Conference paper

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Conference paper

  1. 1. HUMAN GAIT AND POSTURE ANALYSIS FOR DIAGNOSING NEUROLOGICAL DISORDERS H. Lee1, L. Guan1, J. A. Burne2 1 School of Electrical and Information Engineering, 2 Department of Biomedical Science University of Sydney hlee, ling@ee.usyd.edu.au j.burne@cchs.usyd.edu.au ABSTRACT particular, we propose three techniques to enhance the performance of the system. First, in data acquisition, a This paper describes a number of new techniques to tracksuit is introduced to replace the uncomfortable tight enhance the performance of a video analysis system, free outfit used before, in an attempt to provide a more realistic from motion markers and complicated setup procedures, diagnostic environment. Second, this work investigates for the purpose of quantitatively identifying gait colour image processing techniques. Since humans see the abnormalities in static human posture analysis. Visual world in the colour spectrum, processing colour images features are determined from still frame images out of the should provide additional information not available in the entire walking sequence. The features are used as a guide grayscale domain. Finally, a general regression neural to train a neural network, in an attempt to providing network (GRNN) [3] is applied to implement the well assistance to clinicians in diagnosing patients with known sequential feature evaluation methods [4] for neurological disorders. feature selection. The most important features are selected and used in decision making. The system yielded a correct Keywords: image analysis, neural networks, pattern diagnosing rate of up to 85% using the test data. The classification, Parkinson’s disease, gait analysis. result suggests that the techniques have the potential to assist the neurologists in providing a second opinion in diagnosing neurological disorder. 1. INTRODUCTION 2. SYSTEM OVERIVEW Current diagnosis of many neurological disorders, such as The video image analysis system consists of laboratory Parkinson’s disease (PD), involves human observation of setup, a video/image capturing device and a software the posture and movement of the gait. These observations processing tool. tend to be subjective and depend greatly on the experience and judgement of the clinician, which can vary from person to person. Even though, there are image processing 2.1. Laboratory setup systems that attempt to automate this process, they usually require a highly constructed laboratory environment, The human subjects are required to wear a specifically which may not be suitable for some patients. designed outfit, that has different colours for different Disorders that affect movement and posture parts of the body. Originally, a tight bodysuit was used in include stroke and head injuries. All of them have data collection [2] as shown in Figure 1(a). This approach different visual symptoms such as muscle spasms, is not practical in real life, since even a healthy person paralysis of limbs, lopsided stance, shuffling walk and a would feel uncomfortable wearing such a tight outfit, let stooped gait. From these symptoms, an experienced alone patients with a neurological disorder. To alleviate clinician can diagnose the type and degree of severity of this problem, a tracksuit is designed to replace the the disorder [1]. bodysuit ( Figure 1(b)). This has the advantage of relaxing The aim of this work is to improve a video image the subjects during the experiment, and, therefore, more analysis system developed at the University of Sydney [2] realistic gait patterns can be observed and analysed. In to intelligently and realistically incorporate visual addition, the practical laboratory setting would be more assessment techniques used by the neurologists. In beneficial in the context of medical research.
  2. 2. This subsystem performs all the major image processing tasks necessary for analysing neurological disorder, including color image segmentation, medial axial analysis, feature extraction and selection, and decision making. Among these tasks, the new contribution lies in segmentation and feature selection. 3.1 Colour Segmentation A neural network based segmentation technique is used to segment out different parts of the body in the image. A back propagation algorithm is used to train the system to recognise the colours used. It uses the RGB values of any pixel in the image as the input to train the neural network 1(a) 1(b) to recognise different colours appearing in the image. Figure 1: Examples of the costume used in the experiment: tight Particularly, the different colours of the tracksuit bodysuit (a) and tracksuit (b) correspond to different parts of the body. Once the network is trained, segmentation can be obtained much 2.2. Image Acquisition faster for massive data processing. This is in contrast to the previous work [2] where grayscale based segmentation The overall image acquisition subsystem consists of the algorithms were used, and substantial design effort had to following hardware components shown in Figure 2: a be committed in order to obtain reasonable segmentation. video camera, a video recorder, and a frame grabber. This Further more, this technique is applicable for other colour subsystem also includes the necessary software for the combinations, which might not provide significant interface between the PC and the hardware, as well this contrast in the grayscale domain. software can perform some preliminary image processing tasks such as noise filtering, and image enhancement. Figure 2 Hardware settings 2.3. Image Analysis Figure 3 Locations of the visual features The image analysis subsystem is software based. The software performs the required processing to extract 3.1 Feature Extraction important visual information given in Figure 3, such as joint angles and swing distances of the limbs from the PD symptoms such as tremor, rigidity, stiffness of the image obtained in the previous stage. The contribution of limbs and lack of co-ordination, must show some this paper to the image processing subsystem is detailed in significant differences in the features of the gait, such as the following section. joint angles, swing distances, and swing trajectories of the limbs, velocity of the movement, etc. From the observations of the gait in normal people and that in the 3. IMAGE ANALYSIS PD patients, it is evident that the stretchability and the
