A Study of Ergonomic Workload Evaluation on Driving and Working of Excavator using Virtual Reality Motion Simulator

Objective: The aim is to verify the usability of an excavator simulator by comparing the physical and mental workload of excavator operators according to work scenarios.<br/><br/>Background: Existing studies on excavator workload have primarily focused on vibration and posture. However, there is a lack of research quantitatively assessing both physical and mental workload across various task scenarios. To address this gap, this study conducts a preliminary evaluation of operator workload using a simulator capable of task-specific analysis.<br/><br/>Method: The excavator operation process was divided into six tasks: boarding, seating, driving, working, maintenance, and parking. Ergonomic evaluation factors were derived through expert and operator interviews as well as previous research. Among these, the core tasks—driving and excavation/loading—were selected for detailed analysis. Ten experienced excavator operators participated in the study. Each participant was equipped with eight sEMG channels (four muscles on both arms: Upper Trapezius, Biceps Brachii, Brachioradialis, and Flexor Carpi Ulnaris) and one HRV sensor positioned under the left chest. Two scenarios were tested: ① driving on slopes of 25°, 30°, and 35°, and ② excavation and truck loading. Muscle fatigue was assessed using the zero-crossing difference ratio from EMG data, while SDNN, RMSSD, and LF/HF ratio were used as indicators of mental stress.<br/><br/>Results: Mental workload, as indicated by SDNN, increased by 58.6% (to 24.9ms) during slope driving and by 60.3% (to 23.9ms) during excavation and loading, compared to the pre-task baseline. Muscle fatigue patterns varied by task: forearm muscles showed higher fatigue during excavation and loading, while upper arm muscles were more fatigued during slope driving.<br/><br/>Conclusion & Application: The findings confirm that both muscle fatigue and mental stress vary significantly depending on the task scenario. These differences can be quantitatively measured using sEMG and HRV in a simulator environment. This approach can be effectively utilized in future R&D to reduce operator workload and optimize excavator task design.