Gora Chand Nandi

A novel distance estimation approach for 3D localization in wireless sensor network using multi dimensional scaling

Node localization is very important in Wireless Sensor Network (WSN) and distance estimation between pairs of nodes is the prerequisite for localization and thus the applicability of the reported events. The paper proposes a novel distance estimation algorithm to estimate distances of each node to every other node in the network. The main contribution of the paper is the definition of a dissimilarity matrix representing the distance of each node to every other node in the network. MDS based localization algorithm is used to determine coordinates of the node in a local coordinate system and Helmert Transformation is used to convert the local coordinates of the node into a global coordinate system. The effect of various parameters affecting the performance of proposed algorithm is also presented in the paper. Finally, the efficiency of the proposed algorithm is established through the simulation results.

Development of Adaptive Modular Active Leg (AMAL) using bipedal robotics technology

The objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a Central Pattern Generator (CPG) based control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. In this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleighs oscillator. An indigenous probe, Humanoid Gait Oscillator Detector (HGOD) has been designed for capturing gait patterns from various individuals of different height, weight and age. These data are used to design a Fuzzy inference system which generates most suitable gait pattern for an amputee. The output of the Fuzzy inference system is used for designing a CPG best suitable for the amputee. We then developed a CPG based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called AMAL (Adaptive Modular Active Leg). Also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network.

Biometric gait identification based on a multilayer perceptron

In this study, we propose a novel approach for biometric gait identification. We designed a multilayered back-propagation algorithm-based artificial neural network for gait pattern classification and we compared the results obtained with those produced using the k -means and k -nearest neighbor algorithms. A novel aspect of our feature extraction procedure was the use of a kernel-based principal components analysis because the captured real-time data exhibited significant nonlinearity. The gait data were classified into four classes: normal, crouch-2, crouch-3, and crouch-4. The proposed method achieved gait identification with very good activity recognition accuracy (ARA). The experimental results demonstrated that the proposed methodology could recognize different activities accurately in outdoor and indoor environments, while maintaining a high ARA. The identification of disordered or abnormal gait patterns was the fundamental aim of this study. Thus, we propose a method for the early detection of abnormal gait patterns, which can provide warnings about the potential development of diseases related to human walking. Furthermore, this gait-based biometric identification method can be utilized in the detection of gender, age, race, and for authentication purposes. Novel biometric gait identification approach based on a multilayer layer perceptron.Identification of disordered and abnormal gait patterns is a fundamental problem.Development of an intelligent system to identify human activities.

Biologically-inspired push recovery capable bipedal locomotion modeling through hybrid automata

The earlier developed two stage hybrid automata is not a perfect representation of human walk as it is a combination of discrete and continuous phases and the whole human GAIT has 8 stages. Our major contribution is eight stage hybrid automata for large push recovery and various dynamic parameter studies for stable walk model. We have developed a controller to verify different stage of human locomotion by using OpenSim data for model 3DGaitModel2354 and lower extremity data. We verified the hybrid automata model using the real human GAIT data for normal person. We identify the?importance?of?the human lower extremity?for locomotion and push recovery from large perturbation. The novelty of research work is to model the bipedal locomotion as a re-usable component based framework. Our original contribution lies in the fact that we have tried to view it from a software engineering perspective. Hybrid automata for eight stage of GAIT cycle of human is implemented.Design the controller for stable walk.Defined all the dynamic and static parameter.Described the domain break down of humanoid locomotion as hybrid system.Proposed the canonical equation for universal for moment of different join angle to produce exact human locomotion.

Recognizing & interpreting Indian Sign Language gesture for Human Robot Interaction

This paper describes a novel approach towards recognizing of Indian Sign Language (ISL) gestures for Humanoid Robot Interaction (HRI). An extensive approach is being introduced for classification of ISL gesture which imparts an elegant way of interaction between humanoid robot HOAP-2 and human being. ISL gestures are being considered as a communicating agent for humanoid robot which is being used in this context explicitly. It involves different image processing techniques followed by a generic algorithm for feature extraction process. The classification technique deals with the Euclidean distance metric. The concrete HRI system has been established for initiation based learning mechanism. The Real time robotics simulation software, WEBOTS has been adopted to simulate the classified ISL gestures on HOAP-2 robot. The JAVA based software has been developed to deal with the entire HRI process.

