## 7 Discussion
### Limitations and Future Work  
A key limitation of this study lies in the use of head pose telemetry as a proxy for attentional engagement. Although non-intrusive and practical, this method is constrained by the resolution of the RGB sensor and the accuracy of the head pose estimation, with mean absolute error values of 5.4\({}^{\circ}\)\(\pm\) 4.4\({}^{\circ}\) for yaw, 5.5\({}^{\circ}\)\(\pm\) 5.3\({}^{\circ}\) for pitch, and 4.6\({}^{\circ}\)\(\pm\) 5.6\({}^{\circ}\) for roll [[https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/head-pose-estimation-adas-0001/README.md](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/head-pose-estimation-adas-0001/README.md)]([https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/head-pose-estimation-adas-0001/README.md](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/head-pose-estimation-adas-0001/README.md)) (accessed on 30 July 2025). The accuracy limitation was especially noticeable during interactions involving participants wearing glasses, which sometimes caused detection errors. These issues led to misclassifications of minor movements as distractions, introducing noise into the data. The qualitative feedback also indicated that some participants noticed the robot reacting to small or unintended movements, occasionally perceived as exaggerated or mistimed. This highlights the need to fine-tune the sensitivity thresholds and improve robustness. Future iterations of the RJAS and the UBT system could benefit from integrating complementary signals such as eye gaze, blink rate, or facial expressions to provide a more reliable and nuanced view of engagement. Another consideration regarding the current state of the UBT system is the sampling rate of the head pose detector, which operates at 6 FPS due to the hardware constraints of the robot's processing architecture. This frame rate may miss very brief head movements or micro-shifts in gaze. However, its impact on distraction metrics is likely minimal as the system is designed to consider only sustained head orientations lasting at least 1.5 s, thereby deliberately excluding brief movements such as eye saccades or momentary glances. Still, the occasional omission of short gaze shifts could slightly affect the total distraction time measure. Future work could explore optimized processing or external sensors to achieve finer-grained attention tracking. Beyond these technical considerations, there are also methodological limitations that should be acknowledged. Concerning the temporal scope of the study, measuring engagement in a single short interaction (such as a 15 min gameplay session) may not fully capture the dynamics of user involvement over time. Engagement is inherently multidimensional and can fluctuate depending on context, task complexity, and novelty effects. Thus, while our results reflect meaningful differences between experimental conditions, they should be interpreted with caution when generalizing to longer-term or repeated interactions. Future work should explore engagement over extended periods and across a wider variety of tasks beyond gamified settings in order to assess whether the observed benefits of the RJAS persist and translate to broader HRI scenarios. Another methodological limitation is that the specific impact of each expressive modality was not assessed independently. Future work could investigate how variations in prosody, such as pitch, intonation, or speech rate, affect the perceived responsiveness and effectiveness of the robot in recovering attention. Additionally, evaluating the system on alternative platforms with different embodiment features, including the presence or absence of facial LEDs, could help to clarify the role of aesthetic elements in shaping user engagement and distraction. In addition, while the sample included 40 participants with comparable group sizes, some variability remained in demographic factors such as age, gender, and prior experience with the robot (see Table 2).