## 6 Experiments and Results
### Standalone AtomGID Experiments
#### 6.1.2 Comparison with the Literature on Passive RFID Gesture  
To evaluate the performance of AtomGID, it seemed important to review the literature on gesture recognition, despite the fundamental difference in the purpose of our approach. Most gesture recognition research concern analysis of video sequence or accelerometers data [46]. Indeed, in another context, gesture recognition is often considered to be a solved problem [47]. Despite this, very few researchers (other than our lab) have used gesture recognition for RFID. With noisy technology like RFID and in the context of continuous gesture recognition, the problem is actually very difficult. Classical gesture recognition usually does not operate with continuous data streams and therefore ignores the segmentation step (or assumes that it is obtained by the device).  
[PERSON] et al. [48] are among the pioneers in investigating gesture recognition using RFID. They employed a partitioning localization technique in conjunction with reference tags, utilizing three antennas positioned on a desk to monitor an 80 cm \(\times\) 80 cm area subdivided into 64 equally sized square cells (10 cm \(\times\) 10 cm). Their experiments focused on four basic directions, with an average gesture duration of 4.5 s at a velocity of 20 cm/s. While their localization algorithm demonstrated higher accuracy compared to our method, with an error of approximately 10 cm, it was rendered impractical for our context due to the absence of segmentation and reference tags. It is worth noting that although [PERSON] et al., achieved a recognition rate of 93%, they overlooked the segmentation step, conducted experiments with gestures twice the length of ours, and tested on a table without any interference.  
Another notable approach is that of [PERSON] et al. [49], who leveraged RFID systems with phase difference information to recognize hand gestures performed in front of a tag. While their system performed admirably, it is not applicable to our scenario where objects move freely in the environment. Similarly, [PERSON] et al. [50] utilized a comparable approach to recognize gestures on a grid comprising multiple passive RFID tags. Their RFID system achieved impressive accuracy in recognizing various motions and English letters. However, their segmentation method relied on the assumption that gestures were paused between strokes, which does not align with the continuous nature of our context. Despite their  
Figure 9: Confusion matrix of generated gestures.  
achievements, distinguishing confused letters relied on comparing the phase values of feature points, presenting challenges in our scenario.  
In their recent work, [PERSON] et al. [51] proposed a low-cost, non-invasive, and scalable gesture recognition technology and successfully implemented the RF-alphabet, a gesture recognition system for complex, fine-grained, domain-independent 26 English letters. The RF-alphabet had an average accuracy of 89.7% in different domains for new users and new environments, respectively, by performing feature analysis on similar signals. However, the experimental process used in this work is not transparent, and it is difficult to fully assess. For instance, the work does not mention the number of participants nor the way the RFID tags are used on the hand of the user.  
Finally, in previous work [52] from our lab, we proposed a simple algorithmic approach for hand gesture recognition designed to be used as the core component of a fine-grained activity recognition model. It was based on a simple wristband with accelerometers and a gyroscope. A set of 13 atomic gestures for cooking activities was defined, enabling the characterization of high-level cooking tasks as a set of simple gestures. The experiment was performed with 21 users, giving an average recognition rate of 83% for each gesture, which was inferior to the actual recognition performance of the approach proposed in this paper.