Assessment of Comfort Variation among Different Types of Driving Agricultural Tractors: Traditional, Satellite-Assisted and Semi-Automatic

Romano, Elio; Bisaglia, C.; Calcante, Aldo; Oberti, R.; Zani, Alberto; Vinnikov, Denis; Marconi, Andrea; Vitale, Ermanno; Bracci, Massimo; Rapisarda, Venerando

doi:10.3390/ijerph17238836

Cited by 11 publications

(12 citation statements)

References 41 publications

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“…Ng et al (1995) pointed out that the pressure is mainly concentrated in the lumbar region in the seat backrest, showing a worse distribution in comparison with thighs and hips [8]. Therefore, the applied research about lumbar support of tract seat has broad practical prospects [9,10]. Corlett et al (1984) emphasized the significance of the lumbar lordosis to minimize muscular effort in sitting positions [11].…”

Section: Introductionmentioning

confidence: 99%

Influence of lumbar support on tractor seat comfort based on body pressure distribution

et al. 2023

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To solve the problem of lumbar spine injuries of tractor drivers, lumbar support devices were added to the tractor. The purpose of study is to design lumbar supports with different protrusion thicknesses to adjust the load on the operator’s lumbar under whole-body vibration. Integrating pressure distribution measurement with subjective assessment, pressure distribution on the contact surface between ten male tractor drivers and the seat under conditions of different lumbar supports and driving speeds and the influence of the presence of lumbar supports and their thickness on the subjects’ comfort were analyzed. The results demonstrated that there existed a certain correlation between the pressure distribution indexes and the drivers’ subjective evaluation, and the pressure distribution indexes could reflect ride comfort objectively. The 3 cm thick lumbar support did not improve ride comfort significantly, while the 9 cm thick support exerted too much stress on the waist, causing the lumbar spine to lean forward excessively and aggravating lumbar fatigue. Most subjects in the study would prefer to increase the thickness of the lumbar supports to approximately 6 cm. Based on the pressure distribution test, this study conducted an analysis of the influence of lumbar supports in tractors, which could provide a reference design for tractor seats.

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Section: Introductionmentioning

confidence: 99%

Influence of lumbar support on tractor seat comfort based on body pressure distribution

et al. 2023

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“…Recently, the accomplishment of agricultural operations carried-out with tractors has been heading toward the integration of traditional mechanics with geolocation technologies to bring farming procedures closer to the logic of precision agriculture. The possibility of controlling the tractor steering by using GNSS is an important contribution to improving the efficiency of agricultural practices and allows savings of time, fuel, labor, and production factors contributing to the economic and environmental sustainability of the agricultural process [ 46 ]. The automatic steering system or autopilot system tractor combines digital analysis and the image processing method, cameras, and GPS.…”

Section: Precision Crop Farmingmentioning

confidence: 99%

Precision Agriculture for Crop and Livestock Farming—Brief Review

Monteiro

Santos

Gonçalves

2021

Animals

110

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In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.

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“…Typical EMG features utilized for muscle activity assessments include root mean square (RMS), mean frequency (MNF), median frequency (MDF) and percentage of maximum voluntary contraction known as muscle workload (%MVC) [ 4 ]. %MVC is obtained as the ratio of the RMS signal for the muscle under contraction in real time to the signal for muscle under maximum contraction measured without any constraints or load, which represents muscular involvements for a given operation [ 11 ]. EMG also enables the identification of comfort level and body postures for a given operation [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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

Hota

Tewari

Chandel

2023

Sensors

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Dynamic muscular workload assessments of tractor operators are rarely studied or documented, which is critical to improving their performance efficiency and safety. A study was conducted to assess and model dynamic load on muscles, physiological variations, and discomfort of the tractor operators arriving from the repeated clutch and brake operations using wearable non-invasive ergonomic transducers and data-run techniques. Nineteen licensed tractor operators operated three different tractor types of varying power ranges at three operating speeds (4–5 km/h), and on two common operating surfaces (tarmacadam and farm roads). During these operations, ergonomic transducers were utilized to capture the load on foot muscles (gastrocnemius right [GR] and soleus right [SR] for brake operation and gastrocnemius left [GL], and soleus left [SL] for clutch operation) using electromyography (EMG). Forces exerted by the feet during brake and clutch operations were measured using a custom-developed foot transducer. During the process, heart rate (HR) and oxygen consumption rates (OCR) were also measured using HR monitor and K4b2 systems, and energy expenditure rate (EER) was determined using empirical equation. Post-tractor operation cycle, an overall discomfort rating (ODR) for that operation was manually recorded on a 10-point psychophysical scale. EMG-based maximum volumetric contraction (%MVC) measurements revealed higher strain on GR (%MVC = 43%), GL (%MVC = 38%), and SR (%MVC = 41%) muscles which in normal conditions should be below 30%. The clutch and brake actuation forces were recorded in the ranges of 90–312 N and 105–332 N, respectively and were significantly affected by the operating speed, tractor type, and operating surface (p < 0.05). EERs of the operators were measured in the moderate-heavy to heavy ranges (9–24 kJ/min) during the course of trials, suggesting the need to refine existing clutch and brake system designs. Average operator ODR responses indicated 7.8% operations in light, 48.5% in light-moderate, 25.2% in moderate, 10.7% in moderate-high, and 4.9% operations in high discomfort categories. When evaluated for the possibility of minimizing the number of transducers for physical workload assessment, EER showed moderate-high correlations with the EMG signals (rGR = 0.78, rGL = 0.75, rSR = 0.68, rSL = 0.66). Similarly, actuation forces had higher correlations with EMG signals for all the selected muscles (r = 0.70–0.87), suggesting the use of simpler transducers for effective operator workload assessment. As a means to minimize subjectivity in ODR responses, machine learning algorithms, including K-nearest neighbor (KNN), random forest classifier (RFC), and support vector machine (SVM), predicted the ODR using body mass index (BMI), HR, EER, and EMG at high accuracies of 87–97%, with RFC being the most accurate. Such high-throughput and data-run ergonomic evaluations can be instrumental in reconsidering workplace designs and better fits for end-users in terms of agricultural tractors and machinery systems.

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Assessment of Comfort Variation among Different Types of Driving Agricultural Tractors: Traditional, Satellite-Assisted and Semi-Automatic

Cited by 11 publications

References 41 publications

Influence of lumbar support on tractor seat comfort based on body pressure distribution

Influence of lumbar support on tractor seat comfort based on body pressure distribution

Precision Agriculture for Crop and Livestock Farming—Brief Review

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

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