Evaluation of a Navigation Experiment in the Gait Real-Time Analysis Interactive Lab: Gait Analysis by Induced Disorientation in a VR Environment

Challenges in wayfinding and orientation are early symptoms of MCI and dementia. These deficits decrease mobility which again leads to further cognitive decline. In a field study, we developed a pattern recognition model of disorientated behaviour based on accelerometric data. However, it is questionable if phases of disorientation also affect gait parameters. Furthermore, there is growing evidence that impaired cognitive functioning is associated with changes in gait performance, e.g. gait variability, measured in dual-task walking conditions. Increases in heart rate and skin conductance have also been reported during instances of disorientation.

Hence, We implemented a 3D environment of a familiar city centre in the GRAIL, which combines a fully instrumented treadmill with a synchronized VR environment. We record gait parameters through the motion capture system, and accelerometric and physiological data using wearable sensors (movisens), for comparability with the SiNDeM field study. Young and old healthy adults will participate in the first phase of the study, while Mild dementia or MCI patients will participate in the later phases. Phases of disorientation will be induced by changing the virtual environment.We aim to assess gait, accelerometric and physiological parameters during instances of disorientation, using the GRAIL (Gait Real-Time Analysis Interactive Lab, Motekforce Link).

The results will further enable the automatic detection of disorientation based on gait parameters, physiological and accelerometric data. This is necessary for the development of a situation-aware assistive system which supports persons with dementia in autonomous outdoor mobility.