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

Abstract

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. This study presents a smart cushion–based framework to continuously monitor the mental fatigue states of construction equipment operators (CEOs) during daily work. The proposed solution has clear advantages: it (1) is nonintrusive since it no longer requires sensors attached to the skin of operators, (2) is not sensitive to dynamic lighting conditions and does not generate privacy concerns, (3) is easy-to-use since it can be placed on the operator’s seat or seatback and does not need additional power. Experimental results also demonstrated that the proposed Gaussian mixture model and Bidirectional Long-Short-Term Memory model can achieve effective data processing and accurate fatigue recognition. The proposed system can provide construction managers with a quantitative and reliable assessment tool to measure CEOs’ mental workloads and support the intervention (e.g., worker shifts and breaks). If wirelessly connected to a smartphone, the smart cushion can conveniently provide early warnings to operators and their managers to alert the operators at stake to take breaks/rests to avoid mental fatigue-related ill consequences. Moreover, the proposed solution could be used to study and prevent the risks resulting from prolonged sitting, which is a grave occupational health and safety problem plaguing many industries.