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1.
Sensors (Basel) ; 19(4)2019 Feb 23.
Artigo em Inglês | MEDLINE | ID: mdl-30813411

RESUMO

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.


Assuntos
Eletromiografia/métodos , Marcha/fisiologia , Doença de Parkinson/fisiopatologia , Telemedicina/métodos , Dispositivos Eletrônicos Vestíveis , Humanos
2.
Sensors (Basel) ; 18(6)2018 May 29.
Artigo em Inglês | MEDLINE | ID: mdl-29844275

RESUMO

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system 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.


Assuntos
Técnicas Biossensoriais/métodos , Eletromiografia/métodos , Monitorização Fisiológica/métodos , Dispositivos Eletrônicos Vestíveis , Algoritmos , Fontes de Energia Elétrica , Humanos , Processamento de Sinais Assistido por Computador
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