In the last couple of years, wearable devices have gained popularity, and their use has extended to numerous fields, including the sanitary sector. The increasing number of wearable devices that are being used in healthcare bring numerous advantages, such as a deallocated medicine in which patients can reduce the total of visits to hospitals or sanitary centers.

With the development of medical wearable devices, the mobile communications have also grown. This is the case of 5G, that it is becoming widely used. Therefore, medical wearable devices are starting to use 5G, which brings the necessity to provide the developers of these devices with a platform that helps them to test 5G communications.

While the main goal of the project is to design a platform for medical devices that use 5G, there are some steps that need to be covered first such as the selection of a generic 5G module or the medical sensors and tests that have the most compatibility with the platform.

A total of 4 different medical test have been chosen to operate alongside the platform considering the main characteristics of 5G, that are an extremely low latency and the ability to transmit plenty of data. The selected tests are the electroencephalogram (EEG), electrocardiography (ECG), electromyography (EMG) and oximetry.

When it comes to the 5G module, it has been selected after researching in the main providers and manufacturers of 5G products such as Télit, Quectel, Sierra wireless and Thundercomm. Finally, the Thundercomm T55 Development Kit has been selected. This kit includes the TurboX T55 5G module, that allows to test the sub6GHz bands in 5G and has an LGA form factor, making it the perfect candidate to develop the platform for medical devices.

The schematic of the platform for wearable devices have been captured with Altium Designer tool and it has five differentiated blocks as shown in the figure below. These blocks are the power supply, the connections with the medical sensors, connections with a SIM card, the 5G module, that is divided in two different sub blocks, and the antennas.

Alongside with the schematic of the platform for medical devices, a preliminary design of a printed circuit Board (PCB) has been included as shown in the figure below. This layout has been used to have an approximate idea of the dimensions of the platform and the placement and routing of its components. The dimensions of the PCB are 152.4 mm x 101.6 mm, and it has a total of two layers.

The results of this project conclude in a schematic design which provides a complete platform that allows developers to test the 5G connections in medical wearable devices in an efficient way.


Electromyography (EMG) is defined as the discipline related to the detection, analysis and use of the electrical signal that is generated at a muscle’s contraction. On many occasions, generating a database that allows a comprehensive study of measurements is complicated due to the lack of automation of this type of system. The implementation of this type of system in low-cost portable devices is the key to making its use on a large scale feasible.

Picture of the hardware used for control, acquisition and communications. The respective nicknames of these devices are: Heimdall (left), BioACQ (centre) and Cerberus (right).

This work contains the entire development process of an automated 4-channel EMG signal acquisition system. The developed application is based on an ARM Cortex M4 platform internally developed by the B105 Electronic Systems Lab, which suposed a challenge since it is an economic platform with limited resources. Other device used were the signal acquisition board with its amplified probes and the communications module capable of transmitting data in the 434, 868 and 2,400 MHz radio bands.

Diagram of the complete system. The different devices running the developed applications can be seen with the communication interfaces between them.

The application created for this project is divided into modules. The main ones are: the FSM control, the configuration component, the acquisition system and the communications complex. Partitioning the development helps to improve the quality of the code, reduces the time to detect errors and keeps the program simple. One key aspect of the final system is the use of a wireless link for augmented usability and galvanic protection. Additionally, a graphical user interface is stablished which offers live data representation. All the code regarding the application is available via the following link:

Diagram of the finite state machine in charge of controlling the slave module. The transitions are controled via the incoming commands from the control interface.

The project also contains a section of analysis including performance information about the final solution. The resulting performance analytics show a portable system capable of running on batteries with room for improvement via software optimizations. Furthermore, every developed module is independently evaluated using an exclusively matured testing program. The purpose of this segment is to eliminate all bugs introduced in the code and strengthen the robustness of the system.

Picture showing the main graphical user interface. The panel shown is the configuration one, containing the multiple modifiable parameters of the acquisition system.


In this final project it is done a simple prototype, not complex in order not to overload the packet network or the computational part, of a sensor network, which communicating through wireless body area network (WBAN) are able to characterize daily activities. The nodes used were the Adafruit HUZZAH32 from the company Adafruit, it’s a System on Chip, which incorporates a Wi-Fi module that has been used for the communication between devices.

Firstly, an analysis has been done of the available system. On the one hand, an analysis of the devices and on the other hand a study of one possible characterization from data already collected.

In a second phase, the software of the devices has been modified to create the sensor network and to communicate with each other. For this purpose, the Wi-Fi module of the devices was used, after which, once they were connected, a series of experiments were carried out for different scenarios. With these experiments it has been possible to set thresholds for the development of the final classification algorithm.

Finally, in a third phase, the different tests have been exposed according to the algorithm performed in the second phase.

The results obtained have shown that it is a valid algorithm for the characterization of activities. In addition, an accelerometer has been included to differentiate more activities.


This work is part of the ROBIM project in which the working group B105 Electronic Systems Lab of the University Universidad Politécnica de Madrid collaborates. The ROBIM project takes part in the program Programa Estratégico CIEN with the support of the CDTI (Centro para el desarrollo tecnológico Industrial) and the RDF (Regional Development Forum) for Europe.

The ROBIM project seeks to automate technical inspections of buildings, reducing costs and execution times associated with these processes. The system makes use of a drone for inspection work, thus avoiding the installation of scaffolding and all the security measures that the process requires, which is costly in time and money. Currently, the drone has a communication channel that allows users to obtain information on the process, as well as direct the drone whenever necessary.
The main objective of this work is to create a secondary, safe and effective communication channel, for situations where communication with the main system is not possible. To achieve this, the project stablish the following requierements:

– The device must allow radiocommunication in ISM bands.
– The device has an USB interface to connect with the computer/drone.
– The communication must be reliable by allowing communication throwgh various channels and implementing software-defined radio and cognitive radio.

Therefore, to achieve these objectives, this work proposes the design of a 2-channel device for radiocommunication in the 433 MHz and 868 MHz bands, using two SPIRIT1 transceivers and an ARM Cortex-M4 microcontroller.

Picture of the device’s high-level design

The Hardware design has been made usign the Altium Designer PCB design layout tool . The designed PCB is divided into three parts: the power/communication stage, the control stage with the microcontroller and the radiofrecuency stage with both SPIRIT1 trasnceivers.

Picture of the 3D reconstruction of the board designed in Altium Design tool

The software design has been developed in 2 stages: software design of an application for evaluation boards during the PCB manufacturating process and software design of a final application for the designed PCB.
For the software design of evaluation board, the NUCLEO – L053R8 with the X-NUCLEO-IDS01A4 radio frequency module has been chosen, which allows radio communication in the 868 MHz band. The final design of the software is based on the software of the evaluation board but improving its functionality by adding communication through two channels with a cognitive procedure based on the CSMA / CA protocol and implementing serial communication with the user.

The application designed for the device allows, then, a cognitive communication based on CSMA/CA protocol in bands 433 MHz and 868 MHz in addition to communication with the user and the drone enabling the possibility of the implementation of the second channel for the communication with the drone.

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%.