural networks. To provide less latency and less memory for human activity identification at the edge network, we construct a hybrid deep learning-based binarized neural network (HDL-Binary Dilated DenseNet) in this research. Fractal HAR optimization algorithms could be applied to these algorithms. For example, fractal-HAR optimization techniques might be used to provide less latency and less memory human activity identification at the edge network. Using three sensors-based human activity detection datasets such as Radar HAR dataset, UCI HAR dataset and UniMib-SHAR dataset, we implemented the Hybrid Binary Dilated Dense Net. It is then assessed using four criteria. Comparatively, the Hybrid Binary Dilated DenseNet performs better with 99.6% radar HAR dataset which is highest than other models like CNN-BiLSTM and GoogLeNet.

Keywords: Radar Sensors; Wearable Sensors; Edge Computing; Edge Intelligence; Neural Networks; Fractal HAR (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25400262

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