Real-time extensive livestock monitoring using lpwan smart wearable and infrastructure

Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest

Dimensionality reduction for smart IoT sensors

Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation

Reducción de dimensionalidad y técnicas de inferencia de estado para sensores inteligentes

Este proyecto nace del intento de reducir la cantidad de datos enviados por sensores inteligentes para prolongar así su vida útil y dotarles de cierta seguridad al realizar una abstracción de los datos en crudo haciendo imposible su interpretación.Para ello se hace uso de una técnica ya desarrollada hace años, el vector quantization pero añadiéndole una serie de mejoras que permitan recalcular la disposición de los centroides para conseguir minimizar el error de reconstrucción. Se ha desarrollado esta técnica en lenguaje Python y se va a evaluar con una base de datos existente variando, los parámetros de muestreo, el uso de codificación por media y desviación típica, la composición con una o las tres componentes del sensor inercial y el tamaño de la red de entrenamiento.Con este sistema definido, se propone una clasificación de los datos reducidos para intentar sacar aun mayor índice de compresión frente a los datos enviados por el sensor en crudo.Una vez verificado el comportamiento del modelo se implementa en lenguaje micro Python en un sensor inteligente para evaluar el sistema compresor frente a su uso convencional.<br /

On the role of gestures in human-robot interaction

This thesis investigates the gestural interaction problem and in particular the usage of gestures for human-robot interaction. The lack of a clear definition of the problem statement and a common terminology resulted in a fragmented field of research where building upon prior work is rare. The scope of the research presented in this thesis, therefore, consists in laying the foundation to help the community to build a more homogeneous research field. The main contributions of this thesis are twofold: (i) a taxonomy to define gestures; and (ii) an ingegneristic definition of the gestural interaction problem. The contributions resulted is a schema to represent the existing literature in a more organic way, helping future researchers to identify existing technologies and applications, also thanks to an extensive literature review. Furthermore, the defined problem has been studied in two of its specialization: (i) direct control and (ii) teaching of a robotic manipulator, which leads to the development of technological solutions for gesture sensing, detection and classification, which can possibly be applied to other contexts