Comparisons of physical exposure between workers harvesting apples on mobile orchard platforms and ladders, part 1: Back and upper arm postures
Introduction
Agricultural workers suffer from work-related musculoskeletal disorders (WMSDs), with the highest prevalence being in the low back, shoulders, and upper extremities, respectively (Davis and Kotowski, 2007; Osborne et al., 2012). Based on a study of self-reported pain in the USA (Walker-Bone and Palmer, 2002), the prevalence of low back pain was greater in farming (41%) than in other manual (38%) and non-manual (27%) occupations; the prevalence of shoulder pain was also greater in farming (14%) than in other types of manual labor (9.7%) and in non-manual labor (7.1%) occupations. In apple orchards, manual harvesting was reported as the most frequent task (34.5%), and the median duration of the apple harvest typically lasts four weeks for each variety of apple present in an orchard (McCurdy et al., 2003).
Occupational risk factors for low back and shoulder pain include working with moderate to high forces, holding prolonged elevated postures with the back and arms, and using the arms repetitively (English et al., 1995; van der Windt et al., 2000; Leclerc et al., 2004). In apple harvesting, workers are exposed to shoulder stress while reaching to pick apples and to both back and shoulder stress while carrying apple bags or ladders (Fulmer et al., 2002). Research has found that this population is subjected to and holds awkward postures for a greater percentage of time than construction workers and nurses, two groups with the highest WMSD risk (Earle-Richardson et al., 2004).
The tree fruit industry is important in Washington State, USA. This industry, when compared to others in the state, generates the most revenue and accounts for almost one-third of agricultural product sales in Washington State and is the largest apple supplier in the Unites States (USDA NASS, 2018). Nevertheless, the industry is facing challenges in price competition from foreign producers. In response the financial challenges from foreign producers and to increase profits, the Washington State Tree Fruit Research Commission issued a national roadmap aiming to reduce production costs by focusing on technological innovations used to automate orchard systems (Seavert, 2005). In industrialized orchards in Washington State, small trees are planted close together and trained onto a trellis to form a continuous fruiting wall to increase production per acre. This orchard layout can easily accommodate new and innovative harvesting systems, such as the mobile platform. This elevated platform moves semi-autonomously through two rows of trees (Fig. 1). This technology has been introduced in some industrialized orchards to make harvesting more robust and economical with the intent to increase productivity and decrease costs. No longer needing to climb ladders, workers using this technology can stand on the platform and continuously pick apples while being semi-autonomously transported through the orchard. A second team of workers harvests the apples remaining on the lower parts of the tree from the ground. Nevertheless, given the usage of these platforms, ergonomics associated with automation in agriculture is still understudied and undervalued. Research is needed to investigate whether they reduce the prolonged static loading or repetitive motions of the shoulders and back or put workers at greater risk for shoulder- and/or back-related discomfort and injury.
Previous studies presented several techniques to assess non-neutral work postures through observational methods of which is a direct measurement, incorporating a computerized system with markers on the body or self-contained battery-powered sensors with built-in memory (Spielholz et al., 2001; Bernmark and Wiktorin, 2002; Amasay et al., 2009; Scibek and Carcia, 2012). However, only a few studies have collected objective posture measurements in this way from agricultural workers in field settings due to the variability in the work environment. One of those few previous studies used accelerometers to collect and characterize the trunk postures of forestry workers performing their various regular tasks such as tree seeding, harvesting, logging, log sorting, and vehicle maintenance (Teschke et al., 2009). Another study collected tri-axial acceleration data of the upper arm and characterized the upper arm posture of milking parlor workers when the workers were standing at fixed positions while the cows were moved to the workers and always at the same height (Douphrate et al., 2012).
In attempt to evaluate the work postures in apple harvesting, a previous study proved the feasibility of the technique using tri-axial accelerometers to collect and characterize postural exposure in both ladder-based and mobile-platform-based apple harvesting (Thamsuwan et al., 2019). In this study, posture data were collected from various types of orchards including conventional ones where workers used ladders to harvest Red Delicious apples from tall and wide trees as well as two different trellised orchards with shorter tree walls where workers harvested Jazz and Gala apples from the mobile platforms. The fact that different apple varieties are grown on trees with different characteristics, e.g. height, and require different harvesting techniques, e.g. strip-picking vs. color-picking, suggested that this study did not provide a fair comparison between the ladder-based and mobile-platform-based apple harvesting. To make a reasonable comparison between the two equipment, posture data should be collected from the same type of orchards where the same types of apples are picked from the trees in the same ages.
