GestureStudy: Mouse Gesture Recognition Algorithm and Experimental Program Based on BP Neural Network

The application of BP neural networks in mouse gesture recognition represents a highly practical machine learning case study. The core objective of mouse gesture recognition is to transform continuous trajectories drawn by users on screen into specific operational commands, which involves two critical processes: trajectory feature extraction and pattern classification.

First, the system must capture coordinate sequences of mouse movements and preprocess raw data. Common preprocessing techniques include trajectory smoothing, sampling point normalization, and coordinate standardization, implemented to eliminate interference caused by varying drawing speeds and amplitudes. Subsequently, distinctive features are extracted from trajectories, such as curvature variations, directional angles, and velocity changes - these features will serve as neural network inputs.

The BP neural network continuously adjusts network weights through backpropagation algorithms, ultimately learning pattern characteristics of different gestures. The input layer size typically corresponds to feature vector dimensions, while the output layer nodes match the number of gesture categories to be recognized. Hidden layer design requires balancing model complexity and generalization capability, with optimal layer count and node numbers often determined through cross-validation in practice.

When designing experimental programs, a modular architecture is recommended, decoupling functionalities like data acquisition, feature extraction, model training, and recognition testing. The training phase requires collecting sufficiently diverse samples to ensure model robustness while maintaining sample balance. During testing, beyond accuracy metrics, confusion matrix analysis should be employed to examine misrecognition patterns for specific gestures.

Technology extensions include integrating convolutional neural networks for processing trajectory image features or incorporating temporal models to capture gesture dynamic characteristics. Practical implementations must also address engineering considerations like user personalized differences and real-time performance requirements.