## 7 Conclusions  
In conclusion, this paper introduces AtomGID, a non-deep-learning gesture recognition algorithm specifically tailored for imprecise tracking systems. Developed through several years of research at our lab, AtomGID builds upon our prior work in passive RFID tracking and utilizes a flocking-based clustering method. Integrating AtomGID into our existing system demonstrates significant promise in accurately recognizing fine-grained activities within a smart home environment. While the field of activity recognition has made notable strides, addressing fine-grained activities effectively remains a challenge, often constrained by technological limitations or contextual factors. Nonetheless, advancing this area is imperative for enhancing smart home systems, as it holds the potential to enable more targeted support services and enhance the explainability of decision-making processes.  
Looking ahead, several avenues of development warrant exploration. Currently, our approach serves as a bridge in contexts where deep learning may not be suitable, albeit potentially falling short of state-of-the-art (SOTA) methods. This prompts the question of whether to continue refining non-deep-learning solutions or adapt deep learning frameworks for our specific context. For instance, by amassing a comprehensive dataset comprising passive RFID data and partially annotated gestures and activities from diverse home environments, deep learning algorithms could theoretically supplant all three components of our system: passive RFID tracking, AtomGID, and clustering.  
At present, it appears that there is still a niche for simpler, explainable non-deep-learning approaches. However, for future endeavors, our team plans to conduct a comparative study by replicating gesture recognition using an SOTA algorithm to assess its performance against AtomGID. This comparative analysis will provide invaluable insights into the strengths and limitations of each approach, guiding future research efforts towards the most effective solutions for activity recognition in smart home environments. Moreover, it would be interesting to explore techniques for enhancing the interpretability and explainability of gesture recognition algorithms, particularly in the context of decision-making within smart home systems. This could involve developing visualizations or interactive interfaces to help users understand how gestures are interpreted and utilized. Finally, with the availability of other sensors in most smart home environments, it is clear that an avenue of investigation consists of the integration of additional sensor modalities, such as ultrasound or photoplethysmography sensors, alongside passive RFID technology. The fusion of data from multiple sensors could improve the robustness and accuracy of gesture recognition systems, particularly in complex or dynamic environments.  
Conceptualization, methodology, software, validation, visualization, K.B.; writing--original draft preparation, writing--review and editing, resources, K.B. and B.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Sciences and Engineering Research Council of Canada, through the discovery grant program.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.