| 2020

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

Andrea Marcante, Roberto Di Marco, Giovanni Gentile, Clelia Pellicano, Francesca Assogna, Francesco Ernesto Pontieri, Gianfranco Spalletta, Lucia Macchiusi, Dimitris Gatsios, Alexandros Giannakis, Maria Chondrogiorgi, Spyridon Konitsiotis, Dimitrios I. Fotiadis, Angelo Antonini

UOC Recupero e Riabilitazione Funzionale, Ospedale di Lonigo, Azienda ULSS 8 Berica


Parkinson’s disease, freezing of gait, wearable device, insoles, accelerometer, gait monitoring


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.

Moticon's Summary

This study was concerned with the detection of freezing of gate events in patients with Parkinson's disease. The aim in this context was to validate the detection of freezing of gate events based on sensor insoles against manual tagging of events by a clinician based on video footage. For that purpose participants were equipped with Moticon sensor insoles. Subsequently, participants attended a motor assessment protocol for data collection. Sensor insole data was used as input for an algorithm which was set to detect freezing of gait events. Results revealed promising detection rates of the algorithm which may serve as basis for continuous monitoring of patients in the future.

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