## 1 Introduction
### Motivation and Contributions  
Prior research has established that environmental changes and device topology can affect WiFi sensing technology [13]. However, there is a need to explore the limits on the robustness of this technology in accurately detecting small-scale movements such as keystrokes. While prior work has achieved keystroke recognition under ideal setups [11], this study takes a different approach by focusing on the fundamental robustness of WiFi CSI sensing under controlled but increasingly challenging conditions. We emphasise that our findings are context-specific and should not be interpreted as demonstrating generalisable performance. Keystroke recognition using wireless sensing offers promising applications in human-computer interaction, authentication, and health monitoring. However, it also raises serious privacy concerns. The ability to infer typed input passively without user consent or awareness poses potential threats to user privacy, especially in environments like homes or workplaces. Motivated by this dual-use potential, this work not only explores the technical capabilities of keystroke sensing using commodity WiFi and backscatter systems but also assesses scenarios where privacy risks are mitigated by signal degradation, such as during natural two-hand typing. By doing so, we aim to contribute to both technical advancement and responsible deployment of wireless sensing technologies. Keystroke recognition via wireless sensing enables applications in interaction, authentication, and health monitoring, but also raises privacy concerns. While accuracy drops during natural two-handed typing, offering some privacy protection, high recognition accuracy persists in single-finger scenarios like ATM or PIN entry. This underscores the need for privacy safeguards tailored to specific use cases and threat models. We aim to evaluate the reliability and durability of machine learning-enabled WiFi sensing technology under different real-world circumstances and obstacles. This assessment will help us understand the potential privacy threats of WiFi sensing and determine the essential machine learning techniques required to meet its real-time and sustainability requirements. Moreover, our study aims to investigate the accuracy of WiFi sensing for keystroke detection applications and assess the need for mandatory sensing standards in privacy-conscious designs. Through our empirical investigation, we would like to advance knowledge and deepen our understanding of whether WiFi sensing-based keystrokes and keyed data detection can pose any privacy threats in different practical scenarios. All performance evaluations are based on real-world experimental measurements conducted on our deployed system. Overall, we make the following five contributions. 1. We demonstrate that commercial off-the-shelf WiFi devices can be effectively used for small-scale gesture and keystroke recognition through a detailed, fully experimental evaluation. This promotes the practicality, reproducibility, and accessibility of WiFi sensing for broader real-world applications. 2. We propose a baseline performance study using a two-jointed robotic arm to simulate ideal, highly repeatable typing scenarios. This provides an upper bound for recognition accuracy by minimising human-induced variability, serving as a reference for future robustness studies. 3. Building upon the robotic benchmark, we introduce a human hand to localise keystrokes, thereby introducing realistic motor variability. This step bridges the gap between idealised and real-world conditions, offering insights into the system robustness under natural hand movements. 4. We empirically investigate how key factors, such as inter-key distance, number of keys in the classification set, and Tx-Rx separation, affect the spatial resolution and performance of WiFi-based keystroke recognition systems. 5. We conduct a final set of experiments simulating varied, realistic typing scenarios including single-finger, multi-finger, and both-hand inputs.