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Revolutionizing Epilepsy Monitoring with a Discreet Smart Sock: Validation of a Novel Form Factor for Multimodal Biosignal Acquisition. EpilFootSense combines e-textile technology to measure nervous system activity at the foot and an optical sensor to estimate the heart rate from the ankle.


Epilepsy is a chronic neurological disorder that affects millions of people worldwide, and current treatment options are not effective for all patients. Wearable devices have the potential to improve patient outcomes and quality of life by providing continuous monitoring and allowing doctors to track the patient’s condition. However, the form factor, look and overall usability of the wearable device plays a role in its long-term acceptability by the patients. Lower limb wearable devices, such as smart socks, offer a more discreet and less intrusive option for epilepsy monitoring and self-management compared to other currently available arm and wristband-like wearable devices.

Our research thus aims to determine the feasibility of a smartsock in monitoring seizures unobtrusively. A smartsock device for multimodal biosignal acquisition at the foot was developed and tested in subjects, using conductive textiles and sensors for Electrodermal Activity (EDA) and Photoplethysmography (PPG). This research project was the focus of the Master thesis in Biomedical Engineering of the ScientISST Afonso Ferreira, in collaboration with Instituto de Telecomunicações, University of Aveiro and the textile industry company Meia Mania Lda.

EpiFootSense EDA electrodes


The prototype developed for our research experiments used the ScientISST SENSE acquisition board which connected to the EDA, PPG, and accelerometer sensors through the analog channels. Thanks to the board's versatility and modular design, we were able to create a compact hardware unit by integrating these components and attaching it to the sock on the medial side of the ankle.

The required hardware were:

  • 1 ScientISST CORE

  • 1 EDA sensor connected to conductive fabric (electrodes)

  • 1 PPG sensor

  • 1 Accelerometer


To validate the prototype, we monitored the EDA and PPG signals in real time on a population of healthy subjects using the smart sock, while simultaneously using a gold standard device to monitor the same signals at the hand (reference body location). We then assessed the similarity of the acquired signals from both locations using state-of-the-art metrics, including the consensus of EDA peaks between the two locations and the HR estimation error for the PPG signals.


Project Gallery

Technologies Used

Sense Python API
ScientISST BioSPPy

Team of ScientISSTs

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