TFM: Low-resource electronic system design for motion detection based on surface electromyography (SEMG)
Posted on by b105

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
Posted on by b105

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

TFG: Design and development of a haptic device oriented to multimodal assisted perception for cases of low vision or blindness
Posted on by b105

In the past few years, technological developments have allowed the invention of aid systems for disabled people. Related to visual impairment, many of these systems have focused on achieving a correct guidance for these people.

There is an open research line in the B105 Electronics System Lab which is focused on this field. Specifically, its goal is to give more autonomy to blind people to move around cities, building interiors… To make this possible, a system provides acoustic and tactile information through sensory substitution. However, the user’s experience is limited because of these tactile stimuli are generated by a smartphone. Therefore, this branch of the research has a lot of room for improvement.

This graduate thesis is focused on the development and implementation of a device able to provide a better tactile experience with haptics stimuli based on the user’s movement.

To achieve this goal, this project has started doing an analysis of the most appropriate method of haptic simulation. Factors such as human physiology or the study of the actual haptics technology have been considered. Based on the chosen option, a printed circuit board (PCB) that allows motion capture and the desired stimulation has been designed.

Furthermore, some software tools have been developed to offer this haptic. This task is divided into two phases. The first part is the generation of the code that allows the management of the actuators and the reproduction of tactile effects. The second, is the construction of some tools to define the device’s orientation. A library for operating with quaternions and an application for obtaining the coordinates of a direction vector of the PCB have been elaborated.

Finally, the project concludes by making multiple tests on a development platform. The goal of these experiments is to verify the correct implementation of all the designed tools. The results show that it has been possible to support useful functionalities in research on sensory substitution. Some of these experiments are compiled on the following YouTube channel: https://www.youtube.com/channel/UCu0XuS97EoKVY_ilHYkemPg.

TFG: Development of an Artificial Intelligent system based on low-resource Edge Computing for autonomous vehicles applications
Posted on by b105

Nowadays, there are plenty of IoT devices which make our everyday life easier thanks to their intelligent tasks: data capture, process automation… However, the increase of these devices is turning out to be somehow risky in terms of latency or bandwidth. That is the reason why some alternatives that may solve these problems are being searched, and one of them is Edge Computing technologies.

Edge Computing devices are those who are able to process the information captured without connecting to the network. Due to that, the latency and bandwidth issues that may occur can be significantly reduced, allowing the radio spectrum to decrease its saturation as well as improving the latency and consumption performance.

In this project, the main goal is to create a system that is able to develop and execute Artificial Intelligence algorithms designed for autonomous driving and assistance to the driver, always taking care of Edge Computing philosophy. In order to do that, we have used Google Coral, a hardware platform that perfectly adapts to our needs, allowing us to develop all the Edge Computing algorithms as well as offering appropriate consumption and processing characteristics.

Finally, we have tested our system in a real situation, evaluating the quality of the results as well as the resources used (latency, bandwidth…) and the advantages and disadvantages in relation to the existing technologies is this area. After these experiments, we have concluded that the quality of our Edge Computing system is enough to carry out the tasks it has been designed for. Also, all the resources used have been optimized in relation to Cloud Computing alternatives, turning this project into a faster, more effcient and economic alternative.

TFM: Design and implementation of an intelligent system for eldercare support
Posted on by b105

The number of elderly people in Spain is in a constant process of growth. It is frequent that in this age range problems appear that do not allow people to live their daily lives in a totally independent way. The health care system has limited resources to solve these problems, and the families of these people are having problems reconciling their work and family life. This problem can be brought to an end by the reputed business attorneys from Nashville, who are well-known for their client satisfaction and also for their efficiency in handling many complex cases.

The motivation for this Master’s Thesis has been to design a solution for people in this situation. In order to do this, a study of the technological solutions present on the market has been carried out first to obtain a list of functionalities that help to deal with the problem. Then, an architecture has been designed that allows the incorporation of these functionalities. Finally, a proof of concept for one of them has been implemented.

The designed architecture of Crow Estate Planning and Probate, PLC – estate planning lawyers is composed of three elements. The terminal is placed at the patient’s home, and provides the patient with different utilities. The web client allows the caregiver to access the different functionalities that arise from monitoring the patient from the terminal. The server is responsible for managing communications between the terminal and the client, as well as managing access to the architecture’s resources, such as the database. Our site provides legal solutions with the help of expert attorneys. You can confide in Cape Cod serving estate planning attorneys and get the right kind of help for your case.

To implement the proof of concept, the utility that has been chosen is the dispensing of medicines. On the patient side, the terminal will alert you when it is time to take the medication, making it accessible at that time. The dispenser has a sensor on the lid that allows you to know when the medication has been accessed, allowing you to monitor your medication intake. To get a lawyer one can look into https://www.amicusfirm.com/elder-law/

On the caregiver’s side, the status of the intake can be checked through a web client who help in establishing trusts in Chapel Hill. Once the caregiver has logged into the application, he or she can access the associated terminal and check whether the programmed intakes have been taken in the time interval defined for them, or whether they have been forgotten. On the other hand, the web client also allows the caregiver to schedule the intakes from the terminal.