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
for women and
for men. There is a
probability that life expectancy at birth among South Korean women in 2030 will be higher than 86.7 years, the same as the highest worldwide life expectancy in 2012, and a
probability that it will be higher than 90 years [1
]. Due to the increasing life expectancy, the number of old-age diseases is also increasing. One of them is PD. At present, there are 10 million people affected by this disease, and the trend is increasing [2
]. Parkinson’s disease is a neurodegenerative disease and is currently incurable. However, the progression of the disease can be delayed by medication. For this reason, an exact diagnosis is very important so that the medication can be adjusted as well as possible to the particular person. There are different rating scales for the uniform assessment, e.g., the Unified Parkinson’s Disease Rating Scale (UPDRS) [3
]. With the help of this rating scale, for example, cognitive and motor performance are assessed. One of the motor tests is the Timed Up and Go (TUG). The assessment is visual and therefore subjective. For this reason, many researchers are working on the objective evaluation of this test.
Most of the research on gait analysis deals with the analysis of leg motion [4
]. However, the analysis of the arm movement is also important for the assessment of a gait disorder. Stationary systems that use cameras or ultrasound [11
] and mobile systems with inertial sensors [20
] are used to measure the arm swing.
], the arm swings of Parkinson’s patients and healthy persons with the help of a Kinect camera were compared. Significant differences in amplitude and speed were observed. The arm movements of Parkinson’s patients also often showed asymmetry. The PD group showed significant reductions in arm swing magnitude (left, p
= 0.002; right, p
= 0.006) and arm swing speed (left, p
= 0.002; right, p
= 0.004) and significantly greater arm swing asymmetry (ASA) (p
< 0.001). An accuracy of more than
in distinguishing healthy people from persons with PD was also achieved using a Kinect camera in [12
]. Classification between healthy and non-healthy subjects is performed based on the five most relevant features and the two new obtained features from LDA, using four different classifiers, support vector machine (SVM), multilayer perceptron (MLP), the radial basis (RB) neural network, and k nearest neighbor (KNN). 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. The fact that Parkinson’s patients in the early stages have a larger ASA could be confirmed in [14
] with the Vicon and the Baton Rouge motion lab system. The p
-value for distinguishing healthy individuals from individuals with Parkinson’s disease was 0.003. A Kinect system was used in [16
] 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. In [17
], it was investigated which model method provided the best results when using a Kinect to detect Parkinson’s disease stages. The best results with an accuracy of
were obtained with a special Bayesian network classifier using 10-fold cross-validation. The relevant features were related to left shin angles, left humerus angles, frontal and lateral bends, left forearm angles, and the number of steps during a spin. For the recordings in [18
], a Kinect system was used in combination with an e-Motion capture program. The proposed system classifies PD into three different stages related to freezing of gait (FoG). An accuracy of
was reached using the features of the movement and position of the left arm, the trunk position for slightly displaced walking sequences, and left shin angle for straight walking sequences. However, they obtained a better accuracy of
for a classifier that only used features extracted from slightly displaced walking steps and spin walking steps.
], an automatic method for the treatment of levodopa-induced dyskinesia (LID) was developed. Gyroscopes were used on the abdomen and chest and the abdomen, chest, wrists, and ankles. In general, an average detection rate of
for Parkinson’s patients was achieved, and the average detection rate and the precision of the individual classes (LID, Parkinson, healthy) were
, respectively. Several classification techniques have been used for LID assessment, including the naive Bayes classifier, KNN, fuzzy lattice reasoning (FLR), decision trees, random forests (RF), and neural networks using a multilayer perceptron (MLP).
The method used in [19
] consisted of guiding patients with early Parkinson’s on a treadmill and measuring their movements with an ultrasound device on each side. The results were a reduced arm swing amplitude in the patients and a longer stride length compared to healthy people.
], a sensor unit was used on each forearm. This sensor unit consisted of two triaxial G-Link accelerometers that were attached to an aluminum bar. Arm swing asymmetry (ASA), maximal cross-correlation (MXC), and instantaneous relative phase (IRP) of bilateral arm swing were compared between PD and controls. PD subjects demonstrated significantly higher ASA (p
= 0.002) and lower MXC (p
< 0.001) than controls.
An accelerometer was placed on the upper arm, as well as a magnetic angular rate and gravity (MARG) device on the shoulder in [21
]. The Denavit–Hartenberg model was used, and the algorithm was based on the pseudoinverse of the Jacobian by the acceleration of the upper arm. The accuracy of this method was demonstrated by the use of an optoelectronic system for control purposes.
A similar system was used in [22
] with nearly the same sensors and sensor position. An eigenvector method was suggested to compare the axes of the left and right hand. The results showed a difference between people with Parkinson’s disease and healthy people.
In our approach, we want to propose a medical wearable system that:
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
According to the previously mentioned classification, this paper is organized as follows. Section 2
describes our materials. The section is divided into the medical experiment protocol, the hardware used, and the dataset. Then, in Section 3
, a description of our methods and how we apply the methods to our data are described. Section 4
include the results. Finally, a discussion and comparison is found in Section 5
In Table 3
and Table 5
, the x
-axis always shows the best results. The x
-axis corresponds to the movement in the sagittal plane. According to the literature, the most important characteristics of human gait are also present in this plane [31
]. For this reason, it is a logical conclusion that the features with the highest significance are present on this axis.
We presented our results in the previous section. We compared the results when the complete TUG test, Parts (A) and (B), was used for the classification, as well as if we only used the gait, Part (B), for the classification. The results showed that for the classification of motor dysfunctions, the gait alone gave quite good results with an accuracy of , but when looking at the complete test, we obtained even better results with an accuracy of . From this, we concluded that the complete TUG test was necessary for the analysis of motor dysfunctions.
Furthermore, we classified each signal separately. During the classification, we found out that the x-axis of the Euler angle and linear acceleration gave the best results, independent of whether Parts (A) and (B), as well as only Part (B) were used for the classification. From this, we concluded that the x-axis was the most relevant.
The conclusion was that we obtained better results through the combination of the signals compared to single signals. In the classification of Parts (A) and (B), the three-channel CNN proved to be the best solution. When classifying with only Part (B), voting was the best choice.
shows our classification results compared to the corresponding state-of-the-art works. Our results were comparable to the results from large, expensive, and stationary video based systems.
Our system delivered better results than the wearable system that also classified the data [15
]. We could not make a comparison with the other works because they focused on a statistical evaluation of the data. CNN in combination with wavelet transformations was a powerful technique for arm swing analysis.