Whittle’s Gait Analysis - formerly known as Gait Analysis: an introduction - is now in its fifth edition with a new team of authors led by David Levine and Jim Richards. Working closely with Michael Whittle, the team maintains a clear and accessible approach to basic gait. 3D gait analysis provides reliable data, however, is currently in limited use due INTRODUCTION Every limb in our body has agonist and antagonist muscles. "Whittle's Gait Analysis is a basic introduction to this topic. Download: WHITTLE'S GAIT ANALYSIS, 5E FROM BRAND: CHURCHILL LIVINGSTONE PDF.
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Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (K), or click on a page image below. Gait Analysis: An Introduction focuses on the systematic study of human walking and its contributions in the medical management of diseases affecting the. Gait analysis is the systematic study of human walking. Most of the literature in this field is highly specialized and very technical. This book, however, aims to.
Kinect was first developed by Microsoft with the motivation of its applications in the field of virtual reality. It consists of ultrasonic and inertial sensors programmed together and the data is sent to other devices. By programming Kinect, it can be used to develop 3D virtual human body . By applying supervised learning approach to automatically and accurately extract lower and upper body gait parameters, a 3D virtual skeleton is prepared.
This can be formed by a person walking towards the Kinect; it can be online monitored and then notified whether gait cycle is normal or abnormal. The motion can be visualized and stride length and stride velocity can also be obtained.
Another marker less system involves mathematical and machine learning techniques for identification of human joints . Initially, the height of a human is determined by silhouette extraction and then the position of different gait parameter like the knee, ankles, etc is determined.
General assumption that hip is situated at half of the human height, the knee is situated at three fourth of human height and ankle is situated at 90percent of human height is taken into consideration.
After obtaining the position of various gait parameters, gait cycle consisting of angles can be generated. But, this system can face problems during occlusion. The floor is covered with floor sensors; it can record where the foot has been in contact and also measures pressure of the foot imparted on the floor.
This data is sent is to other devices online. The floor sensor consists of a special type of piezo pressure resistive sensors. The patient is indented to walk on this to generate the gait cycle. Pros and Cons All the techniques used for generating gait cycle both wearable sensor systems as well non-wearable sensor system has its own advantages and disadvantages.
The inertial sensors are light weight by the also minute movements of person has a lot of noise in the results; so up to a certain extent accuracy is distorted . The ultrasonic sensors and control board provide a continuous flow of information of stride angles, but one needs to have basic information of operating ultrasonic sensors. The faceplate in textile socks sensors or shoes provide quite accurate results of the heel contact area; but that not the complete gait analysis.
The practices currently ongoing in most of the gait lab use one of the above wearable sensor techniques, as results are quite correct. These techniques are mostly used for further applications of gait analysis like human identification. This techniques mostly need advanced machine learning algorithms like support vector machines; to identify the gait cycle.
The major objective of these techniques is to identify the gait parameter while a person is walking. Techniques without sensors or markers mostly used the basic technique of silhouette extraction.
This technique requires initial background image for the subtraction. All times, this is not possible, so its applications are limited. Whereas, smart floor techniques almost works same as force plate. Segmentation of gait cycle Once a continuous gait cycle is obtained, it needs to be segmented as per required use. The human walking consists of many phases of stance and swing phase. Selection of a single stance and swing phase constitutes a single complete gait cycle.
Stride angles and stride velocity needs to be calculated of a particular segmented single gait cycle. A robust algorithm for gait cycle segmentation is to find out highest and lowest value of the parameter considering it has initial phase and final phase.
Peaks in the cycle are detected and an average number of gait cycle is calculated .
Based on average gait cycles and segmented peak values, gait cycle as per required threshold value is selected. Segmentation of cycle from extracted silhouette is done by using principal component analysis . A trained database consisting of minimum distance classifier is been developed. The generated sequence of the silhouette is inputted to the principal component analysis database and the minimum distance classifier then segments the gait cycle.
The classifier is developed using previously inputted distance samples. Furthermore, different gait parameter can be extracted from silhouette using centroid of the silhouette. The frame is divided into various parts based on the position of the centroid.
Analysis can be done of the required gait parameter part. After noise cancellation in silhouette Hu-moments algorithm for distorted shapes can identify features in the frame. The considered features are centroid of the silhouette and two leg components.
Based on this feature, the sequence is classified in different human activities like running, walking, jumping, etc. Stride length and stride velocity can also be recognized by radio waves . High- end wireless sensors which can receive different frequency of radio waves are used in this technique. This technique is still theoretical and needs to practically implement. Commercial computer software like Kinovea and Dartfish. This commercial software also has some limitations of background and color constraints.
Applications of gait cycle are seen in the field of biomedical disorders identifications and human identifications. Gait can be used as an important parameter for security techniques used for human identification. Gait cycle can overcome disadvantages of voice recognition, face recognition, iris recognition and signature recognition . The existing system has many disadvantages like noise due to poor quality; gait analysis can overcome them.
The basic setup for gait analysis  Fig. Once a gait cycle of a person is recorded and stored, it can be further used to identify the person.
The identification of human can be done using deep neural network . All recorded gait of different humans is stored in a database. A training neural network is prepared from taking out a major part of all available dataset. The neural network is responsible for matching the inputted pattern with all available dataset. The testing neural network is maintained from the remaining dataset; thus making the system an supervised system.
Generally, 70percent of the available dataset is taken for training the neural network and remaining 30percent of the dataset is taken for testing the accuracy of the neural network. Instead of considering entire gait of the human for identification, one can also use only data extracted from joints like joints angle for identification . A joint extraction model is developed before generating a neural network. An also during identification stage before inputting it to the neural network; joint data is extracted.
The neural network is the memory-based neural network. In the neural network, the features can store as eigenspace matrix. This eigenspace matrix leads to a feature matrix [28, 39]. The recognition phase in neural network takes place by L2- Norm method in which the inputted sequence or features are matched with all the dataset to find the similar one.
Recognition can also be done using features of OpenCV [Open source computer vision]. Using OpenCV a stick diagram of the gait cycle can be generated and is stored in the database [29, 37]. Furthermore, using machine learning techniques like Haar classifier  or algorithm of K-nearest neighbor can be used.
Recognition by the neural network can be done using model-based machine learning algorithms like support vector machines [ 35]. The SVM or support vector machines classifies the gait by using different given parameters and matches them using to which parameters it is most similar to.
The features can be extracted also from silhouette or from stick diagrams generated from the gait cycle. By using an algorithm like SVM high accuracy in recognition up to 98percent can be achieved .
By using machine learning algorithms of principal component analysis  and SVM ; gait analysis can be used in real time video surveillance . It is already been started in some countries like Australia and USA on trail basis. Once a dataset containing features of a selected range of humans is generated; then gait analysis is the best option rather than currently being used security features.
The above-listed applications are in the field of human identification; the applications in the field of biomedical disorders are by analyzing the gait parameter according to the disorder. Assessing the manner of walking can be done by various techniques.
The existing techniques follow the flow of capturing the video, extracting silhouette, performing noise elimination, drawing stick diagram if required , and analyzing the various gait parameters required for the particular application. The techniques used generally fall into two categories i. The wearable sensors techniques need the person to attach a sensor or marker on his body. The sensors like inertial, ultrasonic, accelerometer can transmit the generated data to other devices by means of networking.
If markers are used on the human body, then image processing or video processing is the way of obtaining gait cycle. The markers used can be active as well as passive markers. Inertial sensor and accelerometer can also be considered as active markers.
The techniques which do not involve any kind of marker system; rely on machine learning algorithms. Mostly all techniques, extract silhouette by removing previously stored background image. This sequence of silhouette can be used to analyze the gait parameter like limbs, knees, ankles, etc from the position of centroid.
Some techniques determine the height of the silhouette and then determine the positions of knees, ankles, and hip. Using faceplate consisting of pressure resistive sensors are been used in socks, shoes, and floors to obtain exact information of the heel contact pressure areas. Segmentation of the gait cycle needs to be done before analyzing it.
For segmentation, the peak value and average gait cycles are taken into consideration. Some approaches for gait cycle also involves the use of radio waves for gait cycle formation. Other than orthopedic disorders analysis; gait cycle finds large number of upcoming applications in the field of human identification. By storing the gait cycle in database; it can be used any other time to recognize the person.
Gait is proved to be an better security identification feature than the existing techniques currently been used. Although, a lot of research has been done in this field, but then too most of the techniques require high cost for its implementation. Inertial and ultrasonic sensors gives information other than visual output; so interpretation of data is must. In case of marker techniques various cameras and a large setup is used for generated a 3D view of human body.
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