Gesture Recognition System Based on LSTM Neural Network: Real-Time Dynamic Gesture Learning and Interactive Applications

The gesture recognition system based on LSTM neural network is a real-time dynamic gesture interaction application that supports user-defined training. Its core features include: using LSTM to process temporal features for accurate recognition, allowing users to record and train custom gestures, converting recognition results into text, trigger actions, or voice outputs, suitable for scenarios such as auxiliary communication (e.g., for people with speech impairments) and software control. The system uses local data processing to ensure user privacy and security.

As a natural interaction method, gestures are more intuitive and do not require physical contact compared to traditional input methods, which is particularly important for users with special needs. Static gestures focus on spatial posture, while dynamic gestures need to capture temporal trajectories (such as waving or sliding), which are more technically challenging but have wider applications. In deep learning, LSTM can effectively handle long sequence dependencies, bringing a breakthrough to dynamic gesture recognition.

The core innovation of the system lies in its custom training capability. The workflow is divided into two stages: training and recognition. In the training stage, users record gestures, and the system extracts temporal features of hand key points to train the LSTM model. In the recognition stage, it captures video streams in real time to match the model. The output can be configured as text display, action triggering, or TTS voice, flexibly adapting to auxiliary communication and contactless control scenarios.

The training process is intuitive: enter the training tab and input the gesture name → click record to display the gesture for about 5 seconds → stop recording; it is recommended to record the same gesture multiple times from different angles to improve accuracy; after completing the training, click to save the model, and the parameters are stored in the local /models folder, which can be backed up and restored.

After training is completed, switch to recognition mode and click start recognition to detect in real time. The recognition results are displayed immediately; enabling TTS allows voice output (voice settings can be adjusted). The delay is hundreds of milliseconds under recommended configuration; connecting to a power source can avoid performance impact from power-saving mode; a simple background and sufficient light help improve recognition effects.

The application scenarios are wide: auxiliary communication for people with speech impairments in the accessibility field, smart home control (turning lights on/off, adjusting volume), contactless operations in demonstration teaching. Technically, it can expand more gesture categories, integrate other neural networks, or port to platforms; the open-source architecture supports community contributions.

Privacy protection uses local processing: all video data is processed locally, models are only stored locally, and users have full control over data; when uninstalling, delete the software and model files through the Windows Control Panel to completely remove data and eliminate the risk of cloud leakage.

This project demonstrates the practical value of LSTM in human-computer interaction. Custom training achieves a personalized experience, local processing ensures privacy, and low-threshold operations reduce the difficulty of use. In the future, with technological progress, gesture recognition will be more accurate and fast, realizing natural and inclusive interaction in more fields.