Sanjay Saini

Bio: Sanjay Saini is an academic researcher from Petronas. The author has contributed to research in topics: Particle filter & Particle swarm optimization. The author has an hindex of 4, co-authored 10 publications receiving 188 citations. Previous affiliations of Sanjay Saini include Universiti Teknologi Petronas & Shanmugha Arts, Science, Technology & Research Academy.

A low-cost game framework for a home-based stroke rehabilitation system

Abstract: Stroke is a major cause of severe physical disability, leading into a variety of impairments. In general, stroke rehabilitation is a process which requires intensive direct physical therapy and is usually guided by physiotherapists. The long and intensive therapy sessions often results in patients losing the motivation to continue with the therapy, and as a result patients do not recover to their prospective. With increasing occurrence of stroke incidence, therapists are under pressure for time. At present most of the rehabilitation programmes are highly human intensive. Thus an innovative game technology that supports stroke rehabilitation may provide new opportunities. The main objective of this paper is to present a new low cost game framework for stroke rehabilitation programme that would increase patients' motivation for therapy, and also to study the feasibility and effect of a new game based technology to support hand and leg rehabilitation. In this paper, some important new game design principles for hand and leg rehabilitation with a standard angle based representation of the full body motion during exercise, for improving the accuracy of stroke exercise are presented. The design of serious games, with important game design principle frequently linked with worthy user engagement, may offer perceptions into how more effective systems can be developed for stroke rehabilitation. The additional bio-signal and online database will enable evaluation of patient s movement performance.

An efficient vision-based traffic light detection and state recognition for autonomous vehicles

Abstract: Traffic Light Detection(TLD) and understanding their state semantics at intersections plays a pivotal role in driver assistance systems and, by extension, autonomous vehicles. Despite of several reliable traffic light state detection approaches in literature, traffic light state recognition still remains an open problem due to outdoor perception challenge which includes occlusions, illumination and scale variations. This paper presents a vision-based traffic light structure detection and convolutional neural network (CNN) based state recognition method, which is robust under different illumination and weather conditions. In the first step, traffic light candidate regions are generated by performing HSV based color segmentation, which are then filtered out using shape and area analysis. Further, in order to incorporate the structural information of traffic light in diverse background scenarios, Maximally Stable Extremal Region (MSER) approach is employed, which helps to localize the correct traffic light structure in the image. To further validate the traffic light candidate regions, Histogram of Oriented Gradients (HOG) features are extracted for each region and traffic light structures are validated using Support Vector Machine (SVM). The state of the traffic lights are then recognized using CNN. To evaluate the performance of the proposed method, we present several results under a variety of lighting conditions in a real-world environment. Experimental result shows that the proposed method outperforms other vision based conventional methods under varying light and weather conditions.

A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking

Abstract: Automatic human motion tracking in video sequences is one of the most frequently tackled tasks in computer vision community. The goal of human motion capture is to estimate the joints angles of human body at any time. However, this is one of the most challenging problem in computer vision and pattern recognition due to the high-dimensional search space, self-occlusion, and high variability in human appearance. Several approaches have been proposed in the literature using different techniques. However, conventional approaches such as stochastic particle filtering have shortcomings in computational cost, slowness of convergence, suffers from the curse of dimensionality and demand a high number of evaluations to achieve accurate results. Particle swarm optimization (PSO) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space. This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking. An attempt is made to provide a guide for the researchers working in the field of PSO based human motion tracking from video sequences. Additionally, the paper also presents the performance of various model evaluation search strategies within PSO tracking framework for 3D pose tracking.

Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization

Abstract: The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Systematic review of Kinect applications in elderly care and stroke rehabilitation

Abstract: In this paper we present a review of the most current avenues of research into Kinect-based elderly care and stroke rehabilitation systems to provide an overview of the state of the art, limitations, and issues of concern as well as suggestions for future work in this direction. The central purpose of this review was to collect all relevant study information into one place in order to support and guide current research as well as inform researchers planning to embark on similar studies or applications. The paper is structured into three main sections, each one presenting a review of the literature for a specific topic. Elderly Care section is comprised of two subsections: Fall detection and Fall risk reduction. Stroke Rehabilitation section contains studies grouped under Evaluation of Kinect’s spatial accuracy, and Kinect-based rehabilitation methods. The third section, Serious and exercise games, contains studies that are indirectly related to the first two sections and present a complete system for elderly care or stroke rehabilitation in a Kinect-based game format. Each of the three main sections conclude with a discussion of limitations of Kinect in its respective applications. The paper concludes with overall remarks regarding use of Kinect in elderly care and stroke rehabilitation applications and suggestions for future work. A concise summary with significant findings and subject demographics (when applicable) of each study included in the review is also provided in table format.

A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation

Abstract: This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson’s, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect’s accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.

A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System

Abstract: The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge. This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics. This review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Abstract: Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation.

Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Abstract: Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.