  3. 3. joint flexibility of the limbs are the key features to and passed through the kernel function. The summation distinguish the gait patterns between the two groups. The layer has two units A and B, the unit A computes the stretchability and the joint flexibility best describe the summation of exp[-Di2/(2σ2)] multiplied by Yi the rigidity and stiffness of the limbs. Apart from featuring classification result, associated with Xi. The B unit the rigidity and the stiffness, the swing directions and computes the summation of exp[-Di2/(2σ2)]. The output swing distances of two arms relative to the median axis of unit divides A by B to provide the prediction result[3]. the torso indicates the inability to execute simultaneous The advantage of using such a network over histogram movements. In this research, processing is restricted to analysis is that GRNN needs only a single pass of learning static images rather than dynamic image sequences. Based to achieve optimal performance in classification. Also it on the above reasons, the joint angles and the swing provides a non-linear approach for feature selection. distances are used as the inputs to decision making for classification. X To extract visual features, the segmented limbs Input layer must first be reduced to its skeleton representation. To obtain the skeleton of the object, thinning techniques are required. Thinning can be defined heuristically as a set of X1 X2 Xi successive erosions of the outermost layer of the shape, Hidden layer until a connected unit-width set of lines (skeleton) is obtained [5]. After thinning, ten features were extracted from the skeleton, namely: F1 Front knee joint angle A B Summation layer F2 Back knee joint angle F3 Front ankle angle F4 Back ankle angle F5 Front elbow angle A/B Output layer F6 Back elbow angle F7 Stepping distance F8 Front hand to median axis of torso distance Figure 4: GRNN architecture F9 Back hand to median axis of torso distance F10 Front hand to back hand distance These features are graphically illustrated in Figure 3. The selected features, according to their discriminatory power by SFS and SBS, are summarized in Table 1. 3.2 Feature Selection Sequential Forward Selection The importance of selecting the relevant subset from the Front hand to median axis of torso distance original features is closely related to the “curse of Front knee joint angle dimensionality” problem in function approximation, Front hand to back hand distance where sample data points become increasingly sparse Front elbow angle when the dimensionality of the function domain increases, Back elbow angle such that the finite set of samples may not be adequate for Stepping distance characterising the original mapping. In this work, a Back ankle angle general regression neural network (GRNN) [3] is applied to implement the well know sequential forward selection Sequential Backward Selection (SFS) and sequential backward selection (SBS) methods Front ankle angle [4] to select the most effective features. The GRNN Back hand to median axis of torso distance consists of four layers (Figure 4). The input layer simply Front hand to back hand distance passes the input vector variables X (the features, in this Stepping distance case) into the hidden layer. The hidden layer consists of Back knee joint angle all the training samples, X1, …, Xi. When an unknown Front elbow angle pattern X is presented, the squared distance Di2 between Back ankle angle the unknown pattern and the training sample is calculated
  4. 4. Front knee joint angle selected by the SBS, and 82.5% by the SFS features. The Table 1: Features selected by both approaches performance of the neural network using all ten features in training and testing are also tabulated in Table 2. It 3.3 Decision Making provided a 77.5% correct diagnose only, due to curse of dimensionality. The decision making part consists of a three-layered back propagation neural network. It was trained by the features Correct False Correct False selected from the previous stage, and the performance of Normal Positive Patient Negative the system was tested using new images. Separate neural Forward 12 3 21 4 networks were constructed for training SFS features and Selection SBS features. In the case of SFS features there were a Backward 13 2 21 4 total of 14 neurons; 7 input neurons corresponding to 7 Selection SFS features used, 5 hidden neurons and 2 output neurons 12 3 19 6 corresponding to healthy (1,0) and patient (0,1) groups. The second network consists of 8 input neurons, 5 hidden neurons and 2 output neurons. Further more, another network was constructed to train with all 10 features, as to compare with the classification results obtained by the feature extraction strategies described earlier. Six hidden All neurons and 2 output neurons are implemented in the third features Table 2: Classification results from static analysis 5. CONCLUSIONS In this paper, we propose several techniques to improve the performance of a video image analysis system for the diagnosis of neurological disorder. In data acquisition, tracksuit is used to replace the uncomfortable tight outfit in order to provide a more realistic diagnostic network. environment. We investigate colour image segmentation techniques. Since humanbeings see the world in the colour a) Sequential forward selection spectrum, processing colour images provided additional information not available in the greyscale domain. We b) Sequential backward selection also applies a general regression neural network in Figure 5: Feature selection using GRNN selecting the most effective features for decision making. The system yields a correct diagnosing rate of up to 85%. The result suggests that the techniques have the potential 4. EXPERIMENTAL RESULTS to assist neurologists in providing a second opinion on diagnosing neurological disorder. We implemented the proposed enhancement strategies into our video analysis system and tested the system on a database of both PD patients and healthy people. The REFERENCES patients were video taped at Sydney Westmead Hospital, and the data on healthy people was collected at the [1] F.H. McDowell, “The Diagnosis of Parkinsonism or University of Sydney. In total, 80 images (30 from the Parkinson Syndrome,” Contemporary Neurology, patients group and 50 from the normal group) were 1990. extracted from the videos. From the 80 images collected both from normal and patient data, 40 of them were used [2] R. Chang, L. Guan, J. Burne, “An automated form of to train the neural networks, and the other 40 were used to video image analysis applied to classification of test the system performance. As Table 2 shows, 85% movement disorders,” J. of Disability and correct classification was achieved by using the features
  5. 5. Rehabilitation, vol. 22, no. 1/2, pp. 97-108, January/February 2000. [3] D.F. Specht, “A general regression neural network,” IEEE Trans. Neural Networks, vol. 2, no. 6, pp. 568-576, Nov. 1991. [4] J. Kittler, “Feature set search algorithm,’’ in Pattern Recognition and Signal Processing (C.H. Chen, ed.), Sjithoff & Noordhoff, 1978. [5] R.C. Gonzalez, R.E. Woods, Digital Image Procesing, Addison-Wesley Publication Company, 1993.

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