Innovative ergonomic design for muscular fatigue prevention among mango size-sorting workers

This study proposes an innovative ergonomic design for muscular fatigue prevention among mango size-sorting workers. The study consists of (1) Task analysis to prevent work-related musculoskeletal disorders (WMSDs) by using Borg CR-10, the Rapid Upper Limb Assessment (RULA), the Rapid Entire Body Assessment (REBA) and (2) Design ergonomic innovation for the elimination of muscular fatigue and analysis of productivity and efficiency. Phase I, a mixed ergonomic tool analysis algorithm to prioritize work-related musculoskeletal problems: The case study focused on mango-harvesting farmers. The roles of work, including (1) mango harvesting, (2) mango transporting, (3) mango size-sorting, and (4) mango weighing and transporting to the truck, were collected using a modified method analysis consisting of (i) measuring the subjective feeling of fatigue, to estimate the perceived physical exertion while working on a mango-harvesting farm, based on the Borg CR-10 with a modified Standardized Nordic Questionnaire and (ii) posture analysis using RULA and REBA score sheets. The output of subjective feelings of fatigue and posture analysis were normalized and combined using the theorem of power superposition to establish the fatigue effective index (FEI) for determining decision-making priorities to solve ergonomics-based task problems. The result provided the FEI of mango-harvesting farmers, ranked as follows: (1) size-sorting task, (2) weight-lifting task, (3) harvesting task, and (4) transporting task. A proper ergonomics intervention should be implemented to reduce WMSDs among mango farmers, especially in size-sorting tasks.Phase II, an innovative machine for muscular fatigue prevention among mango size-sorting workers was designed. It uses image processing technology to classify and sort the size of mango instead of the human worker. The machine can be operated with a speed of 1.25 mango-unit per second and a speed efficiency of 1.66 times over human-worker. The analysis of productivity results showed a gain of 1.35-1.79 times over human-worker. The percentage of mean error indicates different classification performance of small size, medium size, and large size was 3.6%, 14.2%, 59.6% respectively. The performance of productivity becomes 11.9% in small size, 8.4% (drop) in medium size, and 15.9% (drop) in productivity in large size.