International Journal of Intelligent Systems Technologies and Applications (4 papers in press)

Regular Issues

• Integrating Incremental and Continuous Learning into an Online Emotion Recognition Model  
  by Caihua Chen, Long Xinyuan 
  Abstract: In order to overcome the problems of low accuracy, F1 score, and long recognition time in traditional online emotion recognition methods, a new online emotion recognition model design method integrating incremental and continuous learning is proposed. Systematically collect and preprocess text, speech, and image data. The incremental learning model adopts a dynamic preservation set strategy based on scaling and translation selection method to select informative samples from multimodal data. Build an online emotion recognition model based on continuous learning using the selected samples. This model introduces a positive definite symmetric matrix to constrain the gradient update direction, and achieves feature decoupling and reuse through a task identifier driven feature encoding layer to obtain online emotion recognition results. The experimental results show that the accuracy of the proposed method can reach up to 98%, with an F1 value maintained above 0.95 and an average recognition time of only 1.24 s.
  Keywords: Incremental learning; Continuous learning; Online emotions Recognition model; Dynamic preservation set; Positive definite symmetric matrix; feature encoding.
  DOI: 10.1504/IJISTA.2026.10077230
   
  
• Research on a Computer Vision-Based Quantifying System for Movement Standardisation in Baduanjin Exercises for the Elderly  
  by Xinyu Wang, Tao Wu, Yanzhao Guo, Junxiang Qiu, Mingli Zhou 
  Abstract: This study develops a computer vision system to quantify movement standardisation in elderly Baduanjin exercise .Using smartphone cameras and OpenPose framework with human pose estimation (HPE), the system detects body keypoints through deep convolutional neural networks. It calculates standardized scores (0-1 scale) via weighted Euclidean distances, specifically adapted for elderly physical traits like mild hunching. Error reduction techniques (outlier filtering/interpolation) improved detection accuracy by 15%. In a 12-week trial with 52 participants, the system tracked significant movement standardisation improvements from 0.871 to 0.876 (P<0.0167). Compared to manual assessments, this solution eliminates subjective bias while enabling home-based exercise monitoring through accessible technology. The system fills a technical gap in geriatric rehabilitation by providing precise data support for personalized training plans. Future development may incorporate 3D pose estimation to enhance clinical utility.
  Keywords: Computer Vision; Human Pose Estimation (HPE); Movement Standardisation Quantification.
  DOI: 10.1504/IJISTA.2026.10077781
   
  
• Research on constant temperature control strategy for nonlinear large time-delay fluid systems based on fuzzy Smith predictor  
  by Panpan Li, Chen Niu 
  Abstract: Precise temperature control of fluid systems is critical in industrial and agricultural applications but is often challenged by large thermal inertia, pure transport delays, and nonlinear parameter variations. Conventional PID controllers and standard Smith predictors often fail to provide satisfactory performance under such complex conditions, suffering from overshoot or instability due to model mismatch. To address these issues, this paper proposes a novel constant temperature control strategy based on a Fuzzy-Smith predictor. The proposed method integrates a fuzzy logic inference engine to adaptively tune the PID parameters online, compensating for system nonlinearities, while a modified Smith structure is employed to effectively mitigate the impact of time delays. Extensive numerical simulations and physical experiments on an STM32-based fluid heating platform were conducted. The results demonstrate that the proposed strategy achieves a settling time reduced by approximately 40% compared to conventional PID, with negligible overshoot (<0.5 C) and superior robustness against parameter perturbations (30% mismatch) and external disturbances.
  Keywords: fluid temperature control; large time delay; nonlinear system; fuzzy logic control; Smith predictor; adaptive control.
  DOI: 10.1504/IJISTA.2026.10078186
   
  
• Study on Multimodal Behaviour Detection of Students in Chinese Classrooms Based on the Internet of Things  
  by Shuai Yu, Linlin Zhu 
  Abstract: Detecting multimodal behaviors of students in Chinese classrooms is of great significance for comprehensively and accurately grasping students' learning status and optimizing teaching strategies. A multimodal behavior detection method of students in Chinese classrooms based on the Internet of things is proposed. Firstly, with the support of IoT technology, cameras and pyroelectric sensors are used to collect data,which is then fused and processed to obtain high-quality data. Secondly, the collected images undergo denoising, grayscale transformation, enhancement, and other operations. Finally, the processed data is input into an improved YOLOv5 model, where the Backbone network extracts features and the Neck layer fuses low-level and high-level information to generate three-scale feature maps.Each grid predicts bounding boxes and outputs relevant information,achieving multimodal behavior detection of students in Chinese classrooms.Experimental results show that the average detection accuracy of this method reaches 99.65%, with an AUC value consistently higher than 0.95
  Keywords: Internet of Things; Chinese classrooms; Student; Multimodal behavior; detection.
  DOI: 10.1504/IJISTA.2026.10078466