Optimizing Cotton Picker Cab Layout Based on Upper-Limb Biomechanics Using the AMS-RF-DBO Framework

Prolonged operation of cotton picker poses significant risks of work-related musculoskeletal disorders (WMSDs), primarily driven by non-ergonomic cab layouts that fail to accommodate the unique “left-hand steering, right-hand lever” operational mode. Traditional optimization methods, relying on general digital human models or isolated surface electromyography (sEMG) measurements, often lack the physiological fidelity and computational efficiency for high-dimensional, personalized design. To address this interdisciplinary challenge in agricultural engineering and ergonomics, this study proposes a novel AMS-RF-DBO framework that integrates high-fidelity biomechanical simulation with intelligent optimization. A driver–cabin biomechanical model was developed using the AnyBody Modeling System (AMS) and validated against sEMG data (ICC = 0.695). This model generated a dataset linking cab layout parameters to maximum muscle activation (MA). Using steering wheel and control lever coordinates (X, Y, Z) as inputs, a Random Forest (RF) regression model demonstrated strong performance (R2 = 0.91). Optimization with the Dung Beetle Optimizer (DBO) algorithm yielded an optimal configuration: steering wheel (L1 = 434 mm, H1 = 738 mm, θ = 32°) and control lever (L2 = 357 mm, H2 = 782 mm, M = 411 mm), reducing upper-limb MA from 3.82% to 1.47% and peak muscle load by 61.5%. This study not only provides empirical support for ergonomic cab design in cotton pickers to reduce operator fatigue and health risks but also establishes a replicable technical paradigm for ergonomic optimization of other specialized agricultural machinery.