Ivan Mazzetta

TL;DR: This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes.

TL;DR: A wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease, and achieves the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype.

TL;DR: In this paper, a wearable system was proposed to detect and predict the typical degradation of the walking pattern preceding freezing of gait (FOG) episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of the system to detect FOG.

TL;DR: A stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time, which has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous.

TL;DR: Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients and have high feasibility in predicting PIGD scores in PD.