CALIMOTION

A CLOSED-LOOP SYSTEM FOR REAL-TIME CALIBRATION OF NEURAL STIMULATION PARAMETERS USING MOTION DATA

Summary

Continuous Deep Brain Stimulation (DBS) has been one of the most effective treatments for advanced Parkinson’s disease (PD). Until today DBS parameters were adjusted by an expert every few months and were otherwise kept constant. Recent studies suggested that adaptive DBS with parameters being continuously weighted according to PD patients’ needs, is superior due to a better motor outcome and less side effects. CaliMotion aims to develop rules for DBS parameter adjustments related to the current motor state based on motion capture data. As a first step, we developed objective performance measures to characterize and track the movement quality of a patient across numerous motor tasks. These measures serve to map modifications of the parameter settings. In a second step, these mappings will be used to optimize DBS. In a third step, we aim to use EEG signals as a marker for movement intention to explore the complex relationship between PD patients’ desired vs. actual movements.

Research Status

Motion capture data analysis was data-driven using Random Forests, enhanced by Probability Distributions based on different metrics, such as joint contributions and movement smoothness measures. This allows us to objectively rate the quality of motion e.g. in walking, turning, standing up, fine motor skills etc. We demonstrated that PD patients’ high dimensional motor abnormalities closely correlate to each other and, therefore, allow for continuous performance tracking which, together with EEG-derived intention tracking, will be used to optimize DBS settings.

Project Publications

Burget F, Maurer C, Burgard W, Bennewitz M (2015) Learning Motor Control Parameters for Motion Strategy Analysis of Parkinson’s Disease Patients. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5019-25.

Andreas Kuhner, Tobias Schubert, Massimo Cenciarini, Christoph Maurer, and Wolfram Burgard. A probabilistic approach based on random forests to estimating similarity of human motion in the context of parkinson’s disease. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

Andreas Kuhner, Tobias Schubert, Christoph Maurer, and Wolfram Burgard. An online system for tracking the performance of parkinson’s patients. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

Andreas Kuhner, Tobias Schubert, Massimo Cenciarini, Isabella Katharina Wiesmeier, Volker Arnd Coenen, Wolfram Burgard, Cornelius Weiller, and Christoph Maurer. Correlations between motor symptoms across different motor tasks, quantified via random forest feature classification in parkinson’s disease. Frontiers in Neurology, 8:607, 2017, doi: 10.3389/fneur.2017.00607

Imke Jansen, Alexandra Philipsen, Daniela Dalin, Isabella Wiesmeier, Christoph Maurer. Postural instability in adult ADHD – a pilot study. Gait & Posture October 2018, DOI: 10.1016/j.gaitpost.2018.10.016

Stefan Kammermeier, Kathrin Maierbeck, Lucia Dietrich, Annika Plate, Stefan Lorenzl, Arun Singh, Kai Bötzel, Christoph Maurer. Qualitative postural control differences in Idiopathic Parkinson’s Disease vs. Progressive Supranuclear Palsy with dynamic-on-static platform tilt. March 2018, Clinical Neurophysiology 129(6). DOI: 10.1016/j.clinph.2018.03.002

Daniela Buettner, Daniela Dalin, Isabella K. Wiesmeier, Christoph Maurer. Virtual Balancing for Studying and Training Postural Control. September 2017. Frontiers in Neuroscience 11:531. DOI: 10.3389/fnins.2017.00531

Isabella K. Wiesmeier, Daniela Dalin, Anja Wehrle, Urs Granacher, Thomas Muehlbauer, Joerg Dietterle, Cornelius Weiller, Albert Gollhofer, Christoph Maurer. Balance Training Enhances Vestibular Function and Reduces Overactive Proprioceptive Feedback in Elderly. August 2017. Frontiers in Aging Neuroscience 9. DOI: 10.3389/fnagi.2017.00273