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Open AccessFeature PaperArticle

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

1
Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
2
Institute of Medical Technology, Brandenburg University of Technology Cottbus, 01968 Senftenberg, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2019, 8(12), 1471; https://doi.org/10.3390/electronics8121471
Received: 29 October 2019 / Revised: 20 November 2019 / Accepted: 24 November 2019 / Published: 3 December 2019
(This article belongs to the Section Circuit and Signal Processing)
Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.
Keywords: wavelet transformation; gait analysis; inertial sensors; Parkinson’s disease; machine learning; wearable sensors wavelet transformation; gait analysis; inertial sensors; Parkinson’s disease; machine learning; wearable sensors
MDPI and ACS Style

Steinmetzer, T.; Maasch, M.; Bönninger, I.; Travieso, C.M. Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease. Electronics 2019, 8, 1471.

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