## 10 Concluding Remarks  
In this paper, we investigated the robustness and readiness of WiFi sensing-based keystroke detection technology. We first generated a robotic arm control system and established a benchmark for localisation using WiFi CSI. The accuracy of that benchmark was found to be 99%. Next, we demonstrated that this ability for accurate localisation was maintained even when faced with the real-world challenges posed by a human hand. The accuracy in that scenario was found to be 94%. Following this, we showed an overall accuracy of 89% in predicting keystrokes among five keys. Based on this, we further examined the effect of varying the distance between each test key and the size of the test group. We found that, for both cases, the increase in distance or group size reduced the  
Figure 21: ML accuracy change with Tx-Rx distance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Tx-Rx Distance (m)** & **Accuracy (\%)** \\ \hline
1 & 88 \\ \hline
1.5 & 80 \\ \hline
2 & 72.5 \\ \hline
2.5 & 70 \\ \hline
3 & 68 \\ \hline
3.5 & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of keystroke detection tests. overall accuracy of the constructed ML framework. This accuracy for predicting keystrokes decreased by 5% when the system was evaluated with an unseen hand. Based on the success of keystroke detection, we experimented with name detection using single fingers from a single hand, multiple fingers from a single hand, and multiple fingers from both hands. The overall accuracy for these three scenarios was 93.875%, 86.665%, and 55.835%, and we found that when typing using two hands, we could still achieve WiFi sensing but could not detect accurately, which can protect users from privacy attacks. Further, we noticed a drop in performance as the sensing device separation increased. Thus, our iterative empirical analysis testing the robustness of WiFi sensing technology highlights the need for advanced signal processing techniques for extracting fine-grained keystroke features from CSI to maintain accuracy in more generic cases with larger separation between the IoT devices. The current study was conducted in a controlled environment with a limited number of participants and fixed device placements. While these constraints allowed for the isolation of key performance factors, future work will expand this evaluation to more realistic usage settings. This includes incorporating dynamic human activity, variable room geometries, and broader user diversity to assess the scalability and generalisation of CSI-based keystroke sensing in practical deployments. Nevertheless, future work can also explore system-level metrics such as real-time latency, scalability under concurrent access, and performance in multi-user environments to assess practical deployment feasibility. While our current experiments were conducted in a controlled environment to isolate core system behaviours, we acknowledge this does not fully capture the complexity of real-world deployments. This work was conducted under controlled indoor environments in line with prior literature to ensure repeatability and isolate core system behaviours. Scenarios involving non-line-of-sight placement, concurrent WiFi transmissions, and broader user diversity were beyond the current scope but are noted as important directions for future investigation. Evaluating the system under these more dynamic and diverse conditions is an important next step, and we plan to incorporate such scenarios into an extended test bed in future work to better assess the robustness of our approach. While our system demonstrates the feasibility of keystroke inference using WiFi CSI, the recognition performance degraded significantly under realistic two-hand typing conditions. Importantly, while our study evaluated practical challenges under varied but controlled conditions, our aim was to explore the operational boundaries of CSI-based keystroke detection rather than generalisability across users. Broader real-world robustness and scalability remain open research directions.