With the rapid advancement of global development, there is an increasing demand for health monitoring technologies. Human activity recognition and monitoring systems offer a powerful means of identifying daily movement patterns, which helps in understanding human behaviors and provides valuable insights for life management. This paper explores the issue of human motion state recognition using accelerometers and gyroscopes, proposing a human activity recognition system based on a majority decision model that integrates multiple machine learning algorithms. In this study, the majority decision model was compared with an integer programming model, and the accuracy was assessed through a confusion matrix and cross-validation based on a dataset generated from 10 volunteers performing 12 different human activities. The average activity recognition accuracy of the majority decision model can be as high as 91.92%. The results underscore the superior accuracy and efficiency of the majority decision model in human activity state recognition, highlighting its potential for practical applications in health monitoring systems.
The possibility of implementing intelligent irrigation has a number of undeniable advantages, mainly including the fact that the time can be determined and the volume of irrigation water can be adapted to specific plant types on a specific soil. A neural network has been trained…
Point cloud perception is important in autonomous driving, robotics, and other security-critical 3D systems, yet learned point cloud classifiers remain vulnerable to transferable adversarial perturbations. A central difficulty in transfer-based black-box attacks is surrogate over…
With the accelerating commercialization of autonomous driving technology, robotaxis have emerged as a significant force in reshaping urban transportation systems. However, their service efficiency and system resilience depend heavily on the spatial layout and network structure of…
Industrial surface defect detection faces challenges of complex textures, diverse defect morphologies, and scarce labeled data, especially for non-woven fabrics. This paper proposes a dual-domain reverse distillation algorithm for unsupervised defect detection (DDRD). The algorit…
We present a neuro-fuzzy digital twin for cardiac disease recognition on the PTB-XL dataset that keeps the accuracy of a strong convolutional model while exposing its reasoning as readable fuzzy rules. The key design choice is to separate the two jobs instead of forcing one netwo…
Primary prevention is essential to reduce disease burden before clinical onset, yet it remains less systematically integrated into care than diagnosis and treatment. Although artificial intelligence (AI) is increasingly used in medicine, most applications have focused on secondar…