Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease

Barth J, Sünkel M, Bergner K, Schickhuber G, Winkler J, Klucken J, Eskofier B (2012)

Publication Status: Accepted

Publication Type: Conference contribution, Conference Contribution

Publication year: 2012

Original Authors: Barth Jens, Sünkel Michael, Bergner Katharina, Schickhuber Gerald, Winkler Jürgen, Klucken Jochen, Eskofier Björn

Pages Range: 5122-5125

Conference Proceedings Title: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE

Event location: San Diego, CA

DOI: 10.1109/EMBC.2012.6347146

Abstract

Objective and rater independent analysis of movement impairment is one of the most challenging tasks in medical engineering. Especially assessment of motor symptoms defines the clinical diagnosis in Parkinson's disease (PD). A sensor-based system to measure the movement of the upper and lower extremities would therefore complement the clinical evaluation of PD. In this study two different sensor-based systems were combined to assess movement of 18 PD patients and 17 healthy controls. First, hand motor function was evaluated using a sensor pen with integrated accelerometers and pressure sensors, and second, gait function was assessed using a sports shoe with attached inertial sensors (gyroscopes, accelerometers). Subjects performed standardized tests for both extremities. Features were calculated from sensor signals to differentiate between patients and controls. For the latter, pattern recognition methods were used and the performance of four classifiers was compared. In a first step classification was done for every single system and in a second step for combined features of both systems. Combination of both motor task assessments substantially improved classification rates to 97% using the AdaBoost classifier for the experiment patients vs. controls. The combination of two different analysis systems led to enhanced, more stable, objective, and rater independent recognition of motor impairment. The method can be used as a complementary diagnostic tool for movement disorders.

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Involved external institutions

Ostbayerische Technische Hochschule Regensburg (OTH) Germany (DE)

How to cite

APA:

Barth, J., Sünkel, M., Bergner, K., Schickhuber, G., Winkler, J., Klucken, J., & Eskofier, B. (2012). Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 5122-5125). San Diego, CA, US.

MLA:

Barth, Jens, et al. "Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease." Proceedings of the 2012 Annual International Conference of the IEEE, San Diego, CA 2012. 5122-5125.

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