Litoiu, MarinSergio, LaurenAhuja, Harit2024-03-182024-03-182024-03-16https://hdl.handle.net/10315/41964In the realm of the Internet of Things (IoT) and Machine learning (ML), there is a growing demand for applications that can improve healthcare. By integrating sensors, cloud computing and ML we can create a powerful platform that enables insights into healthcare. Building upon these concepts, we propose a novel approach to address the widespread problem of long COVID. We utilize a wearable device to capture electroencephalogram (EEG) readings, which are then transformed through a set of processing steps into actionable decisions. We use a methodology that initiates data collection from a Cognitive-Motor Integration (CMI) task, followed by data preprocessing, feature engineering, and then the application of ML and advanced Deep Learning (DL) algorithms. To address challenges like data scarcity and privacy concerns, we generate synthetic data and train them using the same model as the original data for comparative analysis. Our method was tested on real cases and achieved prominent results: the CNN-LSTM model achieved 83% accuracy with original data and surged to 93% using synthetic data.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Artificial intelligenceComputer scienceNeurosciencesElectronic Thesis or Dissertation2024-03-16Machine LearningLong COVIDInternet of ThingsElectroencephalogramCognitive motor integration