Non-Invasive Fatigue Detection and Human–Machine Interaction Using LSTM and Multimodal AI: A Case Study

Fatigue in high-stress work environments poses significant risks to employee performance and safety. This study introduces a non-invasive fatigue detection system utilizing facial parameters processed via a Long Short-Term Memory (LSTM) neural network, coupled with a human–machine interaction interface via a Telegram chatbot. The system analyzes eye blink patterns and facial expression changes captured through a webcam, achieving an accuracy of 92.35% on the UTA-RLDD dataset. An interactive feedback mechanism allows users to verify predictions, enhancing system adaptability. We further propose a multimodal AI framework to integrate physiological and environmental data, laying the groundwork for broader applications. This approach provides an effective solution for early fatigue detection and adaptive collaboration between humans and machines in real-time settings.