## 1 Introduction  
The oil palm industry is significant in Malaysia as it has a great potential to boost the country's economy. Several reports highlighted oil palm as one of the major agricultural commodity sectors that contributed to 37% of the Malaysian gross domestic product in 2012 [1]. Apart from fulfilling the increasing global food and energy consumption demand [2], the oil palm industry also provides job opportunities [3]. Malaysia is one of the world's largest producers and exporters of oil palm [3], thus making oil palm an important crop in Malaysia. Given the significance of oil palm in the economic development and growth of Malaysia, it is vital to look into ways to facilitate and improve the harvesting process of oil palm. Several characteristics of oil palm trees such as the crown-canopy arrangement of the fronds, the configuration of the fresh fruit bunches (FFBs) stalk attachments to the tree trunk, the height of the trees, and the weight of the FFBs may require the harvesters to work in unfavourable awkward postures. They also need to handle heavy loads and perform repetitive work with high force exertion [4]. These factors, if poorly handled, may affect the productivity and yield of the oil palm industry, resulting in a negative impact on the economy. Another factor to consider is the gradual cutbacks in labour availability [5]. Although an increasing number of automation techniques have been proposed and used in manufacturing industries [6], the majority of the oil palm harvesting process is being performed manually with human strength and skill. In addition, there is a lack of ergonomic designs for many oil palm manual handling activities and tasks, thus predisposing the workers to inappropriate postures and physical fatigue. As a result, there is a high prevalence of musculoskeletal disorders (MSDs) among the oil palm FFB manual workers [1; 3; 7; 8]. Their subsequent absence from work may be burdensome to the industry [9]. In the past two decades, MSDs have been investigated and studied among different professions such as farmers [10; 11; 12; 13; 14], manufacturing industry workers [15], and computer users [16]. Recently, there is increasing research of MSDs among oil palm harvesting workers. However, most of the studies were observational research or questionnaire-based surveys. Common questionnaires included the modified Nordic questionnaire, Ovako working posture assessment system [1; 3; 4], rapid entire body assessment [3], rapid upper limb assessment (RULA) [17], and simple ergonomics risk assessment [18]. These paper-based observational techniques can provide useful information about the occurrence of MSD among occupational workers. However, to design ergonomic equipment and an ergonomic workplace for the workers, findings from the observational techniques need to be complemented with a biomechanical analysis such as human motion simulation. The Human motion simulation software and the digital human model have been utilised by many researchers in predicting human motion. However, the researchers need to understand the motor tasks involved in daily life or work in order to provide the essential data for the simulation of realistic human motion. The examples of human tasks include reaching overhead, crouching, lifting objects, bathing, shaking hands, or eating [19]. These human tasks have been studied in previous research. For example, a mathematical model was constructed for the control of the human arm in performing the reaching movement [20]. Another published review focused on the methods of modelling human motion and methods for studying and modelling human reaches [21]. Ref. [21] also highlighted the types of information of interest to designers to develop the digital human models, such as anthropometric data, and population demographic subgroups. These data enabled the designers to simulate realistic motion and postures with minimum task input descriptions. Additionally, it was also established that human motor tasks or performances can be affected by age [19; 22].