Make predictions about a future health status of PMSS (Parkinson’s Disease, Multiple Sclerosis and Stroke) patients, given a history of tracked predictor variables.
A pre-trained machine learning model that has been tuned for sentiment classification of opinionated utterances.
Developed to detect activities of daily living (ADLs) using acceleration data obtained from wearable inertial sensors.
The ENORA Unobtrusive Sleep Monitoring and Assessment AI-driven platform, with its user-friendly interface and advanced visualization techniques, transforms raw data into a narrative of nightly rest.
A computer vision application that has been trained to recognise the mood of the user given their facial expression. The model has been developed to run as a background service of other mobile…
FER AI toolkit introduces a user-friendly web application that combines facial emotion recognition with separate CNN models for different neurological conditions, integrating with IAM and SemKG for…
A pre-trained machine learning model that has been tuned for sleep stages classification employing wearable originated data.
The HECA, a dedicated medical CA, is designed to assist the user through medical queries and provide him/her with reliable and accurate responses. Utilizing sophisticated Natural Language Processing…
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017558.
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