Behavioral logic and model analysis of smart phone user interface interaction design

Smartphones have become constant companions in our daily lives, shaping the way we communicate, access information, and stay connected. But for these devices to truly serve us well, their interfaces need to reflect how people actually use them—not just what designers assume. Many existing models that try to understand smartphone use overlook the fact that our touch patterns change based on context and routine, and rarely adapt to the needs of different groups, like older adults.To tackle this, we developed a new approach called the Behaviour-Based Whale Optimization Self-Attention BiGRU model (BWO-SAtt-GRU). We worked with a large dataset that used movement sensors on the backs and thighs of senior users, allowing us to capture the subtle ways people handle their devices over time. Our model combines several advanced techniques to help the system focus on what really matters and learn more effectively from the data.When we compared our model to five popular deep-learning approaches, BWO-SAtt-GRU consistently outperformed them by achieving the highest accuracy, recall, and F1-score. These results show that technology designed with a genuine understanding of human behavior can make smarter predictions and deliver more responsive, adaptive experiences. Our findings pave the way for smartphones that are easier to use and more personalized—especially for older adults and anyone whose interaction style doesn’t fit the usual mold.