Abstract: Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease / Marcante, A; Di Marco, R; Gentile, G; Pellicano, C; Assogna, F; Pontieri, Fe; Spalletta, G; Macchiusi, L; Gatsios, D; Giannakis, A; Chondrogiorgi, M; Konitsiotis, S; Fotiadis, Di; Antonini, A. - In: SENSORS. - ISSN 1424-8220. - 21:1(2021), pp. 1-12. [10.3390/s21010128]

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease

Pontieri FE;
2021

Abstract

Abstract: Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.
2021
parkinson’s disease; freezing of gait; wearable device; insoles; accelerometer; gait monitoring
01 Pubblicazione su rivista::01a Articolo in rivista
Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease / Marcante, A; Di Marco, R; Gentile, G; Pellicano, C; Assogna, F; Pontieri, Fe; Spalletta, G; Macchiusi, L; Gatsios, D; Giannakis, A; Chondrogiorgi, M; Konitsiotis, S; Fotiadis, Di; Antonini, A. - In: SENSORS. - ISSN 1424-8220. - 21:1(2021), pp. 1-12. [10.3390/s21010128]
File allegati a questo prodotto
File Dimensione Formato  
Marcante_Foot-Pressure-Wearable_2021.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 426.28 kB
Formato Adobe PDF
426.28 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1625359
Citazioni
  • ???jsp.display-item.citation.pmc??? 15
  • Scopus 45
  • ???jsp.display-item.citation.isi??? 43
social impact