The aim of the current study was to overcome the challenge of incomparable dataset in the previous study to determine whether the back and upper arm postures and repetitions for apple harvesting differed for ladder and mobile platform workers. Such exposures among orchard workers using ladder-based and mobile-platform-based harvesting methods were collected and compared within a single orchard. This paper contains part 1 of the current study where workers’ exposures to non-neutral back and upper arm posture are being presented, whereas the next paper, part 2 of the study (Thamsuwan et al. in press), describes repetition rates. The postural exposures were characterized in two ways: (1) by the percentage of work time during which the angles of the back and upper arms were greater than certain postural thresholds, and (2) as the 10th, 50th and 90th percentiles of the angles of the back and upper arms.
Section snippets
Subjects
Twenty-four workers who were currently working in a trellised orchard and who had at least one season of harvesting experience (i.e., approximately three months) were selected to participate in the study. All the participants were males of Hispanic origin and native Spanish speakers. This study took place in one orchard in Quincy in 2014, which was different from the previous study (Thamsuwan et al., 2019) conducted in several orchards in Yakima region in 2013, when those subjects in each group
Upper arm postural exposure
The postures (Table 2 and Fig. 5) and the percentage of time above postural thresholds (Table 3 and Fig. 7) show statistically significant differences in arm flexion and elevation between the non-dominant and dominant arms; however, there was no significant difference between the non-dominant and dominant arm abduction. The 10th percentile (minimum) of the upper arm flexion/extension suggested that the workers had their non-dominant arms in greater extension angles than their dominant arms. The
Implications of research findings for the industry
This study presented an objective method for measuring work postures using accelerometers. Non-neutral postures of the upper arms and back were assessed using different angular thresholds of upper arm and torso inclination angles. The study identified differences in the percentage of time spent above various defined angle thresholds between the dominant and non-dominant arms. Mobile platform use reduced the percentage of time workers were exposed to the most extreme postures (% time arms were
Conclusion
Applying the methodology developed and validated in the previous study (Thamsuwan et al., 2019) to characterize the postures of workers harvesting apples, this study was able to compare the upper arm and back inclination of workers harvesting apples while using ladders and mobile elevated orchard platforms. The platform workers had lower exposures to upper arm flexion and abduction than the ladder workers whereas there were no differences in back inclination between the ladder and platform
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This work was supported by the CDC-NIOSH Cooperative Agreement #5 U54 0H007544-11 and the Washington State Medical Aid and Accident Fund. The authors would like to thank the platform manufacturer and all the study participants. We also thank Scott Driscoll, the orchard manager from the collaborating orchard, and the research team members, including Maria Negrete, Pablo Palmández, Margaret Hughes, and Katherine Gregersen for their support in data field collection, and Patrik Rynell for his
References (29)
- et al.
Validation of tri-axial accelerometer for the calculation of elevation angles
Int. J. Ind. Ergon.
(2009) - et al.
A triaxial accelerometer for measuring arm movements
Appl. Ergon.
(2002) - et al.
Accuracy of angular displacements and velocities from inertial-based inclinometers
Appl. Ergon.
(2018) - et al.
Full shift arm inclinometry among dairy parlor workers: a feasibility study in a challenging work environment
Appl. Ergon.
(2012) - et al.
An ergonomic intervention to reduce back strain among apple harvest workers in New York state
Appl. Ergon.
(2005) - et al.
Is what you see what you get? Standard inclinometry of set upper arm elevation angles
Appl. Ergon.
(2015) - et al.
Occupational posture exposure among construction electricians
Appl. Ergon.
(2013) - et al.
Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks
Sensors (Switzerland)
(2014) The Effects of Movement Speeds and Magnetic Disturbance on Inertial Measurement Unit Accuracy: the Implications of Sensor Fusion Algorithms in Occupational Ergonomics Applications
(2017)- et al.
Assessing the ergonomic exposures for drywall workers
Int. J. Ind. Ergon.
(2014)