TFM: Development of an electronic system on smart garments to aid in the diagnosis of neurodegenerative diseases
Parkinson’s disease is a neurodegenerative disorder that affects the nervous system, which mainly causes motor disorders. It affects more than 160,000 people in Spain. In addition, it is expected that due to the growing aging of the population it will become the most common serious disease by the year 2040.
One of the main problems faced in this disease is the delay in its diagnosis. In addition, it is important to ensure that patients’ symptoms are properly monitored in order to correctly adjust their medication.
Over the past few years, the use of wearable devices to monitor patients outside of the hospital environment has increased. Among these devices, those that use sensorized clothing, so that the sensors are integrated into the tissues, are gaining popularity and have great potential. Although these are still at an early stage of development.
In this context begins this Master’s Thesis, which is part of the research line of the B105 Electronic Systems Lab for the development of wearable devices. The main objective of the project is to design and implement an electronic system to control a set of intelligent clothes for the monitoring of different parameters, which can be connected to other wearable devices in the future.
For this purpose, a study of the symptoms of Parkinson’s disease and how it is possible to monitor them have been carried out. We have also analysed which studies have been conducted in recent years using textile sensor to diagnose or monitor this pathology. Subsequently, it has been searched which intelligent garments are being commercialized in the market. And finally, it has been established which requirements are intended to be fulfilled by the design that is going to be carried out.
Due to the initial work done, the design of the system to be implemented has been carried out.
It consists of a pair of socks and a harness, which communicate through Bluetooth with a mobile phone application.
The socks incorporate 3 textile resistors in the sole of the foot, and an IMU in the ankle to monitor the patient’s gait. While the harness makes use of 3 textile electrodes, whose outputs are filtered by a circuit to obtain the ECG. It also incorporates an IMU in the central part of the chest, to monitor the user’s posture. In addition, both garments make use of a PCB in which they operate the control part and the power supply.
In the software development of the project, FreeRTOS has been used together with a state machine to control the measurements of the sensors of the garments and send the measured values via bluetooth to a mobile application.
In the hardware development, the design and implementation of the PCBs has been carried out.
Finally, we have started to perform unit tests on the development carried out, for the hardware as well as for the software, which should be finalized to verify the complete performance of the developed system.
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
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
Nowadays, artificial neural networks (ANNs) are computational models that, while they have solved many different problems, they require a large amount of memory to execute those solutions. Therefore, their implementation is more common in systems with high-performance capabilities, such as data centers or servers. However, there is an increasing interest in developing these solutions on devices with fewer resources, such as personal computers, mobile devices, and microcontrollers. This situation has led to the proliferation of multiple techniques and tools to reduce the requirements of these models and allow their implementation on those platforms.
The main objective of this project
is the evaluation of a tool for the implementation of artificial neural
networks in microcontroller-based systems. For this
purpose, a practical use case has been defined.
To achieve this objective, the classification of types of tremors in Parkinson’s patients was
chosen as the practical use case. In addition, the Arduino Nano 33 BLE Sense
has been also chosen as the microcontroller-based platform to implement the
solutions of the practical use case.
Later, a dataset has been generated
with real measurements from a sensor on that platform. With this dataset
various experiments have been carried out to determine how the different
structures of artificial neural networks deal with the chosen use case.
Then, based on those experiments,
some appropriate ANNs for classifying types of tremors have been designed, trained,
and evaluated in a system with graphics and tensor processing units and in a microcontroller-based
Finally, based on the results
obtained, the trade-offs between classification accuracy, memory footprint and
inference latency involved in the implementation of ANNs solutions in
microcontroller-based systems have been determined. In addition, a discussion
of the strengths and weaknesses of the selected tool for the implementation of artificial
neural networks in microcontroller-based systems has been presented.