Becas Cátedra Securitas Direct-Verisure 2020-2021

Dentro de las actividades de la Cátedra Securitas Direct – Verisure se contempla el establecimiento de un programa de becas en áreas de interés para la empresa y que complementen el proceso formativo de los estudiantes.

Por lo tanto, se lanza esta convocatoria de becas para el presente curso académico.

Fecha de inicio de las becas: preferiblemente el 14 de Octubre de 2020

Duración: 9 meses

Dedicación: 4 horas/día

Remuneración: 500 € / mes

Posibilidad de realizar el Trabajo Fin de Máster, Trabajo Fin de Grado y prácticas en empresas.

Interesados enviar un correo electrónico con el Curriculum Vitae indicando en el asunto [Becas cátedra Securitas] a la siguiente dirección:

Más información:

Oferta de becas de la Cátedra Kairós – Curso 2020/21

La Cátedra Kairós creada como colaboración entre la empresa Kairós Digital Solutions  y los Grupos de Investigación “B105 Electronic Systems Lab” e “Internet de nueva generación“ realiza la convocatoria de 6 becas Kairós para el curso 2020/21.

Los interesados en alguna de las becas deberán enviar un correo electrónico a cualquiera de las siguientes direcciones o con las siguiente información:

  • Cover letter
  • Curriculum Vitae
  • Beca/s en las que estás interesado y la motivación.
  • Situación actual del candidato: curso, asignaturas pendientes, limitaciones de horarios, interés en realizar TFG, TFM, Prácticas en Empresa, etc.

Información de interés:

  • Fecha límite de recepción de la documentación: 21 de Septiembre de 2020 a las 12:00
  • Fecha estimada de inicio de las becas: 1 de Octubre de 2020

Para más información visite la página de la Cátedra

TFM: Design and evaluation of electromyography signal processing techniques using resource-constrained devices

On July 15, 2020, the master student Pablo Sarabia Ortiz read and defended his master thesis entitled “Design and evaluation of electromyography signal processing techniques using resource-constrained devices”. This master thesis is enclosed in the current B105 Electronic Systems Lab research topic of acquiring and processing electromyography (EMG) signals on the human body to achieve a wearable health device based on EMG signals.

Surface electromyographic (sEMG) is an acquisition technique based on recording muscles potential over the skin. sEMG based devices have a wide range of application: early diagnose and treatment of neurodegenerative diseases, tracking of daily activities, rehabilitation, and adaptive training.  sEMG signals are complex and present different challenges like great amount of data, complex signals, and significant variations between subjects and days. For most of these applications is required to identify and classify the gestures or movements that the user is doing. This classification is a task that requires great amount of resources (memory and CPU). This thesis is focused in understanding the sEMG signal characteristics and designing a classifier for hand gestures, by using the custom acquisition board.

Picture of the hardware used for sEMG acquisition. On the left the electrodes, on top of the image a preamplifier and on the bottom right corner the stack of PCBs composed of the microcontroller and the ADC.
Picture of the hardware used for sEMG acquisition. On the left the electrodes, on top of the image a preamplifier and on the bottom right corner the stack of PCBs composed of the microcontroller and the ADC.

First, a quantitative analysis of the sEMG data was carried out by using parallel factor analysis (PARAFAC). The dataset used was NINAPRO, because it contains numerous different hand gestures performed by different subjects in different days. This PARAFAC analysis showed that is possible to reduce the number of channels from 16 to 4 without significant loss of information, as shown in the figure below. It also showed that most of the information is under the 350 Hz range. PARAFAC proved to be an interesting method for choosing the most significant channels in the dataset.

Process followed to do the PARAFAC analysis of the data from the NINAPRO dataset.
Process followed to do the PARAFAC analysis of the data from the NINAPRO dataset.

Second, an acquisition system to log the data to the computer was established. This acquisition system had 4 channels at a sampling rate of 500 Hz each. The data once logged was formatted and stored using MATLAB. Eight different gestures were performed, as shown in the figure. Then a support vector (SVM) machine classifier was trained obtaining an 99% accuracy in cross validation.

Table with all the gestures recorded for the master thesis.
Table with all the gestures recorded for the master thesis.

Third, a two level three variables factorial design was carried out to model the influence of the design variables in three features of the classifier (execution time, memory footprint and accuracy). The three design variables studied were: codification of the SVM, data precision (float32 or float64) and length of the sample. The results shown that float64 should never be used, and that there is always a tradeoff between classifier accuracy versus the memory footprint and speed of the classifier. It was also identified the memory footprint as the bottleneck for the use of the classifier in a resource-constrained device. It was achieved a reduction of 1/14 of the original memory footprint and a speedup of 233 times, however accuracy of the classifier lowered to 85%.

