Development of an Intelligent Embedded Interface for Interpreting Biosignals Recorded by Novel Wearable Devices

Detta är en Master-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Författare: Diego Argüello Ron; [2019]

Nyckelord: ;

Sammanfattning: In recent years there has been a considerable development in the realm of sensing technologies, embedded systems, wireless communication technologies, nanotechnologies, and miniaturization has made it possible to create smart wearable systems that can record data from the human bodies and monitor our daily activities. Most expectations for the successful deployment of wearable devices and their tangible impact for the society is in healthcare. Nevertheless, its use has been limited by the absence of intelligent mobile device interfaces able to process, analyse and inference the recorded data, giving relevant information to the user. On the other hand, new advances in nanotechnology have allowed the creation of so-called electronic skin, which consists in thin and flexible electrodes, easy and comfortable to use. This allows building new wearable devices able to record electrical activity from the surface of the body, which has a large diagnostic and monitoring potential. In this work, the goal is to study the feasibility of using these new sensors for continuous biopotential recording while supporting them with a mobile phone application able to receive, process and analyse the recorded biosignals in order to deliver useful feedback to the user in real-time. The wearable device known as Senso Medical Bio Pot V2 is proposed as a possible candidate to carry out electromyography (EMG), electrocardiography (ECG) and electroencephalography (EEG) recordings via skin tattoo electrodes. Moreover, an Android Application that connects to this device is created. It uses Machine Learning Algorithms embedded on it in order to classify the received signals. Finally, Long-Short Term Memory (LSTM) networks are implemented for classifying EEG and EMG signals. Several conclusions are derived from this work. Firstly, the device Senso Medical Bio Pot V2 is not suitable for its use as a wearable device for biosignal recording. Secondly, the Application designed and simulated offline achieves good performance. As a consequence, it could be used in the future with a suitable wearable sensor and offer good potential for processing and interpreting recorded biosignals with an opportunity to provide real-time feedback to the user in Brain-Computer Interface (BCI) type of applications.

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