2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
With the advancement of robotics in industry and healthcare, a key component of research is to de... more With the advancement of robotics in industry and healthcare, a key component of research is to develop sophisticated design automations to offer precise and accurate response of the system with the freedom to access the system easily. The paper proposes a novel arm-gesture-driven position control of a robot arm, which can be operated by any person even without specialized domain knowledge. The uncertainty in gestures is modeled by type-2 fuzzy membership functions. Type2 fuzzy rules are employed to recognize gestures. The robot learns different tasks from human demonstrations, and finally principles of non-monotonic reinforcement learning are used for mapping the gestures to the tasks. Over 20,000 gestures obtained from 10 subjects are used to validate the effectiveness of the gesture recognition scheme. One interesting parameter is employed in the learning dynamics to control the unlearning rate of old knowledge and learning rate of the new knowledge about gesture-to-action mapping...
Dance posture recognition has emerged as one of the most enriched and pragmatic research genre be... more Dance posture recognition has emerged as one of the most enriched and pragmatic research genre because of its wide applications for facilitating learning of dance by exploiting electronic means only. Ballet dance is one of the most ancient dance forms; moreover the artistic postures and unprecedented elegance of the ballet dance form fascinate the dance enthusiasts a lot. This motivated us to design a system enabling e-learning of ballet dance that allows a user to learn the art all by himself with the help of associative devices and to correct the postures by measuring the extent of correctness, thus not requiring the presence of any instructor to identify the flaws. In this paper, the principles of Type 1 Fuzzy Inference rules have been embedded in the correctness measurement phase of Self Organizing Feature Map. This paper primarily employs Self Organizing Feature Map because it serves the purpose of dance posture recognition and correctness measurement employing Type 1 Fuzzy rul...
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
The crucial role of working memory for cognitive performance based on temporary information proce... more The crucial role of working memory for cognitive performance based on temporary information processing has been studied for decades. Behavioral tests and functional magnetic resonance imaging studies have already established a relation between cortical activation and working memory performance. However, dearth of literature is observed on functional near infrared spectroscopy based assessment of working memory. This paper provides a novel study of cerebral oxygenation as a basis of working memory performance considering verbal working memory task. The recorded signal is filtered and processed for extraction of 96 dimensional features, which is reduced to 24 by means of a meta-heuristic optimization technique. Next, we transfer it to a type-2 fuzzy classifier for classifying into three different cognitive load classes: high, moderate and low. Experimental instances show that the type-2 fuzzy classifier with the proposed artificial bee colony optimization induced feature selection technique, yields high classification accuracy, which tends to reach above 86 percent. Additionally, experimental evidence suggests that low working memory performance is, possibly, due to poor activation of certain regions of frontal cortex.
The recently developed Kinect sensor has opened a new horizon to Human-Computer Interface (HCI) a... more The recently developed Kinect sensor has opened a new horizon to Human-Computer Interface (HCI) and its native connection with Microsoft's product line of Xbox 360 and Xbox One video game consoles makes completely hands-free control in next generation of gaming. Games that requires a lot of degree of freedoms, especially the driving control of a car in racing games is best suitable to be driven by gestures, as the use of simple buttons does not scale to the increased number of assistive, comfort, and infotainment functions. In this paper, we propose a Mamdani type-I fuzzy inference system based data processing module which effectively takes into account the dependence of actual steering angle with the distance of two palm positions and angle generated with respect to the sagittal plane. The FIS output variable controls the duration of a virtual “key-pressed” event which mocks the users pressing of actual keys assigned to control car direction in the original game. The acceleration and brake(deceleration) of the vehicle is controlled using the relative displacement of left and right feet. The proposed experimental setup, interfacing Kinect and a desktop based racing game, has shown that the virtual driving environment can be easily applied to any games belonging to this particular genre.
This article proposes a novel framework for robot navigation in unstructured environments based u... more This article proposes a novel framework for robot navigation in unstructured environments based upon gestural features. In such an environment, it is not possible for a human being to be present in person to accomplish any task. Hence it is a good alternative to direct a robot to navigate to a specific position and execute a specific task. Without efficient instructions there is a high probability of the task remaining incomplete due to the robot not reaching its goal or a possible damage of the robot because of the unknown environment. Gestures are considered to be a wise modality for robot control in unstructured environment because of the high accuracy that the task calls for. For this purpose, an Interval Type 2 Fuzzy based gesture recognition scheme has been employed for the classification phase. This is done to register the uncertainty quotient in the extracted gestural features from different subjects at different time instances. This article addresses an extremely important issue of robot navigation in perilous environments which are not accessible to the human beings. The scheme has shown promising potential for future applications in terms of performance metrics as compared to other existing state of the art techniques.
