## 4 Discussion  
Our primary motivation for the study was to build a hands-free interface for the visually impaired that would facilitate controlling an electronic travel aid built in our project dedicated to help the visually disabled in independent mobility and travel [26]. The main role of the DUO MLX device and the image processing software is to recover a 3D structure of the environment from the captured stereovision images. The sequences of depth images are then sonified and acoustically presented to the visually impaired user. In [38], we have shown that such a non-visual presentation of the environment can help the blind to detect obstacles and find safe walking paths in the surroundings. One of the complications we encountered in the conducted trials is handling of the device by the blind. The use of an additional remote control to manipulate the interface proved to be inconvenient and occupied the other hand of the user who carried a white cane. An example scenario of how the interface might be used is the following. The navigation system generates a series of audio-haptic signals to indicate obstacles or inform about points of interest. Such a system features numerous settings like loudness, sensitivity range, sonification, and haptic activation schemes. The blind user, while standing still, by a series of head gestures, can move within the menu of the system and select the appropriate setting for the encountered environment or a navigation task at hand. System settings are confirmed by synthetized voice messages. It is important to note that a blind person carries a white cane which occupies one hand and the proposed solution does not need to engage the other hand to control the device. Also, after consulting the visually impaired, we plan another application of the interface. The device can play the role of a remote control that can aid the visually impaired in the activities of daily living, e.g., it can be used to control radio/TV volume without a need to seek for the remote of such devices. Compared with the study reported in [24], we obtained a comparable F1-score of 92% for the parametrized IMU signals. It should be noted, however, that in [24] and in [17], individual gestures were not distinguished, but rather whole body activities, e.g., walking, running, cycling, hence the need for longer time data analysis windows: 5 s, 3 s and 1 s in [24] and 5 s and 12 s in [17]. In our case, we recognize delicate head movements, thereby the shorter the time window, the better the motion recognition. In [17; 18], the IMU sensors were attached to the wrist, in [24] two to five sensors were used and they were mounted in different parts of the body, while in [19], IMU was mounted on the head like in our work. The advantage of our study is that 65 people participated in the experiments and our database is more diverse than e.g., in [19] where only five people took part in the trials. None of the works [17; 18; 19; 24] compared the recognition results of body movements for data derived from the parametrized signals and time domain samples of IMU signals. We also tested the recognition rates from a large pool of different data classifiers, i.e., the decision trees and forests (and their cropped versions), k-NN, and SVMs (with an RBF kernel and a 3 rd degree polynomial kernel). Finally, we show that better classification results can be obtained if the classifiers are trained on raw IMU signals rather than on the parametrized signals. We are aware of the limitations of our study. The participants of the trials were mainly young people recruited from the students. Thus, an open question is on the usability of the system for elder people. Secondly, the trial participants remained seated during data collection. This limitation, for some system applications, should be released.