This study created and simulated a musculoskeletal model that mimicked the harvesting motion. This model was adopted from a recent model by Erica et al. , which was the first validated lifting model in OpenSim. The lifting motion requires the model to frequently flex the trunk and lift the object. Hence, the back muscles of the Erica et al. model were ensured to be strong enough to perform the lifting motion. This advantage made it preferred for the harvesting model because many qualitative studies reported that the FFB harvesters complained of low back pain [10, 11, 14], indicating that strong back muscles were required during harvesting. Moreover, many current OpenSim models assume the trunk as one rigid body, such as the full-body models by Rajagopal et al.  and Hamner et al. , which is not true in reality and may not fully represent the dynamic behavior of the trunk. The trunk in the Erica et al. model was made of several rigid body segments, including each lumbar vertebrae (L1 – L5), pelvis and torso . This trunk structure is considered more accurate because the trunk of a real human is a chain of interconnected vertebrae, pelvis and scapula . On the other hand, the biceps and triceps of the simulated harvester were added from another latest validated model by Daniel et al. , which performed pushing and pulling tasks at different elevation angles. The arm muscles were added from this model because the harvesting motion involves pulling motion, similar to the pulling task.
This study demonstrated a novel approach to investigate the worker's behavior in harvesting oil palm FFB. Wearable IMU allows the motion to be captured in the oil palm plantation without sophisticated setup and calibration. It is also proven to be cheaper, lighter and does not suffer from marker occlusion, making it an ideal alternative for outdoor applications than MoCap . Two different representations and platforms were used to determine the upper extremity joint angles during harvesting. The IMU_ME method uses the Euler angle representation to calculate the joint angles , whereas the IMU_OpenSim method uses the quaternion representation to determine the joint angles . Despite their differences, the results showed that the outcomes of the IMU_OpenSim method highly resemble the one derived using the IMU_ME method. The RJA and RMSEJA were within the good to excellent range proposed in . This suggests that OpenSim can be a valid tool to determine the kinematic behavior of the FFB harvester's upper extremity.
Although not directly comparable, the RJA and RMSEJA reported in this study were very close to those reported in other studies [42, 43], which calculate and validate their joint motions with different sensors. In a study for the upper extremity motions, Rodriogo Perez et al. investigated the joint motions when the subjects performed simple motions involving shoulder joint, elbow joint and wrist joint. The joint motions were measured with IMU sensors, validated with MoCap. The shoulder AA (R = 0.718) showed the lowest correlation when compared to shoulder FE (R = 0.994) and shoulder rotation (R = 0.995) . This finding is consistent with the current study, suggesting that it is difficult to obtain a similar result for shoulder AA, even for simple motions. The authors then investigated the motions when the subjects served water from a jar. In this case, the shoulder rotation (R = 0.853) showed the lowest correlation when compared to shoulder FE (R = 0.996) and shoulder AA (R = 0.908) . Jim Richards also pointed out that many papers have focused on sagittal plane joint angles rather than the coronal and transverse planes . These previous findings support our findings, which show that shoulder FE has the highest correlation compared to other shoulder motions.
In another study, Brice Bouvier et al. investigated the joint motions when the subjects performed simple elbow FE motions. The joint motions were measured with IMU sensors, validated with MoCap and has an RMSE value of 24° . A recent systematic review has shown that the validity of joint motions decreases when the level of complexity of the motions increases . This implies that if the RJA and RMSEJA in this study are similar or better than those studies with simple motions, it can be assumed that the findings are acceptable. Since the RMSEJA of elbow FE reported in this study (Dominant: RMSEJA =11.8733°; Non-dominant: RMSEJA =20.2215°) were less than the RMSE obtained in the simple motion study (RMSE = 24.0000°), therefore the results are acceptable.
This study has also provided a novel approach to investigate the muscle activation of the oil palm FFB harvesting motion using OpenSim. The simulated normalized muscle activations were validated with normalized sEMG, which is the conventional standard used in human motion analysis [26, 44]. Other validation methods such as comparison with similar studies are impossible because very limited studies investigated the muscle behavior of FFB harvesters with EMG . From Table 6, the normalized activations of the back muscles (longissimus and multifidus) showed a high correlation in RMA and a good correlation in MAEMA when FRA were 30N and 50N. These findings concur with previous studies in lifting tasks [16, 45] and daily living activities , which obtained high similarity results for the back muscles between the OpenSim muscle activations and EMG.
