The aim of this work is to prove that it is possible to develop a system able to detect gestures based only on ultrasonic signals and Edge devices. A set of 7 gestures plus idle has been defined, being possible to combine them to increase the recognized gestures. In order to recognize them, Ultrasound transceivers will be used to detect the 2 dimensional gestures. The Edge device approach implies that the whole data is processed in the device at the network edge rather than depending on external devices or services such as Cloud Computing. The system presented in this paper has been proven to be able to measure Time of Flight (ToF) signals that can be used to recognize multiple gestures by the integration of two transceivers, with an accuracy between 84.18% and 98.4%. Due to the optimization of the preprocessing correlation technique to extract the ToF from the echo signals and our specific firmware design to enable the parallelization of concurrent processes, the system can be implemented as an Edge Device.

The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.

This work studies the feasibility of a novel two-step algorithm for infrastructure and object positioning, using pairwise distances. The proposal is based on the optimization algorithms, Scaling-by-Majorizing-a-Complicated-Function and the Limited-Memory-Broyden-Fletcher-Goldfarb-Shannon. A qualitative evaluation of these algorithms is performed for 3D positioning. As the final stage, smoothing filtering techniques are applied to estimate the trajectory, from the previously obtained positions. This approach can also be used as a synthetic gesture data generator framework. This framework is independent from the hardware and can be used to simulate the estimation of trajectories from noisy distances gathered with a large range of sensors by modifying the noise properties of the initial distances. The framework is validated, using a system of ultrasound transceivers. The results show this framework to be an efficient and simple positioning and filtering approach, accurately reconstructing the real path followed by the mobile object while maintaining low latency. Furthermore, these capabilities can be exploited by using the proposed algorithms for synthetic data generation, as demonstrated in this work, where synthetic ultrasound gesture data are generated.