Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Covariate conscious approach for Gait recognition based upon Zernike moment invariants

Gait recognition i.e. identification of an individual from his/her walking pattern is an emerging field. While existing gait recognition techniques perform satisfactorily in normal walking conditions, there performance tend to suffer drastically with variations in clothing and carrying conditions. In this work, we propose a novel covariate cognizant framework to deal with the presence of such covariates. We describe gait motion by forming a single 2D spatio-temporal template from video sequence, called Average Energy Silhouette image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the parts of AESI infected with covariates. Following this, features are extracted from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of Directional Pixels (MDPs) methods. The obtained features are fused together to form the final well-endowed feature set. Experimental evaluation of the proposed framework on three publicly available datasets i.e. CASIA dataset B, OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently published gait recognition approaches, prove its superior performance.Comment: 11 page

Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/