# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
## 1 Introduction  
As a means of overcoming the challenges arising from the dynamic, unstructured, and unpredictable nature of open-field agricultural environments [1, 2], humans and robots should work collaboratively [3, 4, 5]. Human-robot interaction (HRI) brings together the unique cognitive abilities of humans, such as perception and critical judgment, and the strengths of robots, including precision, consistency, and physical power [6, 7]. This fusion creates a synergistic partnership that offers numerous benefits, such as greater flexibility for system reconfiguration, the optimized use of workspace, boosted productivity, faster return on investment, and the development of advanced, skill-intensive job opportunities [8, 9, 10]. A key aspect of effective and seamless HRI is human activity recognition (HAR), which involves identifying human body movements, activities, and gestures through data collected from various sensors, such as cameras and wearable devices [11; 12]. By understanding human actions, robots can respond appropriately and align their actions with workers' activities, adapting dynamically without requiring explicit instructions [13; 14]. This adaptability is vital in unpredictable agricultural environments, where human activities can also vary significantly. Furthermore, HAR significantly enhances both perceived safety (humans feel safe when working alongside robots) and trust (confidence in the reliability and predictability of the robots) in collaborative environments [15; 16]. Recently, there has been a growing interest in HAR within the agricultural sector. With a focus on the related literature, to meet the demand for more intuitive communication systems that facilitate HRI, a vision-based approach for recognizing static hand gestures was introduced in [17]. To overcome limitations concerning capturing the full spectrum of human intentions during continuous tasks, dynamic movement recognition systems were introduced in [18; 19], integrating a wider variety of body movements. This advancement enabled detailed and accurate interactions between humans and robots in harvesting operations. Despite the encouraging results, the above studies faced challenges due to the use of RGB-D cameras, which struggled with environmental variations like changing lighting and background conditions. In response to these issues, wearable sensors have emerged as a promising alternative for HAR in agriculture. These sensors provide additional data layers that enhance HAR, even in suboptimal conditions such as those with low lighting or obstructions. However, challenges remain mainly in sensor data fusion, potential drift, synchronization, and the increased computational burden associated with processing multi-sensor data. For example, hand gesture recognition was accomplished using data from sensors embedded in a specialized glove to control a robotic arm for weed removal in [20]. In [21], a shirt with an attached smartphone was used to collect acceleration signals during various material handling tasks. Similarly, [PERSON] et al. [22; 23; 24] utilized accelerometer data, along with inputs from microphones and GPS on smartphones, to evaluate the performance of different ML algorithms. Accelerometers, placed on the wrists, were also used in [25] for classifying specific activities of agricultural operators using vibrating tools. Field experiments in [26; 27] involved collecting data with wearable sensors (accelerometers, gyroscopes, and magnetometers) during a collaborative human-robot harvesting scenario. In these investigations, the obtained data were processed using a Long Short-Term Memory (LSTM) network to enable HAR through the integration of multiple data modalities, while additionally, optimal sensor placement was investigated in [27]. The common denominator of the above studies is that HAR relies on ML classifiers to analyze data from various sources.