TFM: Design and implementation of a neuromuscular stimulator based on electromagnetic induction

In recent years, the development of medical devices has become a key element in order to face the research of new treatments and diagnosis of different diseases. These devices are designed to reduce the negative effects of some pathologies in which traditional pharmacologic treatments are not effective. An example of these pathologies are those that are produced due to a nervous system deterioration. Dysfunction of the human nervous system can be caused by situations such as a stroke or an accident in which the spinal cord is injured. This deterioration can lead to signal transmission disorders to the muscles, which are responsible for the movement of the body, and lead to muscle weakness or paralysis. The pathologies affecting the spinal cord, such as paraplegia, block communication between the central nervous system and the nerves, responsible for transmitting signals to the muscles. Therefore, these signals which are sent to the muscle from the brain can not be propagated, preventing the contraction and relaxation of muscles that give rise to movement. For all these reasons, different techniques of functional electrical stimulation (FES) have been developed and their use has been growing during these years. They are based on the concept of induction of the muscle contraction through the generation of electrical stimulus in the nerve. This technique produces skin damage and pain sensation. On the other hand, artificial stimulation by electromagnetic induction has been barely studied. Magnetic stimulation is based on the induction of a time-varying magnetic field that causes a current into the tissue and therefore, into the nerve. In this End of Master Project a prototype is designed to work on this less common technique.

Model of the generation and propagation of the signal that produces the muscle contraction.

This required a first stage of research on the state of the art in applying electromagnetic induction in neuromuscular stimulation techniques and understanding the main characteristics of the devices used in them. From this study, the advantages and disadvantages are established, and at the end, the characteristics to be considered in the design of the prototype. The prototype is based on a modular solution called modular multilevel converter, which allows to obtain the desired voltage and current to generate a time-varying magnetic field that induces the stimulating current in the nerve.

The device designed in this project is composed by a hardware part and by a software part. In the hardware part of this modular multilevel converter, the microtopology is established, based on the modules as a unit, and the macrotopology, based on the combination of the modules. The different modules and their components are implemented on a printed circuit board (PCB) that will serve as support and connection of the modules. The software part defines the control signals that allow each of the modules to define their working states, and therefore their contribution to the signal that generates the time-varying magnetic field. The designed software allows the modules to work in a synchronized relationship in the macrotopology of the system.

The results obtained on this project allows establishing some first conclusions about the use of modular multilvel converters focused on magnetic stimulation. The control signals of the modules are a great challenge for the implementation of a system composed of more modules than those presented in the prototype. In addition, the size of the system with a larger number of modules, necessary to cause an effective stimulation that leads to muscle contraction, must be considered in successive design iterations. This prototype establishes the first milestones towards the development of a platform that allows the magnetic stimulation of the motor nerves.

TFG: Design and development of wireless sensorization system of a low-cost robotic arm

In the past the last few years, we have observed that new technologies have been improving the quality of life of all people, especially those who have difficulties in their daily lives. An example of this is prosthesis, which offers autonomy to those who need it, recovering part of the lost mobility.

This End of Degree project intends to carry out the sensorization of a wireless robotic arm. The objective of this project is to obtain information on the movement of the prosthesis and to analyse its behavior to improve the functioning of future prostheses, in such a way that it is as similar as possible to a human arm. We have specially focused on the movement of the wrist and the pressure exerted by the index finger and thumb.

Firstly, a previous study was carried out where we analysed the different angles of wrist rotation and their amplitude. Also, we studied the different ways that the hand makes to apply pressure to an object. On the other hand, we made a study of the different prostheses that exist today, separating them according to their mobility, to choose which was the robotic arm that better adapted to our study. Then, all the pieces of that arm were printed in a 3D printer to make its assembly.

Once the previous study has been carried out, the selection of sensors has been made. To do this, we made a small analysis of the different procedures that use these sensors to obtain the desired measurement. After this, a software has been created to obtain these measurements with the aim of being able to be interpreted by the user, through a graphic interface. A demo represents the movement of a human arm through the data provided by the sensor, as you can see in the figure above. The other demo is in charge of symbolizing through colors the different force exerted by the thumb and index finger.

Finally, different tests have been carried out to analyse the movement of the wrist and the pressure  exerted  by  the  fingers  according  to  the  different  positions  of  the  arm  and  using different forces.

TFG: Development of algorithms for monitoring physiological parameters to assist drivers

One of the main problems that we face today is traffic accidents. In recent years there have been more than 1000 deaths per year in Spain due to this reason, however, it is extremely difficult to find products on the market that assist the driver to deal with this problem.

In order to provide a solution to the problem outlined, at the B105 Electronic Systems Lab, a bracelet wearable was designed, which monitors the driver’s body temperature, stress level, heart rate, blood pressure, and the level of alcohol in the air.  This device is intended to provide a tool for the drivers to assist them to check if they are physically good and mentally ready to drive. However, the reliability expected in this device was not achieved, to use it as an end system in a user, due to a lack of time. For this reason, the main objective of this TFG has been to achieve the greatest performance of the electronic device designed in the previous project.

