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.
Highlights
The life expectancy of humankind is increasing worldwide
Life expectancy is projected to increase in the 35 industrialised countries with a probability of at least 65% for women and 85% for men
It was not possible to create a classifier that could classify the subjects with motor dysfunctions (MD) and no MD by using the Euler angles, because the Euler angles were measured in absolute values
Summary
The life expectancy of humankind is increasing worldwide. Life expectancy is projected to increase in the 35 industrialised countries with a probability of at least 65% for women and 85% for men. In [11], the arm swings of Parkinson’s patients and healthy persons with the help of a Kinect camera were compared. Using the motion capture system Motek CAREN in [13], it was detected that Parkinson’s patients have a different jerk and arm swing length compared to healthy people. To detect the differences in speed, amplitude, and symmetry in arm movement between healthy people and people in the early stages of Parkinson’s disease. (g) classifies between subjects with motor dysfunctions and a control group based exclusively on arm motions uses 3D data from the accelerometer, gyroscope, and magnetometer includes new parameters is small and easy to use is not bound to a location requires a small number of sensors is low cost.
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