Study of humanoid Push recovery based on experiments

Human can negotiate and recovers from Push up to certain extent. The push recovery capability grows with age (a child has poor push recovery than an adult) and it is based on learning. A wrestler, for example, has better push recovery than an ordinary man. However, the mechanism of reactive push recovery is not known to us. We tried to understand the human learning mechanism by conducting several experiments. The subjects for the experiments were selected both as right handed and left handed. Pushes were induced from the behind with close eyes to observe the motor action as well as with open eyes to observe learning based reactive behaviors. Important observations show that the left handed and right handed persons negotiate pushes differently (in opposite manner). The present research describes some details about the experiments and the analyses of the results mainly obtained from the joint angle variations (both for ankle and hip joints) as the manifestation of perturbation. After smoothening the captured data through higher order polynomials, we feed them to our model which was developed exploiting the physics of an inverted pendulum and configured it as a representative of the subjects in the Webot simulation framework available in our laboratory. In each cases the model also could recover from the push for the same rage of perturbation which proves the correctness of the model. Hence the model now can provide greater insight to push recovery mechanism and can be used for determining push recovery strategy for humanoid robots. The paper claimed the push recovery is software engineering problem rather than hardware.

Biped model based on human Gait pattern parameters for sagittal plane movement

The present research as described in this paper tries to impart how imitation based learning for behavior-based programming can be used to teach the robot. The simulated model tries to imitate human GAIT pattern and negotiate push with efficient recovery [1]. This paper also proposes the HOAP2 [2] based biped model to achieve gait cycle imitation and push recovery on humanoid. The proposed model follows the Gait cycle [1] and can be further used for developing a model capable to recover from push similar to human biology. This development is a big step in way to prove that push recovery is a software engineering problem and not hardware engineering problem. The walking algorithm used here aims to select a subset of push recovery problem i.e. disturbance from environment. We applied the physics at each joint of Halo with some degree of freedom. The proposed model, Halo is different from other models as previously developed model were inconsistent with data for different persons. This would lead to development of the generalized biped model in future and will bridge the gap between performance and inconsistency. In this paper the proposed model is applied to data of different persons. Accuracy of model, performance and result is measured using the behavior negotiation capability of model developed. In order to improve the performance, proposed model gives the freedom to handle each joint independently based on the belongingness value for each joint. The development can be considered as important development for future world of robotics. The accuracy of model is 70% in one go. In this paper, we achieve to imitate the human gait cycle for HOAP-2 [2] robots model Halo. We validate our model by giving different input configuration parameter i.e. CoM, CoP and joint angle of different samples to HOAP-2[2] model designed in Webots, which can demonstrate the behavior as per new configuration provided for different person.

Recognition of Isolated Indian Sign Language Gesture in Real Time

Indian Sign Language (ISL) consists of static as well as dynamic hand gestures for communication among deaf and dumb persons. Most of the ISL gestures are produced using both hands. A video database is created and utilized which contains several videos, for a large number of signs. Direction histogram is the feature used for classification due to its appeal for illumination and orientation invariance. Two different approaches utilized for recognition are Euclidean distance and K-nearest neighbor metrics.

Face recognition with liveness detection using eye and mouth movement

The recent literature on face recognition technology discusses the issue of face spoofing which can bypass the authentication system by placing a photo/video/mask of the enrolled person in front of the camera. This problem could be minimized by detecting the liveness of the person. Therefore, in this paper, we propose a robust liveness detection scheme based on challenge and response method. The liveness module is added as extra layer of security before the face recognition module. The liveness module utilizes face macro features, especially eye and mouth movements in order to generate random challenges and observing the users response on account of this. The reliability of liveness module is tested by placing different types of spoofing attacks with various means, like using photograph, videos, etc. In all, five types of attacks have been taken care of and prevented by our system. Experimental results show that system is able to detect the liveness when subjected to all these attacks except the eye & mouth imposter attack. This attack is able to bypass the liveness test but it creates massive changes in face structure. Therefore resultant unrecognized or misclassified by the face recognition module. An experimental test conducted on 65 persons on university of Essex face database confirms that removal of eye and nose components results 75% misclassification.

Robotic Services in Cloud Computing Paradigm

Cloud computing and service oriented architecture (SOA) are the dominant computing paradigm. Since past few years Robotics applications have also started to build around these paradigms. This paper presents entrance of robotic services in SOA and cloud computing. Where a client/user can opt for the robotic services present at the cloud like navigation, map building, object recognition etc. Map-reduce computing cluster is also facilitated at the cloud to process large amount of data for the cloud robotic services. The whole system follows the Web 2.0 standard. We also reported the simulation results for service based speech controlled robots with visual programming language (VPL) of Microsoft Development Robotics Studio (MDRS) and implementation of map-reduce computing cluster in robotic cloud.