Next Article in Journal
Fluid Intake Monitoring System Using a Wearable Inertial Sensor for Fluid Intake Management
Next Article in Special Issue
Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images
Previous Article in Journal
FarpScusn: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
Open AccessArticle

Imaging Tremor Quantification for Neurological Disease Diagnosis

1
Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
2
Department of Neurology, Faculty of Medicine, University of Miyazaki, Miyazaki 889-2192, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6684; https://doi.org/10.3390/s20226684
Received: 16 October 2020 / Revised: 11 November 2020 / Accepted: 20 November 2020 / Published: 22 November 2020
In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders. View Full-Text
Keywords: tremor; essential tremor; ataxia; finger–nose–finger test tremor; essential tremor; ataxia; finger–nose–finger test
Show Figures

Figure 1

MDPI and ACS Style

Mitsui, Y.; Zin, T.T.; Ishii, N.; Mochizuki, H. Imaging Tremor Quantification for Neurological Disease Diagnosis. Sensors 2020, 20, 6684. https://doi.org/10.3390/s20226684

AMA Style

Mitsui Y, Zin TT, Ishii N, Mochizuki H. Imaging Tremor Quantification for Neurological Disease Diagnosis. Sensors. 2020; 20(22):6684. https://doi.org/10.3390/s20226684

Chicago/Turabian Style

Mitsui, Yuichi; Zin, Thi T.; Ishii, Nobuyuki; Mochizuki, Hitoshi. 2020. "Imaging Tremor Quantification for Neurological Disease Diagnosis" Sensors 20, no. 22: 6684. https://doi.org/10.3390/s20226684

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop