Human Gait Recognition Using Body Measures and Joint Angles (original) (raw)

Gait recognition based on kinect sensor

This paper presents gait recognition based on human skeleton and trajectory of joint points captured by Microsoft Kinect sensor. In this paper Two sets of dynamic features are extracted during one gait cycle: the first is Horizontal Distance Features (HDF) that is based on the distances between (Ankles, knees, hands, shoulders), the second set is the Vertical Distance Features (VDF) that provide significant information of human gait extracted from the height to the ground of (hand, shoulder, and ankles) during one gait cycle. Extracting these two sets of feature are difficult and not accurate based on using traditional camera, therefore the Kinect sensor is used in this paper to determine the precise measurements. The two sets of feature are separately tested and then fused to create one feature vector. A database has been created in house to perform our experiments. This database consists of sixteen males and four females. For each individual, 10 videos have been recorded, each record includes in average two gait cycles. The Kinect sensor is used here to extract all the skeleton points, and these points are used to build up the feature vectors mentioned above. K-nearest neighbor is used as the classification method based on Cityblock distance function. Based on the experimental result the proposed method provides 56% as a recognition rate using HDF, while VDF provided 83.5% recognition accuracy. When fusing both of the HDF and VDF as one feature vector, the recognition rate increased to 92%, the experimental result shows that our method provides significant result compared to the existence methods.

Human gait identification using Kinect sensor

Kurdistan Journal for Applied Research

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods.

Gait-based recognition of humans using Kinect camera

2013

This work presents a research which objective is to develop a software prototype like biometric system to recognize subjects with a gait analysis, for identification and verification environments, making use of a depth camera such as the Kinect Camera of Microsoft. This software will get some gait features and later, we will use the software WEKA for machine learning to extract, using several algorithms, percentages of similarity between the recordings belonging to a training set, and some different recordings belonging to a validation set. Key-Words: Human recognition, gait analysis, Kinect sensor, biometric system, signal processing.

Kinect-Based Human Gait Recognition Using Static and Dynamic Features

Gait recognition is an important type of biometric that aims to recognize human gait based on their walking style. This paper takes advantage of the Microsoft Kinect sensor to get in-depth information on the human skeleton and trajectory of joints for recognition. In this paper, we investigate two sets of features which were extracted during one gait cycle. The first set is called Static Features (SF), which is based on the length of bones (upper and lower legs, Torso, upper and lower arm). The second set is Dynamic Features (DF), which were extracted from the height to the ground of (wrist, shoulder, and ankle) and distance between shoulders during one gait cycle. Extracting these features is difficult when using traditional camera such as CCTV. A database has been created to perform our experiments; this database consists of twenty participants walking, when indoors, form right to left. For each person 10 videos have been recorded. We used K-nearest neighbour (kNN) as a classification method, based on Cityblock distance. The experimental result of the proposed method accomplished a success rate of 95.5%. The experimental result also shows that the proposed provides a significant result compared to the existing methods

Kinect-Based Gait Recognition Using Sequences of the Most Relevant Joint Relative Angles

J. WSCG, 2015

This paper introduces a new 3D skeleton-based gait recognition method for motion captured by a low-cost consumer level camera, namely the Kinect. We propose a new representation of human gait signature based on the spatio-temporal changes in relative angles among different skeletal joints with respect to a reference point. A sequence of joint relative angles (JRA) between two skeletal joints, computed over a complete gait cycle, comprises an intuitive representation of the relative motion patterns of the involved joints. JRA sequences originated from different joint pairs are then evaluated to find the most relevant JRAs for gait description. We also introduce a new dynamic time warping (DTW)-based kernel that takes the collection of the most relevant JRA sequences from the train and test samples and computes a dissimilarity measure. The use of DTW in the proposed kernel makes it robust in respect to variable walking speed and thus eliminates the need of resampling to obtain equal-l...

Biomechanical application: exploitation of kinect sensor for gait analysis

2017

Human gait recognition is an important indicator and are extensively studied research area especially with the aging population and rehabilitation applications. Application of gait analysis ranges from diagnosis, monitoring and early detection of potential hazards such as human fall. There are various types of approaches used in gait analysis including wearable, ambient and vision based devices. Microsoft Kinect sensor is well-known among researchers since it can give depth and normal colour images as well. This paper presents a preliminary study on gait analysis of lower body parts. The measurement taken includes step width, step lengths, stride lengths and angles of knee respective hip and ankle while walking. The results showed that the algorithms implemented were able to accurately measure the lengths with low error rate.

Person identification using Kinect sensor

Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), 2014

A simple and easy-to-use system is designed for recognition of persons using their walking style. Here Kinect sensor is employed to generate the twenty body joint co-ordinates of the persons. In this proposed algorithm, we have processed five walking data sets from each twenty five persons. Total nine body joint co-ordinates are considered for each frame using Kinect sensor. Once the co-ordinates are obtained, then normalization is carried out based on the coordinate of the hip center for the first frame. All the coordinates are in the 3D space. The total dataset is divided into two parts for training and testing purposes. For the training procedure, 960 walking data sets are processed and the average walking data set is examined for each person. Remaining 320 walking data sets for each person are considered for testing purpose. The recognition procedure comprised with mean and standard deviation parameters with accuracy rate of 92.4786%.

Human gait recognition using preprocessing and classification techniques

International Journal of Electrical and Computer Engineering (IJECE), 2020

Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system's capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

Improved Gait Recognition with Automatic Body Joint Identification

Lecture Notes in Computer Science, 2011

Gait recognition is an unobtrusive biometric, which allows identification of people from a distance by the manner in which they walk. In this paper, a new approach is proposed for extracting human gait features based on body joint identification from human silhouette images. In the proposed approach, the human silhouette image is first enhanced to remove the artifacts before it is divided into eight segments according to a priori knowledge of human body proportion. Next, the body joints which act as the pivot points in human gait are automatically identified and the joint trajectories are computed. To assess the performance of the extracted gait features, fuzzy k-nearest neighbor classification technique is used to identify subjects from the SOTON covariate database. The experimental results have shown that the gait features extracted using the proposed approach are effective as the recognition rate has been improved.

Artificial Neural Network Based Gait Recognition Using Kinect Sensor

IEEE Access, 2019

Accurate gait recognition is of high significance for numerous industrial and consumer applications, including video surveillance, virtual reality, on-line games, medical rehabilitation, collaborative space exploration, and others. This paper proposes a new architecture designed using deep learning neural network for a highly accurate and robust Kinect-based gait recognition. Two new geometric features: joint relative cosine dissimilarity and joint relative triangle area are introduced. Both of the proposed features are view and pose invariant, thus enhancing recognition performance. The proposed neural network model is trained using the feature vector of dynamic joint relative cosine dissimilarity and joint relative triangle area. Subsequent application of Adam optimization method minimizes the loss of the objective function iteratively. The performance of the proposed deep learning neural network architecture is evaluated on two publicly available 3D skeleton-based gait datasets recorded with the Microsoft Kinect sensor. It is experimentally proven that the accuracy, precision, recall, and F-score of the proposed neural network architecture, trained using introduced dynamic geometric features, is superior to other state-of-the-art methods for Kinect skeleton-based gait recognition. INDEX TERMS Kinect-based gait recognition, human motion, Microsoft Kinect, joint relative cosine dissimilarity, joint relative triangle area, deep learning neural network.