Fraunhofer Center for Assistive Information and Communication Solutions – AICOSDescription:
Freezing of Gait (FOG) is a common motor symptom in Parkinson’s Disease (PD). It was defined by Nieuwboer and Giladi as “an episodic inability (lasting seconds) to generate effective stepping in the absence of any known cause other than Parkinsonism or high-level gait disorders”. FOG is typically associated with advanced stages of the disease and frequently leads to the occurrence of falls and injuries. It can occur during the initiation of the first step, or during walking (e.g. walking through narrow spaces, reaching destinations or passing through doorways). The episodes are typically brief (one to two seconds), but can also last more seconds. Due to the daily, unpredictable and frequent occurrence of FOG events, they can drastically degrade the quality of life in patients with advanced PD.
A common strategy used to support functional gait is the use of Rhythmic Auditory Stimulation (RAS). However, the effect of RAS may wear off over time so permanent stimulation, in addition to not being comfortable during daily life conditions, is not efficient and therefore not advised. The detection of FOG episodes in real time allows assistive/cueing devices to actuate at the right time, but a minimum latency is required in the detection to support this functionality. While RAS upon detection helps to shorten the duration of FOG episodes, it cannot avoid them due to the latency of the detection. Some studies suggested that a pre-FOG transition period can typically be observed, which opens new research questions related to pre-FOG detection and pre-emptive RAS delivery.
This project aims at the development of a system capable of detecting FOG episodes with a low latency, using inertial sensors placed at distinct positions, including the ear. It also aims at exploring the detection of pre-FOG periods with a view to developing RAS preventive strategies.
Outcome:
This project focus on the development of new mechanisms to detect and predict FOG episodes in Parkinson’s Disease using inertial sensor data. Multiple sensor locations will be explored in view to comparing the performance of the algorithms when different body positions are considered. The FOG detection algorithm will need to operate in real-time, being capable of predicting an event (pre-FOG detection) or detecting it with a minimum latency. A proof-of-concept will be implemented and evaluated with real patients using the Inertial Measurement Unit (IMU) on the ear.
Author: Susana Sousa
Type: MSc thesis
Partner: Faculdade de Engenharia da Universidade do Porto
Year: 2018
Download: