Karim Gasmi¹, Olfa Hrizi^1,*, Najib Ben Aoun^2,3, Ibrahim Alrashdi¹, Ali Alqazzaz⁴, Omer Hamid⁵, Mohamed O. Altaieb¹, Alameen E. M. Abdalrahman¹, Lassaad Ben Ammar⁶, Manel Mrabet⁶, Omrane Necibi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817 - 30 May 2025

Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

Ali Mohammad Alqudah¹, Zahra Moussavi^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3753-3841, 2025, DOI:10.32604/cmc.2025.063643 - 19 May 2025

Abstract This review presents a comprehensive technical analysis of deep learning (DL) methodologies in biomedical signal processing, focusing on architectural innovations, experimental validation, and evaluation frameworks. We systematically evaluate key deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and hybrid systems across critical tasks such as arrhythmia classification, seizure detection, and anomaly segmentation. The study dissects preprocessing techniques (e.g., wavelet denoising, spectral normalization) and feature extraction strategies (time-frequency analysis, attention mechanisms), demonstrating their impact on model accuracy, noise robustness, and computational efficiency. Experimental results underscore the superiority of deep learning… More >

Hanmin Ye^1,2, Yinghui Zhou¹, Xiaomei Tao^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1709-1732, 2023, DOI:10.32604/csse.2023.039186 - 28 July 2023

Abstract With the popularity of online learning and due to the significant influence of emotion on the learning effect, more and more researches focus on emotion recognition in online learning. Most of the current research uses the comments of the learning platform or the learner’s expression for emotion recognition. The research data on other modalities are scarce. Most of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on data. Because of the need for other modal research data, we construct a synchronous multimodal data set for analyzing learners’… More >

Wen-Tsai Sung¹, Sung-Jung Hsiao^2,*

Computer Systems Science and Engineering, Vol.43, No.1, pp. 59-75, 2022, DOI:10.32604/csse.2022.020745 - 23 March 2022

Abstract With technological and social development in recent decades, people have begun pursuing more comfortable lives that frequently feature household pets that are treated like members of the family. On average, one out of every three households has a pet. This has also led to the creation and growth of many businesses in the pet industry. A few companies have developed a system that allows busy office workers to remotely care for pets at home based on the Internet of Things and an intelligent adjustment function. As owners of two dogs, the authors of this study… More >

Yun-Kyu Lee¹, Dong-Sung Pae², Dae-Ki Hong³, Myo-Taeg Lim¹, Tae-Koo Kang^4,*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 657-673, 2022, DOI:10.32604/iasc.2022.020849 - 17 November 2021

Abstract With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record… More >

Wen-Tsai Sung¹, Sung-Jung Hsiao^2,*

Computer Systems Science and Engineering, Vol.41, No.1, pp. 289-304, 2022, DOI:10.32604/csse.2022.020453 - 08 October 2021

Abstract The safety of patients and the quality of medical care provided to them are vital for their wellbeing. This study establishes a set of RFID (Radio Frequency Identification)-based systems of patient care based on physiological signals in the pursuit of a remote medical care system. The RFID-based positioning system allows medical staff to continuously observe the patient's health and location. The staff can thus respond to medical emergencies in time and appropriately care for the patient. When the COVID-19 pandemic broke out, the proposed system was used to provide timely information on the location and… More >