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Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

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Juan Delgado Terán (University of Twente)
Laurens Kirkels (Radbout University)
Tjitske Heida (University of Twente)
Richard van Wezel (University of Twente)

Abstract:
Freezing of gait (FoG) has been defined as a brief episode where the subject cannot move forward despite the intention to walk. FoG often leads to balance impairments and constitutes a frequent cause of falling in patients with Parkinson’s Disease (PD). FoG frequently coexistence with non-motor symptoms such as depression, anxiety, and Fear of Falling (FoF), which are the strongest predictors of low Quality of Life. Assessment of most PD symptoms is currently performed via questionnaires, and this might lead to biases. However, most people with PD are unable to correctly identify FoG, leading to underdiagnosis. Accurate and continuous evaluation of FoG in home environments using wearable sensor data is critical to diagnose and adequately managing FoG. However, most systems have failed to deliver the same performance in home environments because algorithms created in the laboratory do not address the large variability of human behavior seen in daily life (free‐living conditions). Our main goal is the detection of FoG and FoF using Machine Learning algorithms including physiological sensors (PPG (e.g., heart rate variability), EEG, and skin conductivity) and wearable movement sensors (pressure-measuring insoles and inertial measurement units (IMUs) under semi‐controlled, semi‐free living, and free‐living conditions. The configuration of the sensors placed on the person with PD is essential to predict FoG and FoF. Moreover, the obtained dataset will be used to train novel algorithms to investigate the influence of different types, numbers, and locations of sensors on the performance of FoG and FoF detection. Moreover, with the optimized measurement configuration and machine learning algorithms, we aim to detect and even predict FoG episodes in semi-controlled and free-living conditions.