Vijay Bhaskar Semwal

Human Activity Recognition Using Gait Pattern

Vision-based human activity recognition is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here the authors propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. The authors contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that their method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.

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.

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.

Toward Developing a Computational Model for Bipedal Push Recovery–A Brief

The human being can negotiate with external push up to certain extent reactively. Grown up persons have better push recovery capability than kids and also the professional wrestlers acquire better push recovery capability than normal human being. The acquired push recovery capability, therefore, is based on learning. However, the mechanism of learning is not known to us. Researchers around the world are trying to explore this mystery through developing various models and implementing them on various humanoid robots. All the models based on conventional mechanics and controls have inherent limitations. We believe appropriate computational model based on learning will be able to effectively address this issue. Accordingly, we have collected extensively humanoid push recovery data using our innovative idea of exploiting the accelerometer sensor of smart phone. Through our experiments, we have studied the human push recovery by fusing data at feature level using physics toolbar accelerometer of android interface kit. 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. A learning vector quantization-based classifier has been developed to identify the coordination between various push and hip and knee joints.

Analysis of Gait Pattern to Recognize the Human Activities

Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval.

Generation of Joint Trajectories Using Hybrid Automate-Based Model: A Rocking Block-Based Approach

Human walk is the combination of seven different discrete subphases. It is difficult to express the one gait cycle as a whole. To develop the human like bipedal robot, the walk cycle is divided into seven discrete subphases. Each subphases has its own continuous dynamics. To express this discrete behavior for the development of the more accurate bipedal robot, the hybrid automata are proposed. The bipedal walk is configured as the rocking block model. It is the first attempt to express the bipedal walk as a rocking block. During double support phases, it is configured as a vertical rectangular plane, and during the left and right leg swing, it is configured as the tilt of the rectangular rocking block in the left and right direction. In this paper, we have configured the bipedal robot as the rocking block before and after impact. The novelty of work is the configuration of bipedal walk as the rocking block and the development of hybrid automata. We configured the hybrid automata dynamic walk model for individual subjects. The trajectory generated by the model is compared with the two models of OpenSim bipedal Gait2354 and normal walk. This paper presents a new modeling technique of bipedal locomotion using hybrid automata. The hip, knee, and ankle trajectories have been synthesized from the model. The stability margin has been defined analytically. Similarly, these trajectories have been fed to a real humanoid robot HOAP2, which were able to perform the stable walking with these trajectories.

Design of Vector Field for Different Subphases of Gait and Regeneration of Gait Pattern

In this paper, we have designed the vector fields (VFs) for all the six joints (hip, knee, and ankle) of a bipedal walking model. The bipedal gait is the manifestation of temporal changes in the six joints angles, two each for hip, knee, and ankle values and it is a combination of seven different discrete subphases. Developing the correct joint trajectories for all the six joints was difficult from a purely mechanics-based model due to its inherent complexities. To get the correct and exact joint trajectories, it is very essential for a modern bipedal robot to walk stably. By designing the VF correctly, we are able to get the stable joint trajectory ranges and able to reproduce angle ranges from theses designed VFs. This is purely a data driven computational modeling approach, which is based on the hypothesis that morphologically similar structure (human-robot) can adopt similar gait patterns. To validate the correctness of the design, we have applied all the possible combination of joint trajectories to HOAP-2 bipedal robot, which could walk successfully maintaining its stability. The VF provides joint trajectories for a particular joint. The results show that our data driven computational model is able to provide the correct joints angle ranges, which are stable.Note to Practitioners—In this research, we have developed the vector field (VF) for each joint (hip, knee, and ankle) of a biped, which plays an important role in walking. The idea is noble and based on data driven computational model. The generated trajectories are applied on HOAP-2 bipedal humanoid robot and compare the two joint trajectories from VF with HOAP-2 model and hybrid automata model.

Data Driven Computational Model for Bipedal Walking and Push Recovery.

In this research, we have developed the data driven computational walking model to overcome the problem with traditional kinematics based model. Our model is adaptable and can adjust the parameter morphological similar to human. The human walk is a combination of different discrete sub-phases with their continuous dynamics. Any system which exhibits the discrete switching logic and continuous dynamics can be represented using a hybrid system. In this research, the bipedal locomotion is analyzed which is important for understanding the stability and to negotiate with the external perturbations. We have also studied the other important behavior push recovery. The Push recovery is also a very important behavior acquired by human with continuous interaction with environment. The researchers are trying to develop robots that must have the capability of push recovery to safely maneuver in a dynamic environment. The push is a very commonly experienced phenomenon in cluttered environment. The human beings can recover from external push up to a certain extent using different strategies of hip, knee and ankle. The different human beings have different push recovery capabilities. For example a wrestler has a better push negotiation capability compared to normal human beings. The push negotiation capability acquired by human, therefore, is based on learning but the learning mechanism is still unknown to researchers. The research community across the world is trying to develop various humanoid models to solve this mystery. Seeing all the conventional mechanics and control based models have some inherent limitations, a learning based computational model has been developed to address effectively this issue. In this research we will discuss how we have framed this problem as hybrid system.