## 2 Related Work
[24] introduced a novel tag trajectory index method aimed at resolving the issue of localized tag trajectories between RFID readers. It also presents a tag identification model based on RFID readers equipped with multiple antennas, offering a more cost-effective and efficient deployment solution. Additionally, they proposed a multi-antenna optimization control scheme capable of automatically adjusting the number of operational antennas to meet real-world demands, thereby enhancing recognition efficiency. Furthermore, it introduces an indoor tag trajectory reasoning data structure called OPTR-tree, facilitating the fast recognition of multiple tag tracks in indoor environments with room-level accuracy. Both the theoretical analysis and experimental findings validated that the proposed multi-antenna model enhances the reliability of recognizing multiple tags within building premises. In regard to our team [31], we presented a method that uses the signal strength indication of RFID antennas with statistical features to perform relative positioning in a smart home. The goal of the proposed method was to enable the tracking of most objects inside a smart home in real-time, allowing for activity recognition based on this tracking. This work also introduced a new dataset of 4,100,000 RFID data collected in a real full-scale smart home setting. The dataset was made available for the community. The method showed an accuracy of 95.5%, which is similar to previous works but requires a fifth of the time to compute. At the end, it is clear that the ambient distributed sensor approaches are the most suited in the context of Ambient Assisted Living (AAL) and that these approaches could give good results. However, only several works in this context addressed specifically the find-grained gesture recognition, and very few tackled the issue of integrating spatial reasoning to enhance the assistive potential of the model [9; 11]. In summary, most of these existing approaches encounter a significant limitation by operating at a very low granularity, restricting their capability to differentiate between broad categories of activities (such as morning routines, washing, meal preparation, etc.). This limitation arises from the predominant focus of prior research on monitoring user routines and discerning patterns within them, rather than prioritizing real-time assistance. Notably, this trend is exemplified in the well-known works of [PERSON] and her team from Washington State University [35; 36; 37; 38], who have dedicated over a decade to exploring the ADL recognition problem from various perspectives. Their efforts have yielded diverse approaches for identifying high-level ADLs from low-level ambient sensor data, employing techniques such as Decision Tree (DT) classifiers, Naive Bayes Classifiers (NBCs), Random Forests (RFs), and Support Vector Machines (SVMs) [35]. Additionally, they have delved into multi-resident ADL recognition [38] and recently introduced a method for finely discerning activity start and end times [36]. While this advancement provides valuable temporal information, the issue of recognizing very specific low-level actions (steps) remains unaddressed, as does the consideration of spatial relationships between objects and users. In contrast, our approach distinguishes itself by prioritizing the recognition of low-level actions (steps) within the context of Activities of Daily Living, leveraging spatial relationships to enhance understanding.