Abstract
Epilepsy is one of the most common chronic brain diseases, but with the proper medication, patients enjoy everyday life. New trends in Internet of Things approaches are now introduced to detect seizures in users with epilepsy to adjust treatments, reducing mortality rates due to the fatal consequences they develop. Wearable devices on the market can detect motor seizures thanks to their built-in acceleration sensors. However, the limitations of this type of device are the short battery life, a non-ergonomic design, and the high price. In this work, we propose a low-cost system that allows the monitoring of users in residential centers using a wearable device equipped with an acceleration sensor. The epileptic seizure detection algorithm has been built following federated machine learning, creating a general model for all possible users from the models learned in each residential center. Based on the first results, the system has more than 60 h of autonomy, obtaining favorable detection rates in the first simulations. In this case, the information of each user is kept in the local environment, maintaining privacy.
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Acknowledgements
This research has been funded by the R+D+i project RTI2018-095993-B-I00, financed by MCIN/AEI/10.13039/501100011033/ and ERDF “A way to make Europe”; by the Junta de Andalucá with reference P18-RT-1193; by the University of Almería with reference UAL18-TIC-A020-B and by the Department of Informatics of the University of Almería. M. Lupión is supported by FPU program of the Spanish Ministry of Education (FPU19/02756).
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Lupión, M., Sanjuan, J.F., Medina-Quero, J., Ortigosa, P.M. (2023). Epilepsy Seizure Detection Using Low-Cost IoT Devices and a Federated Machine Learning Algorithm. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_22
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