TFM: DISEÑO DE UN SISTEMA DE MONITORIZACIÓN DE CONSTANTES VITALES DE ROEDORES A DISTANCIA
Posted on by rookie

The VISNE project, from the B105 Electronic Systems Lab at the ETSIT in collaboration with the Neuro-Computing and Neuro-Robotics group at the Complutense University, focuses on the development of a thalamic prosthesis to restore vision in humans. In its initial phases, this system will be tested on rodents, specifically mice, through behavioral tests in an operant conditioning chamber, also known as a Skinner box (as can be seen in the image below) .

However, the use of animals for medical research is one of the most controversial and debated topics in the modern scientific community. Therefore, ensuring the welfare of the animals has become a fundamental task, and to this end, the aim is to remotely monitor their vital signs.

In this master’s thesis, two techniques for monitoring mice were evaluated and tested: an infrared camera (MLX90640 from Melexis) for temperature measurement and an FMCW radar (AWR6843AOP from Texas Instruments) for tracking heart rate and respiration through thoracic variations. An electronic system was designed and implemented, consisting of two components: a proof-of-concept using both sensors and a prototype PCB that integrates the temperature monitoring system.

The proof of concept was integrated with a central interface within a Skinner box for mice. A user-friendly graphical interface was developed to display measurements from both sensors over time. A program was created using the infrared camera to detect the rodent’s warm body, positioning it at the central point to enable precise tracking and presence detection. The motion data collected could be used to estimate the rodent’s stress level during behavioral tests. Additionally, this program records temperature and movement data in text files for further analysis.

System tests demonstrated that the camera enabled continuous monitoring of the mouse’s body temperature, while the radar successfully measured heart rate in humans, with results closely aligning with those obtained through traditional methods. However, the radar measurements exhibited notable variability. Additionally, the system effectively measured the respiratory cycle and accurately detected presence.

The Printed Circuit Board (PCB) for the prototype temperature monitoring system was designed and manufactured with compact dimensions of 50 x 103 mm. It includes wireless connectivity and supports data storage on a microSD card. Additionally, the PCB is equipped with a micro-USB port for easy programming and powering of the system. All the TFM’s files are available in this repository: https://bitbucket.org/b105upm/tfm_rpeon/

The PCB has been successfully soldered, tested, and programmed. The embedded software enables data communication with a central node using the MQTT protocol, while the central server capture the data and displays thermal images on a web interface. All the embedded software of this system is located in this repository: https://bitbucket.org/b105upm/skinnerbox

TFM: Design strategies for detecting action potentials in actions based on movements
Posted on by anacp

This work is located in the studies of the brain and their signals. The puspose is to know when someone wants to make a movement. Thus, it might help to people that actually are not able to move a member of their body or more. Mainly, it is focused in the design of strategies for detection of action potentials or spikes when a movement wants to be made. This study is not looking for action potentials form, it is looking for patterns and characteristics that allow to recognize the movement. Although there are action potentials covered by the signals taken from the electrodes, but they are unavailable.

To accomplish the objective, it is used the EEG signals of a public data base. It is selected the ones related to the movement of the hands, concretely, the movement of open and close the fist. Signal sources of noise that dirty the signal are analyzed, they are called artifacts, and then, filtering stage comes, giving the signals of below for movement and no movement.

slotMov15slotRest15

Now, possible algorithms are checked. It is decided to use the Wavelet transform and the way in which it obtains the energy of the signal. Thanks to the calculation of Wavelet energy in 22 subjects, it is reached to the conclusion that Wavelet energy for movement is higher than for no movement. So, electrodes that comply with this condition at 100% are 4.

The final algorithm is implemented three features: correlation, a parameter that gives a relation between two signals, their energy range and their energy average. It could be said that algorithm has two parts: a training stage and a decision stage. Inside decision part, there are three algorithms: ProMove, ProMove + improve and Logic. The basic difference among ProMove and Logic is an or (||) and an and (&). The improve is based on empiric knowledge.

commonstages

 

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Final conclusions show that the signals between subjects are very changing. Therefore, same algorithm is not useful for everybody. To some subjects, the successful probability is very high (92,86% – 1 fail), while for others is more low than what is expected (50% – 7 fail). With these test, the importance in the length of the signals is reflected, because if signals for subjects with more than 3 fails are inversely processed, the fails are reduced. The most useful algorithm for a larger number of subjects is ProMove + improve.