A Vision-Based System for Movement Analysis in Medical Applications: The Example of Parkinson Disease

Spasojević, Sofija; Santos-Victor, José; Ilić, Tihomir V.; Milanovic, S.; Potkonjak, Veljko; Rodić, Aleksandar

doi:10.1007/978-3-319-20904-3_38

Cited by 17 publications

(20 citation statements)

References 22 publications

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“…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][12][13][14][15][16][17][18][19] and mobile systems with inertial sensors [20][21][22] are used to measure the arm swing.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…This results show a promising technique for feature combination of different MRI modalities. In [23], a vision based approach is presented for Parkinson Patients movement analysis. Several comparisons of several classifiers were performed as a classification system is required to assist diagnosis and then treatment.…”

Section: Medical Applicationsmentioning

confidence: 99%

Hybrid Multistage Fuzzy Clustering System for Medical Data Classification

Abdullah

Al-Anzi

Al-Sharhan

2018

EasyChair Preprints

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Abstract-Due to the rapid development in technology nowadays, massive amount of data are available. In medicine, decision making is entirely based on the hidden information in these massive data. For that reason, data mining and machine learning technologies provide powerful tools for knowledge discovery within data. Two main techniques are used interchangeably: clustering and classification. In machine learning, clustering is an unsupervised learning technique while classification is a supervised learning method. These techniques are capable of extracting useful patterns and information which aid the process of data analysis and clinical decisions. This research presents a recent study of these techniques in the medical field during the past five years. Moreover, this paper proposes a hybrid multistage fuzzy clustering system applied to medical data classification. In the proposed system, two fuzzy clustering algorithms specifically FCM and GK were initially employed to obtain the membership values. These weights are then used in the second stage of the system as additional informative features to improve the classification process completed by SVM algorithm. Wisconsin Breast Cancer dataset, real-world application, obtained from UCI were used in the experiments. The results of the experiments show that the additional weights further improve the classification accuracy with 99.06% and 100% sensitivity.

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“…Rocha et al [75] evaluate the discriminant power of several gait parameters extracted from Kinect data, for distinguishing between PD patients treated with deep brain stimulation and control subjects. In [86], a Kinect-based approach for analyzing the movements of PD patients during rehabilitation treatment is presented, as a preliminary step towards a system suitable for home usage. Gait analysis consists simply in the estimation of gait speed and hand rigidity while subjects are walking from 3.5 to 1.5 m away from the Kinect.Ťupa et al [90] also delve into the use of Kinect for describing walking parameters and recognizing gait disorders in PD patients.…”

Section: Related Workmentioning

confidence: 99%

“…This makes gait a reliable indicator to detect symptoms of worsening health caused by aging [114], physical malfunction [27], or neurodegenerative disorders. Some examples of these last ailments are Parkinson's disease [75,86,105], multiple sclerosis [55] and strokes [92]. In this regard, neurologists handle a number of diagnostic tests for assessing and manually scoring gait disorders, such as the Unified Parkinson's Disease Rating Scale (UPDRS) [22] or the Rating Scale for Gait Evaluation (RSGE) [59].…”

Section: Introductionmentioning

confidence: 99%

“…However, the last decade has witnessed a growing interest in clinical applications of gait assessment such as rehabilitation [24], medical diagnosis [75], and detection of medical emergencies in hospital environments [73]. These results are supported by different sensors for extracting gait data, including wearable gadgets (e.g., gyroscopes, accelerometers) [9,79] and vision-based devices (e.g., Microsoft Kinect, RGB cameras) [73,75,86,114]. Sensors in the first group acquire accurate information, although they can be deemed intrusive since they are usually attached to joints, thus causing discomfort to patients.…”

Section: Introductionmentioning

confidence: 99%

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Análisis automático del patrón de la marcha mediante visión por computador: problemas, métodos y aplicaciones

Ortells¹

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A Vision-Based System for Movement Analysis in Medical Applications: The Example of Parkinson Disease

Cited by 17 publications

References 22 publications

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

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

Hybrid Multistage Fuzzy Clustering System for Medical Data Classification

Análisis automático del patrón de la marcha mediante visión por computador: problemas, métodos y aplicaciones

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