TFM: Development of a wearable device for monitoring therapy animals

Animals have long been part of the human experience, serving multiple purposes throughout history, from food to companionship. In recent years, the therapeutic potential that offers the use of animals to help people overcome illness and/or mental disorders has been increasingly recognized, leading to more healthcare facilities providing Animal-Assisted-Interventions (AAIs) to their patients.

The steadily increasing popularity of AAIs programs is supported by the fact that they deliver health benefits to the patients. A growing literature gathers testimonials of veterinarians, psychologists and other pet-therapy enthusiasts about the effectiveness of AAIs programs for humans. In contrast, very few researchers have focused on the possible ill effects that AAIs programs have on the animals themselves.

Nowadays, the present lines of research that are trying to determine both positive and negative effects on the physical and mental well-being of the animals involved in AAIs are divided in two groups:

  • Non-invasive methodologies based on the interpretation of the body language of the animals. For instance, a dog’s wagging tail may mean different things depending on the speed of the wag, and whether the full tail or just the tip is wagging. Besides, dogs also use a range of what the renowned dog trainer Turid Rugaas refers to as
    “calming signals” that they use to defuse stressful situations. For example, a dog may lick her nose, sniff the ground, yawn, turn away, or stare in response to a stressful situation. The main drawback of these methodologies is the subjectivity of the observer.
  • Invasive methodologies based on medical procedures such as blood extractions, faces analysis or saliva analysis in order to measure certain hormones levels that could have correlation with the stress that could be suffering the animals during the AAIs. Despite of the fact of the objectivity of the results, due to the nature of these procedures, these interventions by themselves could provoke stress in the animals.

Thus, the aim of this Master’s Thesis is to design and develop an electronic wearable device to collect physiological and behavioral variables in dogs participating in the AAIs in order to extract stress patterns in different scenarios and therefore determine objectively the effects of the AAIs in the animal welfare. The data gathered will be analyzed by ethologists than can
evaluate what is happening in the process of interaction of the therapy dog with the rest of the actors. This way, conclusions related to the dog state in the different stages of therapy could be obtained, allowing the modification of the routines to increase the dog’s quality of life.

It is worth mentioning that this project is being carried out in collaboration with the Escuela Técnica de Ingenieros de Telecomunicación and the animals and society chair at the Universidad Rey Juan Carlos, which will be in charge of the visualization and interpretation, respectively, of the data acquired by the system to be developed in this Master’s Thesis.

To achieve this goal, this Master’s Thesis has focused on the development of the electronic wearable device that will monitor the therapy dog. This development has covered both the design and hardware implementation of the three printed circuit boards that make up the device, as well as the software implementation of the drivers needed to control each sensor individually in addition to the application architecture at the user level.

Both software implementations are based on two existing design patterns that provide modularity to the system in order to incorporate new sensors to the device. Finally, in order to validate the design and implementation
phases at hardware and software level, functional tests of the system have been carried out which have allowed conclusions to be drawn on the development of this project as well as to propose future lines to improve its current state.

TFM: Design and implementation of a hospital automation and signaling system

The work has been carried out within the R&D area of ACE Business Group. The project has been started from scratch, being the only development engineer involved.

In hospitals and nursing homes there is a need to use a healthcare system that allows an effective communication between the patient and the nurses, in addition to monitoring possible events that allow immediate actions.

The project proposes the design of a complete solution that allows integrating a low-cost peripherals network in a modular and user-configurable way. The design focuses on a centralized architecture with a gateway capable of automating the behavior of sensors and actuators in its environment, with a wired or wireless connection. In addition to automation, the user receives notifications of each of the events, allowing real-time monitoring of the rooms.

During the project, two electronic systems, a central node, and an assistance push-button mechanism have been designed, with the aim of integrating a generic assistance call system and a scalable communications protocol to a future more complex sensors and actuators network. The development deals with both the hardware and the software necessary for its implementation, as well as a set of tests to validate its operation for future commercialization.

The gateway acts as a hub for nodes within the rooms, with BLE, Wifi, Ethernet, RS485 interfaces and GPIO ports. The design is done in a modular and scalable way over FreeRTOS Operating System.

The push button is designed with 3 different configurations on the same PCB: wired, wireless, or by direct digital I/O. It is oriented to an ultra low consumption design with the ability to last for several years over BLE.