## 1 Introduction  
Agricultural environments, particularly those including open fields, present several unpredictable and varied conditions that hinder effective management and operational efficiency [1; 2]. To tackle these challenges, human-robot collaboration has been suggested towards achieving common objectives through effective information sharing and task coordination [3; 4; 5]. During human-robot interaction (HRI), robots need to understand human intentions and respond appropriately. This can be achieved through human activity recognition (HAR), which involves the use of wearable sensors, computer vision, and machine learning (ML) to classify human activities [6; 7; 8]. Consequently, robots can synchronize their operations with human actions, allowing them to work alongside farmers and assisting with tasks such as weeding, harvesting, and transporting crops to storage [9; 10]. Studies on HAR are still scarce in agriculture, mainly due to the complex nature of the environments they involve. Furthermore, the lack of standardized datasets and benchmarks for agricultural HAR research has hindered progress in this field [11]. Motivated by the necessity of creating natural communication systems for facilitating HRI in agriculture,a vision-based static hand gesture recognition framework was developed in [12]. This framework was successfully tested in an open-field scenario involving an Unmanned Ground Vehicle (UGV) following the participants as they harvested crops and helping transfer crates from the field to a specific area. For the purpose of overcoming the challenges of static gestures, including their limited ability to capture the full range of human intentions in dynamic situations, [PERSON] et al. [9] developed a dynamic movement recognition HRI system. This advancement provided more detailed insights by integrating a wider range of body movements and variations, allowing for more accurate and natural interactions between humans and robots in a similar field scenario with [12]. In both studies, however, as they relied on data coming from an RGB-D camera mounted on the UGV, recognizing human movements accurately in different environments, like with different lighting or backgrounds, proved to be a considerable challenge. To address the challenges associated with vision-based HAR, wearable sensors can provide a trustworthy alternative option [13; 14; 15]. These sensors offer additional layers of information, enabling more robust activity recognition, even in conditions with poor lighting or occlusions. Nevertheless, challenges still remain regarding mainly the fusion and synchronization of multi-sensor data, possible sensor drift, and the increased computational complexity [6; 16]. Hand gesture recognition, through data originated from sensors embedded within a specially designed glove, was accomplished and successfully tested in [17] towards controlling wirelessly a robotic arm for removing weeds. In addition, [PERSON] et al. [18] presented a wearable shirt with a smartphone attached to it, allowing the measurement of acceleration signals during several material handling activities and tested the performance of different ML classifiers. In a similar vein, [PERSON] et al. [19; 20; 21] used accelerometer data along with data from a microphone and global positioning system (GPS) from smartphones and evaluated the performance of several ML algorithms. Towards classifying specific agricultural worker's activities, [PERSON] et al. [22] used two accelerometers fixed to the wrists of operators of vibrating agricultural tools and a k-nearest neighbors (KNN) classifier. In [23], data collection field experiments were conducted to acquire data using wearable sensors throughout a human-robot collaborative harvesting task using two different UGVs for ergonomic purposes. In [24], the obtained signals from wearable sensors were fed into a long short-term memory (LSTM) network for HAR.