The first step has been to carry out a study of other similar products that can be found in the market, as well as the design of the device.

Then, an analysis of the parameters obtained with the bracelet has been conducted, to understand what aspects need to be known about them to measure them, and the different methods that exist to obtain them. In addition, the measurement method used by the device for each of them has been analysed in further detail, focusing on what problems it could present, and which factors could affect them.

Afterwards, tests have been carried out in all the modules in a separate way, in which the previous analysis has been considered. Different measurements have been performed on all the sensors during the tests, to calibrate them and to check their behaviour towards the factors that affect them.Through these tests, it has been concluded which is the optimal method to obtain each one of the parameters, the design problems that the device presents, and how it could be improved.

Lastly, the integration of all the modules has been carried out, in which besides obtaining all the parameters considering the conclusions obtained from the tests, an alarm system has been carried out. This system warns the user by the vibration of the bracelet, if a value out of a healthy range is detected in any of the measurements on the parameters. This integration has also been tested and depurated using the debugger.

Finally, it can be concluded that the main objective of the project has been achieved, although some changes would be necessary to improve its functionality, in order to be used as an end device.

TFM: Low-resource electronic system design for motion detection based on surface electromyography (SEMG)

There are certain electronic devices that use surface electromyography signals for many purposes such as for muscle rehabilitation or to control a hand prosthetic, among others. But most of them use powerful microprocessors and external computers, making them expensive, and having a large power consumption.

    Therefore, these devices are only available for a narrow group of people when in reality a great amount of them are in need of them. They need to be fast, cheaper and have low power consumption. For those reasons, the elaboration of this project is encouraged.

The goal of this project is to design and implement a system that recognize different gestures and identifies them, calculates the muscle’s force, and detects the muscle’s activation time (when it goes from rest to being activated), through the implementation of low resources. This will have a positive impact on its cost, its power, and its autonomy.

Block diagram of the system

    C language has been used as the programming tool for this project due to the possibility of high-level programming. As for the hardware, microprocessors Cortex®-M4 and Cortex®-M0 have been used. The decision of implementing the program in both was to demonstrate that the same results can be achieved with both a more powerful processor and a less powerful one. Each microprocessor is integrated in a STM32 board, which also has a user button that by pressing it, it calibrates the system. So, anyone can obtain accurate results.

For this project, SEMG signals data was provided by the University of California at Berkeley. The data was taken from five different subjects wearing a bracelet with a 4×16 array of surface electrodes (64 channels). They performed 21 different hand gestures throughout many experiments involving resting position, different effort levels, and replacement of the surface electrodes.

4×16 array of electrodes

By reading the data and calculating a group set of features, it was possible to successfully achieve gesture recognition with the base subject, and through calibration with the rest of the subjects. It was also conceivable to measure the muscle’s force and detect its activation time.

Regarding the power consumption, the system implemented with the Cortex®-M4 microprocessor consumes about 90mA when active and the one with the Cortex®-M0 consumes about 83mA at 3.3V. Therefore, it has been proven that both systems are low power due to the use of low resources, but the M0 microprocessor consumes less. As for the time response of the program, it was measured the Cortex®-M4 takes 208.3 seconds total to read and process the data whereas the Cortex®-M0 takes 320.3 seconds.

For the proper creation of a complete system, a device was designed by integrating the data acquisition, the microprocessor, a transceiver for communication with other devices, the user’s interface (button), and its power supply.

TFG: Design and implementation of algorithm for Deep Brain Stimulation devices

In recent years, Deep Brain Stimulation (DBS) techniques using electrical signals have been studied. These techniques allow us to reduce certain brain diseases’ symptoms, as is the case in Parkinson’s disease, which causes heavy trembling in patients’ limbs.

Marketed systems use stimulation signals with a fixed shape, which may entail certain secondary effects and an increase in the long-term symptoms, despite of the continuous use of these methods. That is why DBS adaptative algorithms are being developed. These algorithms can adapt to the optimal stimulating current depending on the patient’s state. They operate in one way or another depending on the information provided by various biomarkers.

A biomarker is a tool used for obtaining different biometric data from the patient. The most common biomarker when treating with DBS is the local field potential (LFP), whose signal amplitude is closely related to the symptoms being experienced.

One of the most widely known adaptative algorithms, the dual threshold algorithm, makes use the of the LFP signal as input data. This algorithm consists of varying the amplitude value of a PWM stimulation signal depending on the data provided by the biomarker. In this project the goal is to develop an algorithm which may serve as an alternate method to the dual threshold algorithm. This algorithm seeks to balance between efficiency (data looked for in the biomarkers) and energy consumption, as reducing the latter may result in a longer lifespan for the device running it. As being an implantable system requires it to have the longest lifespan possible