Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70136
Title: Analysis and classification of motor dysfunctions in arm swing in parkinson’s disease
Authors: Steinmetzer, Tobias
Maasch, Michele
Bönninger, Ingrid
Travieso González, Carlos Manuel 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Gait Analysis
Inertial Sensors
Machine Learning
Parkinson’S Disease
Wavelet Transformation, et al
Issue Date: 2019
Journal: Electronics (Switzerland) 
Abstract: 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.
URI: http://hdl.handle.net/10553/70136
ISSN: 2079-9292
DOI: 10.3390/electronics8121471
Source: Electronics (Switzerland), v. 8 (4), p. 1-15
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