1. 1 Extracting Spatiotemporal Gait Properties from Parkinson's Disease Patients Albert Sama Andreu Català Cecilio Angulo Alejandro Rodríguez-Molinero

2. 2 Motivation Parkinson’s disease patients suffer motor complications Gait of patients is strongly affected

3. 3 Motivation Daily life monitoring of gait parameters may help to identify motor alterations in order to Obtain a better diagnose Adjust the medication optimally Wearable inertial sensors can estimate spatio- temporal parameters of gait

4. 4 Outline 1. Related work 2. Gait analysis 2.1 Sensor system location 2.2 Experiments 2.2 Problem formulation 3. Results 4. Conclusions

5. 5 1. Some related work Estimation of spatio-temporal parameters of gait  Using four uni-axial gyroscopes and a double pendulum model [Salarian, 2004]  Using a tri-axial accelerometer on the lower trunk [Zijlstra, 2003]  Also estimating center of mass, using a tri-axial accelerometer on the lower trunk [Miechtry, 2005]

6. 6 2. Gait analysis Sensor system location Sensor system position  Near L3 region  Lateral side of waist

7. 7 2. Gait analysis Experiments 5 PD patients walked several times over a plain surface of 6 m. Recorded by a video camera and tri-axial accelerometer recorded measures Step length and velocity obtained by measuring footprints or visual markers

8. 8 2. Gait analysis Problem formulation First: automatic detection of steps [Zijlstra, 2003]  Toe off event  Initial contact event T.O.I.C.T.O.I.C.T.O.I.C.

9. 9 2. Gait analysis Problem formulation Estimate i th step length l i and velocity v i by means of its accelerations: Ideally, find the maps f(·) and g(·) such that:

10. 10 2. Gait analysis Problem formulation Three main properties are chosen: Maps are:

11. 11 3. Results ε-SVR 3-degree polynomial kernel Step length results RegressionMean RMSE (cm/s) Linear regression17.36 ε-SVR14.64 Step velocity results RegressionMean RMSE (cm) Linear regression24.61 ε-SVR18.46

12. 12 4. Conclusions and future work New location for on ambulatory gait analysis has been evaluated Due to the machine learning approach the sensor position is flexible Few features necessarily independent of gravity are used Good performance on estimating spatiotemporal measures of gait Real-time implementation Evaluate whether center of mass is possible to estimate

13. Thank you for your attention 13 In memoriam of Antoni Anglès 1953-2009