## 1 Introduction
In particular, HAR relied on sensors has been applied to numerous areas including rehabilitation [15], sports [16], healthcare [17], and security monitoring [18]. HAR based on sensor data is considered to be more reliable compared to vision-based techniques, since the latter are affected by visual disturbances, such as lighting variability, and need fixed site implementations [19]. In contrast, wearable sensor-based techniques are suitable for real-time implementation, as they are easy to be deployed, cost effective, and are not site dependent [20]. Among the most commonly utilized sensors, accelerometers, magnetometers, and gyroscopes are often used [21; 22; 23]. Smartphones, having multi-sensor systems, are also gaining attention [24; 25]. In general, multi-sensor data fusion has been proved to be more reliable than using a single sensor, as possible information losses or the imprecision of one sensor can be compensated by the other sensors [24]. Remarkably, a promising technology is Digital Twin (DT), where multi-physics modeling can be integrated together with data analytics. As a result of its ability to combine data processing tools (from the digital world) as well as data acquisition (from the physical world), DT have been proposed as a means of better identifying scenarios of high-risk to optimize risk assessment and, thus, workers' safety [26; 27]. Processes, such as data preprocessing, describing data collection, data fusion, outlier removal, and noise reduction, precede all other processes that are involved in data-driven methodologies. These steps are required to bring the data into an optimal state, since they are derived from sensors that potentially can produce irregularities. Depending on the nature of the problem and the ML algorithm selected to tackle it, the data need to be further processed and transformed into the appropriate shape for a series of mathematical or logical operations. In general, the purpose of a ML algorithm is to be able to produce a model that will fit the data in the best possible way so as to predict unknown examples with the highest accuracy. In the case of HAR, the aim of the ML algorithm is to learn the characteristic features of the signals collected from the on-body sensors in order to be able to classify the correct activity for a particular timeframe. Afterwards, important feature vectors are extracted to minimize the classification errors and computation time [28]. Finally, the classification phase serves to map the selected features into a set of activities by exploiting ML techniques. Through implementing ML algorithms, models can be developed via iterative learning from the extracted features, up until they are able to optimally model a process. ML has extensively been implemented in agriculture, thus offering valuable solutions to several tasks such as crop, livestock, water, and soil management [29], to mention but a few. As far as the HAR is concerned, a plethora of ML models have been utilized, such as Hidden Markov Model [30], Support Vector Machine [31], K-Nearest Neighbor [32], Naive Bayes [33], Decision Tree [34], and Long Short-Term Memory (LSTM) [35]. Nonetheless, the literature regarding the use of ML for automated recognition, by using the data from wearable sensors collected throughout agricultural operations, is very limited. Indicative studies are those of [PERSON] et al. [36] (use of accelerometers to detect digging, harvesting, and sowing) and [PERSON] et al. [37; 38; 39] (use of GPS, accelerometers, and microphone sensors to detect harvesting, weeding, bed making, and transplantation. The aim of the present study was to properly identify human activities related to a particular task, which is lifting a crate and placing it onto a robot suitable for agricultural operations with the use of ML algorithms (LSTM) for sequential data classification.