## 5 Conclusions  
In the presented study, we have compared two approaches to the classification of head gestures, i.e., training the classifiers on the parametrized signals, and training on direct signal samples recorded from the IMU. The obtained results of head gestures recognition allow to formulate the following conclusions:  
1. Head movements (_roll, yaw,_ and _pitch_) can be efficiently recognized on the basis of signal recordings from an IMU that is positioned on the user's forehead. 2. The data classifiers trained on IMU signal samples outperformed the classifiers that were trained on a set of statistical parameters derived from the IMU signals. These performance differences were confirmed by running statistical tests. The Friedman test revealed that the classifiers yield statistically different results (\(p<0.05\)). Also, the paired Wilcoxon signed-rank test has confirmed statistically valid differences for most of the classifiers (no significant differences between the random forest and the \(k\)-NN classifier for \(k=19\) was noted only). Our explanation about high head gesture recognition performance from raw IMU signals is that the six signal channels carry rich enough information for the classifiers to confidently recognize the head gestures. Also, we conclude that the inherent measurement noise (that occurs randomly) is averaged out during the training procedures of the classifiers. 3. The SVM classifier outperformed other classifiers in recognizing the head gestures, and if trained on the IMU signal samples, it achieved a recognition accuracy above 95%. 4. The recognition rates of the head gestures for the person-independent test dataset, i.e., the data taken from 12 persons whose recordings were not used in training the classifiers are comparable to the recognition rates obtained for the data recorded from the individuals who have also recorded the training data (see Figures 5 and 6). Interestingly, if the classifiers were trained on raw IMU signal samples, their recognition performance was generally better for data recorded from participants who did not train the classifiers. 5. The proposed method is suitable for on-line implementations. In Table 8, we show that the computation times required for detection of a gesture (on a PC with Intel Core i5 processor) by pre-trained classifiers do not exceed 11 ms.  
We hypothesize that the presented head-gesture-controlled interface can find numerous applications. Firstly, it can offer an alternative communication channel for people with serious physical disabilities, e.g., individuals suffering from serious spinal injuries or stroke that result in tetraplegia. We made a new public benchmark dataset consisting of the training data (from 53 participants) and testing data (from 65 participants). The signal database with description is available for download at [29]. In further studies, on the basis of high head-gesture recognition performance, one could even further improve the recognition accuracy by accumulating classifier recognitions with each arriving IMU signal sample (i.e., with every 100 ms time interval) and achieve close to 100% recognition rates for the entire gesture. Our motivation is to further test the interface in a mobile travel aid system [38] and as a supporting interface in activities of daily living for the visually impaired, e.g., as a remote control system for home appliances. We will also consider the usefulness of such an interface as a potential rehabilitation aid for individuals with neck stiffness. A.B.-T.--data curation, formal analysis, methodology, software, visualization, writing--original draft. P.S.--conceptualization, methodology, project administration, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research was partially funded from the European Union's Horizon 2020 research and innovation programme under grant agreement No 643636 \"Sound of Vision\".