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.
Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic in…
The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, incr…
Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can sto…
Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for…
Wearable technologies have slowly invaded our lives and can easily help with our day-to-day tasks. One area where wearable devices can shine is in human activity recognition, as they can gather sensor data in a non-intrusive way. We describe a real-time activity recognition syste…
The recognition of human movements is a crucial aspect of AI-related research fields. Although methods using vision and sensors provide more valuable data, they come at the expense of inconvenience to users and social limitations including privacy issues. WiFi-based sensing metho…