For the arm muscles, the triceps demonstrated a high correlation in RMA and a good correlation in MAEMA. The biceps demonstrated a high correlation in RMA and a poor correlation in MAEMA when FRA was 30N. Three possible reasons may explain the poor correlation of biceps. Firstly, higher speed can result in higher deviations than lower speed . The harvesters must use a large force at a very high speed to harvest the fruit . Since the biceps is the primary muscle to perform this motion [9, 48], it is reasonable that the biceps showed higher MAEMA values when compared to other muscles. Secondly, Roberto Bortoletto et al. proved that different FRA could affect the simulated muscle activation in OpenSim . From Table 6, it can be observed that the multifidus and longissimus showed the best correlation of RMA and MAEMA when the FRA were 30N and 50N, respectively. The biceps showed the highest RMA when FRA was 30N, whereas it showed the lowest MAEMA when FRA was 50N. The triceps showed the highest RMA when FRA was 50N, whereas it showed the lowest MAEMA when FRA was 30N. These findings are valuable and important because currently there aren’t any recommended value of FRA for harvesting activity in OpenSim. Thirdly, the muscle architectural parameters of the simulated harvester may not represent the muscle architectural parameters of the real harvesters.
The potential risk of MSD that harvesters face during oil palm FFB harvesting were discussed based on the joint angles and muscle activations obtained with OpenSim. It was found that the back flexion, back rotation, shoulder flexion and elbow flexion were stressful joint motions during harvesting. During harvesting, the back muscles (longissimus and multifidus) and biceps were actively used. These findings are consistent with many qualitative studies on harvesters. Through questionnaires and direct interviews, many harvesters reported discomforts on their lower back [10, 11, 14, 50]. Many studies that use the direct observation assessment method also found out that the harvesters were facing a prevalence of MSD on their backs [14, 48, 50], shoulders (upper arms) [47, 50] and elbows (lower arms) [14, 47, 48, 50].
In a study by M. Faiz Syuaib , he analyzed the harvesting motions based on a video. He found out that the flexions of the back, shoulder and elbow were always stretched to the NROM of the joints. This result agrees with the findings reported in this study. However, the author did not analyze the rotation of the back joint. This may be due to the limitation of his approach because it is very difficult to measure the joint angle of rotation based on a video. In a recent study by YX Teo et al. , the authors used the sEMG and IMU to investigate the joint motions and muscles used during harvesting activity. They found that the longissimus, multifidus and biceps were the main muscles, consistent with this study's findings. They also reported that the back extension, back LB, shoulder flexion, elbow supination and elbow flexion were at risk of MSD. Other than the flexions of the shoulder and elbow, the other joint motions were not consistent with the findings in this study. This result may be due to the different focus of the studies. The previous study analyzed longer harvesting motions, which lasted for a few seconds. On the other hand, this study only analyzed the harvesting motion at that instant the fruit was harvested, which lasted approximately 0.3 seconds. It is motivated by the indication that this particular harvesting motion could be the main contribution of the MSD for the harvesters .
It was observed that many qualitative studies reported the harvesters suffered low back pain. A few possible reasons can explain this issue. First, it was found that after a long duration of repeated back flexion, the stiffness of the spine decreased. The deformation of the intervertebral disc and the stretching of the ligaments in the spine generated a change in the loading pattern, leading to low back pain . Second, during the flexions of the back and shoulder, the moment arms of the body segments increase, causing the joint moment and compensatory tension in the back muscles to increase. It is due to the extremely small moment arms of the back muscles; hence a large force is required to counteract the joint moment . Lastly, it was known that the passive moment (i.e., moment due to passive structures such as ligaments, fascia and cartilage) increases exponentially when a joint is approaching the ROM of flexion . A large increase of passive moment might also lead to low back pain in the harvesters. All these evidence supported our findings, which proved that the flexions of the back joint, shoulder joints and elbow joints were the stressful joint motions during harvesting.
There are some limitations to this study. The muscle architectural parameters of the musculoskeletal model, such as the force-length relationship of muscle and tendon, may not represent the muscle architectural parameters of the real harvesters [26, 35, 46]. Moreover, the assumption that the harvesting force was 303.5N may not represent the actual harvesting force for each harvester. The harvesting force is also influenced by factors such as the frond maturity, cutting angle, speed of cutting and the sharpness of the cutting edge of the harvesting tools . It is suggested that a mock harvesting environment should be prepared indoors. A more controlled indoor environment will allow the other measurements such as MoCap and force sensor to improve the accuracy of the results. It has to bear in mind that although all these limitations of modelling software limit the extent of interpretation, they do not invalidate the model results . The discrepancies between OpenSim and sEMG might be caused by the limitation of sEMG. There might be crosstalk and movement artefacts from neighbouring muscles [53, 54], especially during fast motions . The small recording region of the sEMG electrodes on a particular muscle may also not represent the whole muscle's activation . The fine-wire EMG would be a more direct and accurate approach to validate OpenSim results. However, it is invasive and is not practical for field study .