Studies in computational intelligence, Jul 6, 2017
This work aims at designing a fuzzy matching algorithm that would automatically recognize an unkn... more This work aims at designing a fuzzy matching algorithm that would automatically recognize an unknown ballet posture from seventeen fundamental ballet dance primitives. A novel and simple 7-stage system is proposed to achieve the desired objective. Minimized skeletons of the dance postures are generated after performing skin color segmentation on them. Straight line approximation on the minimized skeletons with the help of chain code and sampling generate their equivalent stick figure diagrams. Significant straight lines from the stick figure diagrams are considered, their fuzzy membership with respect to the 4 quadrants are evaluated. Finally with the help of the evaluated data, a fuzzy T-norm operator determines the proximity of a generated dance posture with the seventeen fundamental dance primitives.
This paper presents a novel fuzzy based approach to gesture driven human robot interaction. Now a... more This paper presents a novel fuzzy based approach to gesture driven human robot interaction. Now a day, gestures are considered to be the most effective communicative medium for remotely controlling a robot. In this work, the gestures are employed to instruct a Khepera II robot to move from a specific starting position to a goal position following a specific path. The main highlight is the determination of exact degree of rotation with proper application of acceleration and brake in order to reach the specified goal position without hitting the obstacles. A Takagi-Sugeno-Kang based fuzzy model with two antecedents (type-2 fuzzy sets) and one consequent (crisp value) has been employed to determine the angle of rotation. The performance of the proposed framework has been tested in terms of a number of parameters like accuracy, precision, error rate etc. And in each case, the formulated strategy has proved its worth.
The paper introduces a novel approach to short term memory modeling using electroencephalographic... more The paper introduces a novel approach to short term memory modeling using electroencephalographic signals acquired from occipital, prefrontal and parietal lobes of the subjects during the recognition of asymmetric two-dimensional object shapes. Each subject is presented with a two-dimensional object of irregular geometry and is asked to remember it for a short period until he/she is instructed to draw the two-dimensional shape of the object. After the drawing is complete, the reference two-dimensional shape and the one produced by the subject are compared. The paper attempts to model Short term memory using unsupervised Hebbian Learning, considering functional invariance of the other relevant brain modules lying on the encoding and recall pathways of the memory. An error norm is defined to measure the magnitude of the error between the input and reproduced shape pattern, and thereby is used to adapt the short term memory. A small value in error indicates proper learning of the proposed short term memory model, and thus can be used to discriminate people with memory failure. Experiments undertaken reveal that the error metric could be used successfully to detect memory failures in five patients, two of whom were in early stages of Alzheimer's disease, two of whom suffer from amnesia, and one patient with parietal lobe damage. The proposed short term memory model thus is expected to have applications in detecting memory failures in shape-recognition tasks.
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
The paper attempts to model human working memory using fuzzy relational equation with an aim to r... more The paper attempts to model human working memory using fuzzy relational equation with an aim to retrieve the relevant stored information in the memory from the partial input using the model. Psycho-physiological experiments have been developed to validate the model to match the model generated memory-response with the actual memory response using EEG signals acquired during memory encoding and recall phases. The fuzzy relational equation developed here represents brain connectivity in the fuzzy space between the encoding and the recall instances. The paper introduces a novel approach to compute inverse fuzzy relation with respect to max-min composition operator to determine the short-term memory information from the working memory, when the latter is stimulated with partial faces of people already encoded in the short-term memory. An error metric is defined to measure the error amplitude between the model-predicted encoding pattern and the actual pattern encoded in the short-term memory. A small value in error indicates a good accuracy of the proposed working memory model, and thus can be used to discriminate people with memory failure. Experiments undertaken reveal that the error metric could be used successfully to detect memory failures in five patients, two of which suffer from Parkinson, two from the early Alzheimer's disease and one from frontal lobe damage.
