Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Numerous sensors on smart devices have made it possible to automatically recognize human movement, which might be helpful for intelligent applications like elder care, smart homes, and health monitoring. Nevertheless, implementing an activity recognition model in practical situations faces two main obstacles. First, machine learning models use a large number of labeled data to recognize human activities, which is not always feasible in real scenarios. Second, existing human activity recognition (HAR) systems cannot dynamically adapt to a new action. Furthermore, current methods fail to separate short-term activities from heterogeneous smart devices with varying positions and orientations that have similar sensory reading patterns. To address these issues, we propose Flexi-HAMR, an intelligent adaptive human activity monitoring and recognition system that dynamically recognizes activities using online, real-time activity signals. Many empirical findings show that the suggested flexible activity recognition model performs competitively on multiindividual activity identification tasks and has a comparatively more vital generalization ability.