## 4 Discussion and Main Conclusions  
HAR is of major importance in the design process of agricultural collaborative robotic systems, as they should be able to operate in dynamic and crowded farm environments, where almost nothing is structured. In addition, these collaborative systems do not use isolated cells as occurs with conventional industrial robots. Toward optimizing the required activities, robots are working in the same working region concurrently with their \"co-workers\", namely humans. \"Cobots\", as these robots are usually referred to [69], can carry out either the same task or distinct tasks. The present envisioned application focuses on the latter scenario, where the robot can follow the workers while harvesting and, subsequently, place the crate onto the robot. Afterwards, the robot can safely transfer the full crates outside the field. Aside from the aim to provide safety and time saving, this cooperation can contribute to the prevention of the fatigue of agricultural workers, because the arduous task of carrying the crates for a long distance is performed by robots based on human-aware planning. Apart from the HRI, the results of this study are also applicable to conventional in-field operations such as lifting crates and loading to platforms for transferring to storage. The activity recognition of workers is closely related to an essential feature of HRI, which is usually mentioned as \"social-aware robot navigation\" [70,71]. While autonomous navigation is restricted to obstacles' avoidance and reaching the target destination [72, 73, 74], the social navigation, apart from that, takes into consideration other factors associated with human naturalness, comfort, and sociability [75,76]. More specifically, naturalness is related to navigation in paths such as those for humans via adjusting the robot's speed and its distance from farmers. Comfort offers also the feeling of safety, whereas sociability has to do with abstracting decisions pertaining to robot's movements by considering ethical and regional notions [71]. In a nutshell, HAR within agricultural human-robot ecosystems has a great potential to assure a sociable acceptable safe motion of robots and provide a free space to farmers to perform their activities unaffected by the simultaneous existence of robots, while the latter can approach them when it is required [12,77,78]. The present study focuses solely on HAR. To this end, data originated from 20 healthy participants, carrying out a particular task, were gathered by five wearable IMUs. This task included walking an unobstructed distance, lifting a crate (either empty or with a total mass of 20% of each participant's body mass), and carrying it to the point of departure, where they have to place it onto an immovable UGV (either a Husky or a Thorvald). By carefully distinguishing the sub-activities comprising the above task, the obtained signals were properly preprocessed for the purpose of using them in the learning phase (training of the model) and the testing phase (evaluating the model's performance and robustness) of an ML process (LSTM). Overall, the problem of properly classifying stationary activities was challenging. The \"Bending\", \"Lifting\", and \"Placing\" activities were initially misclassified by a large margin. However, noise removal and normalization increased the overall performance of the trained model significantly. One of the factors that improved the performance of the model was the width of the temporal window which, when it varied more than one second, the performance of the overall model would decrease substantially. The LSTM architecture has provided the appropriate tools for the model to be able to learn the features of the activity signals. Early experimentation with artificial neural networks (ANN) and one-dimensional convolutional neural networks (CNN) has resulted in low performance on the trained model.