Monitoring Mental Fatigue of Construction Equipment Operators: A Smart Cushion–Based Method with Deep Learning Algorithms

Construction equipment operators (CEOs), who are required to work in seated positions for prolonged periods, often develop excessive mental fatigue, causing human error-related accidents, lower productivity, and psychological illnesses. However, the current practice for assessing fatigue is limited on construction sites. Previous studies utilizing smartwatches, electroencephalography, or eye-tracking technologies are intrusive and not convenient since they require operators to wear special devices, while vision-based solutions are sensitive to lighting conditions and have serious privacy concerns. There is a demand for continuously and accurately monitoring CEOs’ mental fatigue levels without causing discomfort and aversion. This study introduces a noninvasive and noncontact smart cushion method to bridge the knowledge gap. We first developed a smart cushion system incorporating optical fiber sensors to collect human heartbeat and respiration data. Then, we adopted the Bidirectional Long-Short-Term Memory (BiLSTM) model to recognize fatigue states. An experiment was conducted in which data was collected from 16 subjects engaged in simulated excavation tasks. Experimental results demonstrate the feasibility of the proposed method, and the BiLSTM model obtained an accuracy of 94.0%. The proposed smart cushion method could also be convenient for understanding ergonomic risks resulting from prolonged sitting, a grave occupational health and safety problem that plagues various industries.