## 1 Introduction  
Agriculture employs a significant number of workers all over the world, particularly in the developing countries. The adoption of modern technologies, including Information and Communication Technologies (ICT), such as Internet of Things (IoT), Artificial Intelligence (AI), Farm Management Information Systems (FMIS), wearable computers and robotics, has led to the so-called \"agriculture 4.0\" [1, 2]. However, despite the plethora of technological advances, which have arguably ameliorated the farmers' living standards to a substantial degree, their safety is frequently underestimated. Epidemiological studies have identified several safety and health issues associated with the agricultural occupations [3, 4]. Focusing on the non-fatal health issues, work-related musculoskeletal disorders (MSDs) have proved to be the most ordinary ones [5, 6]. The most common manual operations in agriculture include harvesting, weeding, digging, pruning, sorting, and load lifting and carrying, with the last task having gained relatively little attention. This does not conform to the gravity of the matter, as has been reported recently in [7]. According to the literature, during load carriage, the energy expenditure increases as the load and carriage distance increase, while the possibility of getting injured increases when the worker carries the load incorrectly [8]. Repetitive load carriage, usually combined with lifting, has strongly been related to knee injuries and cumulative stresses on the lumbar region that can result in lower back pain, which have reached epidemic proportions among farmers [9]. Overall, the physical symptoms associated with load carriage can include aches, soreness, and feeling of fatigue that, in turn, can reduce noticeably farmer's performance. Another important aspect is that pickers, in tree and other perennial high value crops, spend considerable amount of time in carrying the filled crates outside the field, which can be a long distance. Alternatively, this task is shouldered by other farm workers, consequently increasing the costs for the farm owners. Remarkably, approximately one-third of the total working time can be spent in carrying crates, as stressed in [8]. Toward meeting the challenges of both preventing farmers from musculoskeletal injuries and assuring safety, the adoption of a fleet of low-cost lightweight robots with high-load carrying capacity for transporting the crates from the site of picking to the end of the farmyard can take place. Nevertheless, this is a multidisciplinary problem, as it entails issues coming from several scientific topics such as Human-Robot Interaction (HRI), IoTs, economics, Machine Learning (ML), clinical, safety, and ergonomics. As a consequence, each feature should be investigated separately, at a first stage, before integrated efforts are made for accomplishing a safe and economically viable solution. The present study focuses on a very important aspect of safe HRI, namely the activity recognition of the workers that can increase the situation awareness. This concept constitutes an emerging scientific field in HRI research to eliminate errors that usually appear in complex tasks. Most of the time, humans are situation aware and thus can reliably display competent performance. This skill is highly required also for autonomous vehicles, and ongoing research is carried out toward this direction [10; 11]. If a task necessitates cooperation, the participants (human and robot in this case) should synchronize their actions. This represents a key challenge that should be addressed in HRI, as highlighted by [PERSON] et al. [12], who examined the safety and ergonomics during human-robot synergy in agriculture. Human Activity Recognition (HAR) has received extensive attention as a result of the progress of advanced technologies such as ML [13] and IoT (e.g., Inertial Measurement Units (IMUs) and high accuracy Global Positioning Systems (GPS)) [14].