Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

Özel, Erdoğan; Tekin, Ramazan; Kaya, Yılmaz

doi:10.18280/ts.380306

Cited by 8 publications

(4 citation statements)

References 31 publications

(42 reference statements)

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“…The five abnormal gaits were mainly determined by different types of gait behavioral disorders. In our experiment, we used five recognition methods, i.e., Gradient Boosting (GB), KNeighbors (KN), Multilayer Perception (MLP), Random Forest (RF), and SVM, to classify the six gait features [28][29][30][31][32][33]. The parameter settings of the machine learning model obtained by GridSearch are shown in Table 3.…”

Section: Discussionmentioning

confidence: 99%

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

Zhao

Wang

et al. 2022

Sensors

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The recognition of abnormal gait behavior is important in the field of motion assessment and disease diagnosis. Currently, abnormal gait behavior is primarily recognized by pressure and inertial data obtained from wearable sensors. However, the data drift and wearing difficulties for patients have impeded the application of these wearable sensors. Here, we propose a contactless abnormal gait behavior recognition method that captures human pose data using a monocular camera. A lightweight OpenPose (OP) model is generated with Depthwise Separable Convolution to recognize joint points and extract their coordinates during walking in real time. For the walking data errors extracted in the 2D plane, a 3D reconstruction is performed on the walking data, and a total of 11 types of abnormal gait features are extracted by the OP model. Finally, the XGBoost algorithm is used for feature screening. The final experimental results show that the Random Forest (RF) algorithm in combination with 3D features delivers the highest precision (92.13%) for abnormal gait behavior recognition. The proposed scheme overcomes the data drift of inertial sensors and sensor wearing challenges in the elderly while reducing the hardware requirements for model deployment. With excellent real-time and contactless capabilities, the scheme is expected to enjoy a wide range of applications in the field of abnormal gait measurement.

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Section: Discussionmentioning

confidence: 99%

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

Zhao

Wang

et al. 2022

Sensors

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“…The shifted 1D-LBP was applied to each of the 18 vertical ground reaction force (VGRF) signals to construct 18 1D-LBP mode histograms and achieved an accuracy of 88.88% in PD diagnosis. Özel et al [30] first applied the Weighted Common Average Reference (WCAR) to reduce noise in the output signals. Then, the statistical features were extracted from multi-sensor signals using the Local Binary Pattern (LBP) conversion, and the PD diagnosis accuracy of 92.96% was achieved using the K Nearest Neighbors (KNN) method.…”

Section: A Existing Gait Detection Algorithmsmentioning

confidence: 99%

Static-Dynamic Temporal Networks for Parkinson’s Disease Detection and Severity Prediction

Dong

Chen

Huan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Most patients with Parkinson's disease (PD) have different degrees of movement disorders, and effective gait analysis has a huge potential for uncovering hidden gait patterns to achieve the diagnosis of patients with PD. In this paper, the Static-Dynamic temporal networks are proposed for gait analysis. Our model involves a Static temporal pathway and a Dynamic temporal pathway. In the Static temporal pathway, the time series information of each sensor is processed independently with a parallel one-dimension convolutional neural network (1D-Convnet) to extract respective depth features. In the Dynamic temporal pathway, the stitched surface of the feet is deemed to be an irregular "image", and the transfer of the force points at all levels on the sole is regarded as the "optical flow." Then, the motion information of the force points at all levels is extracted by 16 parallel two-dimension convolutional neural network (2D-Convnet) independently. The results show that the Static-Dynamic temporal networks achieved better performance in gait detection of PD patients than other previous methods. Among them, the accuracy of PD diagnosis reached 96.7%, and the accuracy of severity prediction of PD reached 92.3%.

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“…This decision boundary, also called the hyperplane, is orientated in such a way that it is as far away as possible from the closest data points (support vectors) from each class [28]. The RF classifier utilizes the bootstrap resampling method to extract multiple samples from the original samples, constructs a decisionmaking tree for each bootstrap sample, and finally combines all the decision-making trees to obtain the final classification result [29,30].…”

Section: Constructing Of Classifiersmentioning

confidence: 99%

Robust Identification of Subtypes in Non-Small Cell Lung Cancer Using Radiomics

Wang¹,

Yushkov²,

Li³

et al. 2022

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Identifying the histological phenotype of non-small cell lung cancer (NSCLC) is of crucial importance to its treatment and prognosis. The radiomics-based prediction model has the potential to non-invasively extract the tumor phenotype characteristics. However, the existing research ignores the stability of extracted features, which restricts the performance and robustness of the constructed model. While most of themethods in the literature use classification accuracy to solve theproblemofradiomics featuresstability, in this paper we propose the use ofSOM (Self-organizing Mapping) and K-means to evaluate the stability of different feature subsets. The subset with good clustering performance is selected as the optimal feature subset.When the optimal feature subset is used for modeling, compared with other feature subsets, the higher AUC(Area Under Curve) and lower SD(Standard Deviation) on the three classifiers show that the feature subset had excellent classification performance and good stability, and can distinguish NSCLC subtypes more accurately and robustly.

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Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

Cited by 8 publications

References 31 publications

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

Static-Dynamic Temporal Networks for Parkinson’s Disease Detection and Severity Prediction

Robust Identification of Subtypes in Non-Small Cell Lung Cancer Using Radiomics

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