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Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques.

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  • 1Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur 721302, WB, India.

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Summary

Tractor operators experience high muscular strain and discomfort during clutch and brake operations. Machine learning accurately predicts discomfort, aiding in designing safer, more efficient agricultural machinery.

Keywords:
dynamic operator workloadergonomic transducerslower limb musclesmachine learningtractors

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Area of Science:

  • Ergonomics and Human Factors
  • Occupational Health and Safety
  • Agricultural Engineering

Background:

  • Dynamic muscular workload assessments for tractor operators are under-researched, impacting operator efficiency and safety.
  • Repeated clutch and brake operations in tractors impose significant physiological and ergonomic loads.

Purpose of the Study:

  • To assess and model the dynamic muscle load, physiological variations, and discomfort experienced by tractor operators.
  • To evaluate the effectiveness of wearable ergonomic transducers and data-run techniques for workload assessment.
  • To explore the use of machine learning for predicting operator discomfort and optimizing machinery design.

Main Methods:

  • Nineteen tractor operators performed tasks on three tractor types across varied speeds and surfaces.
  • Electromyography (EMG) and custom foot transducers measured muscle load and actuation forces.
  • Heart rate (HR), oxygen consumption rates (OCR), and energy expenditure rates (EER) were recorded.
  • Operator-reported overall discomfort ratings (ODR) were collected, and machine learning models were applied.

Main Results:

  • EMG revealed high muscle strain (e.g., gastrocnemius right at 43% MVC), exceeding recommended levels.
  • Clutch and brake forces were significantly influenced by operating speed, tractor type, and surface (p < 0.05).
  • Energy expenditure rates (EER) indicated moderate-heavy to heavy physical workload (9-24 kJ/min).
  • Machine learning models (KNN, RFC, SVM) predicted ODR with high accuracy (87-97%), with RFC being most effective.

Conclusions:

  • Existing tractor clutch and brake systems may require design refinement to reduce operator strain.
  • Energy expenditure and actuation forces correlate well with EMG signals, suggesting simpler workload assessment methods.
  • Machine learning offers a robust approach to objectively assess and predict operator discomfort, informing future ergonomic designs for agricultural machinery.