A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos

Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with motor and non-motor symptoms. Depressive symptoms are prevalent in PD, affecting up to 45% of patients. They are often underdiagnosed due to overlapping motor features, such as hypomimia. This study explores deep learning (DL) models-ViViT, Video Swin Tiny, and 3D CNN-LSTM with attention layers-to assess the presence and severity of depressive symptoms, as detected by the Geriatric Depression Scale (GDS), in PD patients through facial video analysis. The same parameters were assessed in a secondary analysis taking into account whether patients were one hour after (ON-medication state) or 12 hours without (OFF-medication state) dopaminergic medication. Using a dataset of 1,875 videos from 178 patients, the Video Swin Tiny model achieved the highest performance, with up to 94% accuracy and 93.7% F1-score in binary classification (presence of absence of depressive symptoms), and 87.1% accuracy with an 85.4% F1-score in multiclass tasks (absence or mild or severe depressive symptoms).

Parkinson's disease patient rehabilitation using gaming platforms: lessons learnt

Parkinson's disease (PD) is a progressive neurodegenerative movement disorder where motor dysfunction gradually increases as the disease progress. In addition to administering dopaminergic PD-specific drugs, attending neurologists strongly recommend regular exercise combined with physiotherapy. However, because of the long-term nature of the disease, patients following traditional rehabilitation programs may get bored, lose interest and eventually drop out as a direct result of the repeatability and predictability of the prescribed exercises. Technology supported opportunities to liven up a daily exercise schedule have appeared in the form of character-based, virtual reality games which promote physical training in a non-linear and looser fashion and provide an experience that varies from one game loop the next. Such "exergames", a word that results from the amalgamation of the words "exercise" and "game" challenge patients into performing movements of varying complexity in a playful and immersive virtual environment. Today's game consoles such as Nintendo's Wii, Sony PlayStation Eye and Microsoft's Kinect sensor present new opportunities to infuse motivation and variety to an otherwise mundane physiotherapy routine. In this paper we present some of these approaches, discuss their suitability for these PD patients, mainly on the basis of demands made on balance, agility and gesture precision, and present design principles that exergame platforms must comply with in order to be suitable for PD patients.