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

Content Provider	IEEE Xplore Digital Library
Author	Barth, J. Sunkel, M. Bergner, K. Schickhuber, G. Winkler, J. Klucken, J. Eskofier, B.
Copyright Year	2012
Description	Author affiliation: Dept. of Mol. Neurology, Univ. Hosp. Erlangen, Erlangen, Germany (Bergner, K.; Winkler, J.; Klucken, J.) \|\| Fac. of Electron. & Inf. Technol., Univ. of Appl. Sci. Regensburg, Regensburg, Germany (Schickhuber, G.) \|\| Dept. of Comput. Sci., Pattern Recognition Lab., Digital Sports Group, Univ. of Erlangen, Erlangen, Germany (Eskofier, B.) \|\| ASTRUM IT GmbH, Erlangen, Germany (Barth, J.; Sunkel, M.)
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.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	5122
Ending Page	5125
File Size	696012
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6347146
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Diseases Frequency measurement Footwear Extremities Accelerometers Frequency conversion
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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