Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Multimodal Technologies and Interaction

DOI: 10.3390/mti9060063

Authors: Muon Ha, Yulia Shichkina, Xuan-Hien Nguyen

Institutions: Trường Đại học Thông tin Liên lạc, Saint Petersburg State Electrotechnical University, Hanoi University of Industry

Language: English

Topic: Sleep and Work-Related Fatigue

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930