## 2 Materials and Methods
### Data Acquisition  
The experimental sessions were conducted on a farm located in the region of Volos, central Greece. They involved twenty participants, evenly split between males and females. The average age of the participants was 30.13 years (with a standard deviation (SD) of approximately 4.13 years), their average height was 1.71 m (SD \(\approx\) 0.10 m), and their average weight was 70.20 kg (SD \(\approx\) 16.10 kg). To participate in this study, all subjects were required to have no history of surgeries or musculoskeletal injuries within the last year that could potentially affect their performance. Each participant provided informed consent, which was approved by the Institutional Ethical Committee, prior to the commencement of any experimental procedures. Each participant was required to: (a) Remain still until the start signal was given; (b) Walk straight 3.5 m without a crate; (c) Bend down to pick up the crate; (d) Lift it from the ground to an upright stance; (e) Walk back 3.5 m while carrying the crate; and  
Figure 1: Overview of the proposed machine learning-based human activity recognition framework workflow using wearable sensor data. (f) Position the crate onto a UGV. For the experimental setup, a UGV, known as Thorvald (SAGA Robotics SA, Oslo, Norway) was utilized, having a crate deposit height equal to 80 cm. This robot is commonly used in agricultural environments, due to its versatility and ability to navigate various types of terrain, making it a reliable choice for research and practical agricultural applications [4; 28]. The participants were required to handle either an empty crate (with a tare weight of 1.5 kg) or a crate loaded with weight plates to achieve a total mass equivalent to 20% of the participant's body weight [24; 29]. The weight plates, available in 1 kg and 2.5 kg increments, allowed for easy adjustment to the required weight. The open plastic crates used in this study had handles positioned 28 cm above the base and dimensions of 31 cm (height) \(\times\) 53 cm (width) \(\times\) 35 cm (depth). Each participant performed both sub-cases (empty crate and loaded crate) three times in a randomized order, moving at their own pace. To minimize the risk of injury, all participants were instructed to perform a five-minute warm-up before beginning the task. The inclusion of a diverse group of participants, varying in gender, age, weight, and height, was intentional to ensure that the collected data captured a wide range of variability. This variability is essential for training ML models that can accurately identify activities under different conditions. As far as the sensors used to acquire the essential data are concerned, five IMUs (Blue Trident, Vicon, Nexus, Oxford, UK) were implemented, which have been extensively used in relative literature [16; 23; 30]. Considering the number of available IMUs, we prioritized placing sensors on the upper body and core for effectively recognizing the specific activities performed in this study. These sensors were attached to specific body locations: the chest (over the breastbone), cervix (near the T1 vertebra), lumbar region (near the L4 vertebra), and both wrists. The sensors on the wrists were secured using special velcro straps, while the other sensors were attached using double-sided tape. Each IMU was equipped with a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer, providing detailed motion data across multiple axes. During the experiments, a sampling frequency of 50 Hz was utilized, which is considered sufficient for capturing the dynamics of the tasks being performed [23; 24]. In brief, the chest provides a central view of overall body dynamics and posture, while the cervix and the lumbar region offer insights into upper and lower body motion and alignment, fundamental for activities like bending and lifting, where the spinal region plays a significant role in maintaining posture and balance [31; 32].