2018 IEEE Congress on Evolutionary Computation (CEC), 2018
The proposed system aims at automatic identification of an unknown dance posture referring to the... more The proposed system aims at automatic identification of an unknown dance posture referring to the 34 primitive postures of ballet, simultaneously measuring the proximity of an unknown dance posture to a known primitive. A simple and novel seven stage algorithm achieves the desired objective. Skin color segmentation is performed on the dance postures, the output of which is dilated and edge is detected. From the boundaries of the postures, connected components are identified and the boundary is piecewise linearly approximated using modified artificial bee colony algorithm. Here, lies the novelty of our work. From the approximated boundary, features are extracted in terms of internal angles. This whole procedure is repeated for all the training images as well as testing image. The classification of the training image containing ballet posture is done using Euclidean distance matching.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
We study the vowels detection from brain activation due to vowel sound imageries. At first we exp... more We study the vowels detection from brain activation due to vowel sound imageries. At first we experimentally determine the maximum and relatively longer activation that takes place in the frontal or pre frontal lobe during vowel sound Imagination using acquired electroencephalographic signal analysis. Then we capture pre-frontal or frontal vowel sound imagery using a functional near infrared device to extract certain statistical features. Differential evolution based feature selection is used to for dimensionality reduction. The reduced feature set is then used to design an interval type 2 fuzzy classifier to classify the vowels from the pre frontal or frontal f-NIRs response to vowel sound imagination. Experiments undertaken confirm that the proposed classifier outperforms its competitors in classification accuracy for each vowel sound imagery class. They further confirm that the f-NIRs based classification outperforms EEG based modality for better capture of brain activations. Consonants are encoded with two vowel sounds with a space between them. Thus the proposed technique can effectively be used for mind driven type writing of vowels and consonants, serving people suffering from vocal deficiency.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P3... more The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P300 and N400 event related potentials during the learning phase of a simulated car-driving task. A set of driving stimuli, including braking on sudden appearance of bumpers, steering control at the sharp bending points, and the like, are prepared to experimentally classify the learning skill of the subjects in three distinct classes: Low, Medium and High. The complexity in classifying the subjective learning-skill arises because of intra- and inter-session variations in brain signals due to temporal fluctuation in activation levels of the involved brain lobes. A novel z-slice based model of general type-2 fuzzy set is proposed to represent the intra- and inter-session variations by the footprint of uncertainty and their composite variations by z-slices at different values of a given feature. Further, a novel technique for z-slice based classifier is proposed to classify the learning-skill under intra-and inter-session uncertainty. The proposed classifier is found to outperform traditional and well-known type-2 classifiers for the present learning-skill classification task in Experiments undertaken confirm the frontal and parietal theta (4-7 Hz) activation in discrete learning steps. The proposed research outcome may directly be utilized to certify driving learners with required learning skills.
Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2018
This chapter introduces a novel approach to examine the scope of tactile sensory perception as a ... more This chapter introduces a novel approach to examine the scope of tactile sensory perception as a possible modality of treatment of patients suffering from certain mental disorder using a Support Vector Machines with kernelized neural network. Experiments are designed to understand the perceptual difference of schizophrenic patients from normal and healthy subjects with respect to three different touch classes, including soft touch, rubbing, massaging and embracing and their three typical subjective responses. Experiments undertaken indicate that for normal subjects and schizophrenic patients, the average percentage accuracy in classification of all the three classes: pleasant/acceptable/unpleasant is comparable with their respective oral responses. In addition, for schizophrenic patients, the percentage accuracy for acceptable class is very poor of the order of below 12%, which for normal subjects is quite high (42%). Performance analysis reveals that the proposed classifier outperf...
2017 IEEE Congress on Evolutionary Computation (CEC), 2017
We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranki... more We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranking policy, instead of the Pareto ranking, supposing that a solution is unlikely to perform well for all objectives in a MaOO problem. A solution is thus evolved with respect to a specific objective only, which it may proficiently optimize. First, all objectives of the MaOO problem are individually optimized by evolutionary algorithms in parallel. The second step is concerned with judiciously selecting and filtering the quality solutions obtained by individual optimization of all objectives in parallel. A unique ranking policy is proposed to grade the members of the union set of quality solutions based on their extent of optimization of individual objectives. The evolutionary algorithm used for parallel optimization of all objectives in a MaOO here has been realized with differential evolution (DE). The mutation strategy of DE is also amended here with an aim to allow controlled communication between population members, concerned with parallel optimization of different objectives of a MaOO problem. Experiments undertaken with DTLZ and WFG test suits reveal that the proposed algorithm outperforms the state-of-art techniques with respect to inverted generational distance and hypervolume metrics.
2017 International Joint Conference on Neural Networks (IJCNN), 2017
Multi-robot cooperation entails planning by multiple robots for a common objective, where each ro... more Multi-robot cooperation entails planning by multiple robots for a common objective, where each robot/agent actuates upon the environment-based on the sensory information received from the environment. Multi-robot cooperation employing equilibrium-based reinforcement learning is optimal in the sense of system resource (time and/or energy) utilization, because of the prior adaption of the environment by the robots. Unfortunately, robots cannot enjoy such benefit of reinforcement learning in presence of multiple types of equilibria (here Nash equilibrium or correlated equilibrium). In the above perspective, robots need to adapt with a strategy, so that robots can select the optimal equilibrium in each step of the learning. The paper proposes consensus-based multi-agent Q-learning to address the bottleneck of the optimal equilibrium selection among multiple types. An analysis reveals that a consensus (joint action) is coordination type pure strategy Nash equilibrium as well as pure stra...
