## 4 Discussion and Main Conclusions
Nevertheless, further investigation on more elaborate architecturesutilizing the benefits of multiple methods, such as CNN-LSTM [77] or convolutional LSTM [78] networks might be worth conducting. However, being a characteristic of data-driven approaches, the volume and variability of data play a significant role in a model's performance. That being stated, this study has shown that by obtaining data from 20 subjects, equipped with five IMU sensors each, performing a few recordings and fine-tuning a state-of-the art LSTM network can help train a robust model, which can properly classify all activities with an accuracy of larger than 76%. Toward increasing the volume and variability of data (and, thus, the overall accuracy), a study with a sample that consisted of more participants covering a wider range of ages, physical strength, and anthropometric characteristics exists in the immediate plans of the authors. Moreover, with the intention of providing real-world data, these experimental tests are planning to be performed in a real agricultural environment by workers at their own pace, according to the complex conditions that they may face. Additionally, it can be concluded that the gyroscope and the accelerometer can be both used independently for recognizing the specific sub-activities, which are commonly performed in agricultural environments. However, their synergetic contribution can somewhat increase the overall performance. In contrast, the use of a magnetometer alone cannot lead to equally reliable results and should only be considered for supplementary use. The best performance was presented when the data from all sensors were fused. Furthermore, for the sub-activity of walking without the crate, the present methodology indicated the higher precision. On the contrary, as anticipated, the sub-activity presenting the smallest precision was that of bending down in order to approach the crate, since it can be executed in several ways, depending on each participant. For example, it was observed that most of the time, participants could solely bend their trunks (stooping) or kneel without bending their trunk enough, or simultaneously stoop and kneel to catch the crate. This resulted from the instruction of participants to carry out the task in their own way. This is justified from our intention to increase the variability of the dataset for capturing, as widely as possible, most of the different manners in which someone can perform the desired task. Obviously, assuring a fluid and safe HRI in agriculture involves a plethora of different issues. However, each issue must be addressed separately, at a preliminary stage, before a viable solution is proposed. This study demonstrates the framework for both conducting direct field measurements and applying a ML approach to accurately identify the activities of workers automatically, by analytically presenting the applied methodology at each phase. Finally, the examined dataset is made publicly available, thus assuring research transparency while allowing for experimental reuse and lowering the barriers for meta-studies. Conceptualization, L.B., A.A., D.T., N.T., D.B. ; methodology, L.B., A.A., A.T.; software, A.A.; validation, A.A.; writing--original draft preparation, L.B., A.A.; writing--review and editing, L.B., A.A., D.T., A.T., N.T., D.B. ; visualization, L.B., A.A.; supervision, D.T., D.B. ; project administration, D.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethical Committee under the identification code 1660 on 3 June 2020. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The dataset used in this work is public available in [41].