Browsing by Author "Cho, Chien-Wen"

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A vision-based analysis system for gait recognition in patients with Parkinson's disease
(2009-04-01)
Cho, Chien-Wen
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Chao, Wen-Hung
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Lin, Sheng-Huang
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Chen, You-Yin
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電控工程研究所
Recognition of specific Parkinsonian gait patterns is helpful in the diagnosis of Parkinson's disease (PD). However, there are few computer-aided methods to identify the specific gait patterns of PD. We propose a vision-based diagnostic system to aid in recognition of the gait patterns of Parkinson's disease. The proposed system utilizes an algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA). This scheme not only addresses the high data dimensionality problem during image processing but also distinguishes different gait categories simultaneously. The feasibility of the proposed system for the recognition of PD gait was tested by using gait videos of PD and normal subjects. The efficiency of feature extraction using PCA and LDA coefficients are also compared. Experimental results showed that LDA had a recognition rate for Parkinsonian gait of 95.49%, which is higher than the conventional PCA feature extraction method. The proposed system is a promising aid in identifying the gait of Parkinson's disease patients and can discriminate the gait patterns of PD patients and normal people with a very high classification rate. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.
A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
(2012-01-01)
Chen, You-Yin
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Cho, Chien-Wen
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Lin, Sheng-Huang
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Lai, Hsin-Yi
;
Lo, Yu-Chun
Parkinson's Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson's Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality. (C) 2011 Elsevier Ltd. All rights reserved.
Design and fabrication of a polyimide-based microelectrode array: Application in neural recording and repeatable electrolytic lesion in rat brain
(2009-08-30)
Chen, You-Yin
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Lai, Hsin-Yi
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Lin, Sheng-Huang
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Cho, Chien-Wen
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Chao, Wen-Hung
The design and testing of a new microelectrode array, the NCTU (National Chiao Tung University) probe, was presented. Evaluation results showed it has good biocompatibility, high signal-to-noise ratio (SNR: the root mean square of background noise to the average peak-to-peak amplitude of spikes) during chronic neural recordings, and high reusability for electrolytic lesions. The probe was a flexible, polyimide-based microelectrode array with a long shaft (14.9 mm in length) and 16 electrodes (5 mu m-thick and 16 mu m in radius); its performance in chronic in vivo recordings was examined in rodents. To improve the precision of implantation, a metallic, impact-resistant layer was sandwiched between the polyimide layers to strengthen the probe. The three-dimensional (3D) structure of electrodes fabricated by electroplating produced rough textures that increased the effective surface area. The in vitro impedance of electrodes on the NCTU probe was 2.4 +/- 0.52 M Omega at 1 kHz. In addition, post-surgical neural recordings of implanted NCTU probes were conducted for up to 40 days in awake, normally behaving rats. The electrodes on the NCTU probe functioned well and had a high SNR (range: 4-5) with reliable in vivo impedance (<0.7 M Omega). The electrodes were also robust enough to functionally record events, even after the anodal current (30 mu A, 10 s) was repeatedly applied for 60 times. With good biocompatibility, high and stable SNR for chronic recording, and high tolerance for electrolytic lesion, the NCTU probe would serve as a useful device in future neuroscience research. (C) 2009 Elsevier B.V. All rights reserved.
Fuzzy Rule Inference Based Human Activity Recognition
(2009)
Chang, Jyh-Yeong
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Shyu, Jia-Jye
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Cho, Chien-Wen
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電控工程研究所
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Institute of Electrical and Control Engineering
Human activity recognition plays an essential role in e-health applications, such as automatic nursing home systems, human-machine interface, home care system, and smart home applications. Many of human activity recognition systems only used the posture of an image frame to classify an activity. But transitional relationships of postures embedded in the temporal sequence are important information for human activity recognition. In this paper, we combine temple posture matching and fuzzy rule reasoning to recognize an action. Firstly, a fore-ground subject is extracted and converted to a binary image by a statistical background model based on frame ratio, which is robust to illumination changes. For better efficiency and separability, the binary image is then trans-formed to a new space by eigenspace and canonical space transformation, and recognition is done in canonical space. A three image frame sequence, 5:1 down sampling from the video, is converted to a posture sequence by template matching. The posture sequence is classified to an action by fuzzy rules inference. Fuzzy rule approach can not only combine temporal sequence information for recognition but also be tolerant to variation of action done by different people. In our experiment, the proposed activity recognition method has demonstrated higher recognition accuracy of 91.8% than the HMM approach by about 5.4 %.
Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree
(2008-11-15)
Chao, Wen-Hung
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Chen, You-Yin
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Cho, Chien-Wen
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Lin, Sheng-Huang
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Shih, Yen-Yu I.
The purpose of this study was to improve the accuracy rate of brain tissue classification in magnetic resonance (MR) imaging using a boosted decision tree segmentation algorithm. Herein. we examined simulated phantom MR (SPMR) images, simulated brain MR (SBMR) images, and a real data. The accuracy rate and k index when classifying brain tissues as gray matter (GM), white matter (WM), or cerebral-spinal fluid (CSF) were better when using the boosted decision tree algorithm combined with a fuzzy threshold than when using a statistical region-growing (SRG) algorithm [Wolf 1, Vetter M, Wegner 1, Bottger T, Nolden M, Schobinger M, et al. The medical imaging interaction toolkit. Med Imag Anal 2005;9:594-604] and an adaptive segmentation (AS) algorithm [Wells WM, Crimson WEL, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Med Imag 1996; 15:429-42]. The segmentation performance when using this algorithm on real data from brain MR images was also better than those of SRG and AS algorithm. Segmentation of a real data using the boosted decision tree produced particularly clear brain MR imaging and permitted more accurate brain tissue segmentation. In conclusion, a decision tree with appropriate boost trials successfully improved the accuracy rate of MR brain tissue segmentation. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
Vision-based forward-looking traffic scene analysis scheme
(2007)
Chang, Jyh-Yeong
;
Cho, Chien-Wen
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Vision-based driver assistant systems are very promising in Intelligent Transportation System (ITS); however, algorithms capable of describing traffic scene images are still very difficult to date. This paper proposes a system which can segment forward-looking road scene image into natural elements and detect front vehicles. First, the scene analysis system deals with scene segmentation and natural object labeling of forward-looking images. By the use of fuzzy Adaptive Resonance Theory (ART) and fuzzy inference techniques, the scene analysis task is accomplished with tolerance to uncertainty, ambiguity, irregularity, and noise existing in the traffic scene images. Secondly, the proposed system can detect the front vehicles and utilize a bounding box shape to further refine the segmentation result. Compared with conventional approaches, the proposed scheme can analyze forward-looking traffic scenes and yield reliable and efficient segmentation results. The validity of the proposed scheme in car detection was verified by field-test experiments. The traffic scene segmentation and front vehicle detection are successful.