Arduino-Based Electromyography System for Enhanced Monitoring and Optimisation of Oil Palm Harvesting Manual Workers

Abstract

Significant changes have been made in the harvesting operations implemented within the oil palm industry. Mechanisation has replaced conventional manual harvesting methods, which are physically intensive work, leading to increased effectiveness. However, despite the known association of manual labour with musculoskeletal problems among workers, it remains crucial in harvesting operations. Electromyography (EMG) has emerged as an essential tool for monitoring muscle activities. This paper aimed to develop an Arduino-based EMG system for muscle activity monitoring in oil palm harvesters as a cost-effective and user-friendly method. The results showed that there was a direct correlation between recorded values and lifted loads, whereby 8kg had the highest reading while 2kg had the lowest reading. It reflects some positive trends in mean values in the weight categories found in the dataset, specifically 0.4238 for 2kg, 0.5078 for 4kg, and 0.7937 for 8kg. Similarly, standard deviations showed an increasing pattern with an increase in weight, indicating a trend for more variation at higher weights. The prototype demonstrated promising results regarding capturing muscle activities under various scenarios, which indicate its potential to assist in designing and developing ergonomically improved machines. Further research needs to be conducted to test the accuracy.

Keywords

• Arduino
• Electromyography
• Harvesting
• Muscle activity
• Oil palm