## 5 Implementation and Evaluation
### HGR Performance Evaluation
#### 5.4.1 Recognition Accuracy
Similar to MIFS, the accuracy of CIFE increases with \(k\) and shows higher recognition accuracy for the home dataset. Table 3 compares the overall recognition accuracy of the proposed methodology HOS-Re with corresponding values from the literature [29] adopting CNN with and without signal processing (SP). The number of users (\(N_{u}\)) that participated in the lab and home environment data acquisition with 276 gestures is 1, whereas for a lab environment with 150 gestures is 5. The number of samples per gesture per user (\(N_{su}\)) for which the CSI values were acquired is 20 and 10 for lab and home environments with 276 gestures (\(N_{g}\)). For a lab environment with 150 gestures, this value is 10. The product of \(N_{u}\times N_{su}\times N_{g}\) gives the total number of gesture samples (\(N_{gs}\)) acquired. From this \(N_{gs}\), the number of gesture samples per user is estimated to be 5520 and 2760 for lab and home environment respectively with 276 gestures (lab 276 and home 276). For the lab environment with 150 gestures (lab 150), this value is 1500. SignFi [29] reported 95.72% recognition accuracy for lab 276 and for home 276, it is 93.98% when no signal pre-processing is applied. This reduction in recognition accuracy could be attributed to the reduced samples per user with home 276. Deep learning methods adopted in [29] is expected to produce higher gesture recognition accuracy with an increase in sample size. However, the combined lab + home 276 datasets reported 92.21% recognition accuracy. This drop in accuracy is anticipated due to the influence of environmental factors in lab 276 dataset, as inferred from Figure 6a. SignFi [29] with signal pre-processing achieves recognition accuracies higher than the corresponding values without pre-processing. For lab 276 the accuracy is 98.01%, and for home 276 it is 98.91%. This marginal increase in home 276 can be due to lower interference in the home environment compared to the _lab_ environment. As observed earlier, the recognition accuracy of lab + home 276 declines to 94.81%. Lab 150 achieves least recognition accuracy of 86.66% since the number of samples per user acquired is lesser than other datasets, where the performance of deep learning method suffers. Deep learning methods [29] had large variations in recognition accuracy estimates influenced by the interferences and/or sample size. The present work HOS-Re achieved consistently higher recognition accuracies in all scenarios. In particular, when the samples acquired per user is lesser as in _lab_ 150, the recognition accuracy estimated by present work is 96.23%, and it is almost 10% higher than the corresponding value obtained from the deep learning methods with signal pre-processing. It is preferred to have a gesture recognition method achieving higher recognition accuracy with minimum samples, as it is challenging to acquire a large number of samples from human participants. Therefore, the presently adopted machine learning approach along with the identified feature extraction, selection, and classification methods perform well, irrespective of the sample size and environmental factors. Figure 12: Performance of CIFE with \(k\) on different datasets. For more insight on the robustness of recognition performance of the presently adopted HOS-Re methodology, the results from the present work are compared with the related work reported in the literature. Table 4 compares some of the related work on gesture recognition using COTS Wi-Fi devices, and it can be observed that most of the work reported so far used lesser samples. SignFi [29] is the only dataset having more number of gesture classes and samples instances. Amongst the cited work in Table 4, the works that used machine learning classifiers on CSI traces to recognize the gesture, have not handled such sizeable data and complex multi-class classification task. It can also be observed that the presently adopted HOS-Re methodology achieves the highest recognition accuracy.