Patashov Dmitry1, Ben-Haim Ohad1, Yakobi Shelly1, Gazit Eran2, Maidan Inbal2, Mirelman Anat2, Goldstein Dmitry1, Hausdorff JM 2 1Holon Institute of Technology, Holon, Israel 2Center for the study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Israel

The purpose of this project was to develop a method to assess walking performance of 23 patients with Parkinson’s disease and 8 idiopathic fallers (age: 70±12) on a virtual reality (VR) obstacle course. Thirty one subjects participated in a 6 week treadmill training program augmented by VR aimed to decrease fall risk. The VR system consisted of an augmented reality simulation and a camera based motion capture (Kinect) which monitored the subject’s feet movements walking on the treadmill while negotiating different types of virtual obstacles. We developed unique algorithms based on visual analysis, Fourier methods, and statistical tests to help characterize the pattern of obstacle negotiation and determine the effects of training on performance. The method decreased the signal to noise ratio of the obtained data and allowed determining each walking step with high sensitivity and accuracy. Common spatio-temporal measures of gait such as step length were extracted from 3 phases of the walk: (1) preparation of obstacle negotiation, (2) stepping over the obstacle, and (3) gait recovery after the obstacle. Our findings indicate improvement in step length throughout the training. Effects of training on obstacle negotiation are in progress.

X

Y

Z

Noise Errors

Subjects undergoing training course on the VR-system (1). The recorded signals are then being processed while taking in to account the coordinate system (2).

Adaptive filter automatically calculates region of frequency which contains relevant information for each signal independently to insure high accuracy calculations (5,6). Two different low pass filters are then being used on a signal for further calculations (7).

Using custom optimization technique on the original Z-Axis signal alongside the two filtered signals we located gait cycles with a very high accuracy, from which we later extract evaluation parameters.

The algorithm locates gait cycles with a very high accuracy (8b,9b) even when the signal is extremely noisy and contains many system errors (8a ,9a).

For conclusion we only used an actual Z-Axis signal. All filtered and deformed signals were only used to accurately locate gait cycles within the signal recorded by the system. We concluded that subjects step lengths have been improving throughout the training course. The graph (10) shows results of step lengths and step durations from second session, middle session and final session of the training course.

Acknowledgments: This research was supported in part by the European Commission (FP7 project V-TIME- 278169)

Extracted gait signals at different resolutions (3,4).

Development of a method to identify walking pattern and performance in a virtual reality obstacle course