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
This paper proposes an Artificial Bee Colony optimization based static video summarization techni... more This paper proposes an Artificial Bee Colony optimization based static video summarization technique that represents the synopsis of a video by the most vital frames present in it. First the pixel clusters or Regions of Interest of the video frames which capture the most significant content variations are identified. A novel set of features measured in terms of the average hue values of each of these regions is then used to characterize the frames. Based on these features, the sequence of frames of the video is divided into segments by the Artificial Bee Colony optimization algorithm. The lengths of the segments are so optimized that all the frames of a particular segment have similar features, whereas, the middle frames of different segments are significantly different from each other. These middle frames are treated as the key-frames of the video. Any redundancy present in the selected key-frames is eliminated by comparing their hue histograms. The proposed work is validated on th...
The paper attempts to determine the brain connectivity among different brain regions/lobes during... more The paper attempts to determine the brain connectivity among different brain regions/lobes during subjective engagement in color perception. Traditionally, Granger causality analysis is employed to handle the problem. However, due to the applicability of Granger causality analysis for purely stochastic systems only, its suitability of applications in brain connectivity analysis is limited due to the presence of both deterministic and stochastic characteristics in the human brain. Additionally, because of the high parameter sensitivity of Granger causality technique, identifying the right set of parameters itself poses an additional problem. Convergent cross mapping overcomes the above limitations and thus has immense scope for exploitation in brain connectivity analysis. This paper makes an honest attempt to determine brain connectivity during subjective engagement in color perception using convergent cross mapping technique. Experimental results envisage that the proposed technique...
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
Although there exist recent works on fMRI based cognitive learning, there is a dearth of literatu... more Although there exist recent works on fMRI based cognitive learning, there is a dearth of literature on fNIRs based studies on learning and memory. This paper provides a novel study on the cognitive load detection of subjects engaged in symbol-meaning associative learning tasks from the direct measurement of the hemodynamic response of the brain. The hemodynamic response collected during symbol-meaning associative learning tasks by subjects are pre-processed (filtered from artifacts) for extraction of 112-dimensional features, which are reduced to 20 dimensions by a meta-heuristic optimization algorithm for subsequent transfer to a interval type-2 fuzzy classifier to classify three levels of cognitive loads (High, Low and Moderate) borne by the subjects at different time slots of the learning task. Analysis undertaken reveals that the type-2 fuzzy classifier with the proposed feature selection mechanism has a high performance in classification of the cognitive loads over 89%. Experimental analysis further reveals that the transfer of brain activation from orbitofrontal to ventrolateral prefrontal cortex takes place during transition of cognitive load from high to low. In addition, the activation of dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. These findings would offer justification of inability to handle high cognitive loads by people with under-developed/damaged orbitofrontal and dorsolateral prefrontal cortex.
2017 International Joint Conference on Neural Networks (IJCNN), 2017
Currently, gesture recognition from continuous video sequences is one of the most exciting resear... more Currently, gesture recognition from continuous video sequences is one of the most exciting research areas. This paper proposes a novel HMM-based gesture recognition scheme that can be implemented for developing an improved HCI system capable of providing enhanced performance. This framework explores the high potential of Microsoft's Kinect sensor in gesture recognition by utilizing it in the data acquisition phase. The primary novelty of the work lies in the choice of an active difference signature-based feature descriptor that contains time-warped information in a single sequence over the classically used geometric features. The discussed framework has been tested for 12 distinct gestures embodied by 60 different subjects and it is important to note that for all the gestures the proposed scheme has attained a fairly high recognition rate of nearly 90% which proves the worth of the present work in real time applications. Further, to check the efficacy of the newly formulated framework the performance of the same has been validated against the existing standard technologies.
IEEE Transactions on Cognitive and Developmental Systems, 2019
Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This pap... more Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This paper deals with color perceptual-ability measurement of subjects using brain response to basic color (red, green and blue) stimuli. It also attempts to determine subjective ability to recognize the base colors in presence of noise tolerance of the base colors, referred to as recognition tolerance. Because of intra-and inter-session variations in subjective brain signal features for a given color stimulus, there exists uncertainty in perceptual-ability. In addition, small variations in the color stimulus result in wide variations in brain signal features, introducing uncertainty in perceptual-ability of the subject. Type-2 fuzzy logic has been employed to handle the uncertainty in color perceptual-ability measurements due to a) variations in brain signal features for a given color, and b) the presence of colored noise on the base colors. Because of limited power of uncertainty management of interval type-2 fuzzy sets and high computational overhead of its general type-2 counterpart, we developed a semi-general type-2 fuzzy classifier to recognize the base color. It is important to note that the proposed technique transforms a vertical slice based general type-2 fuzzy set into an equivalent interval type-2 counterpart to reduce the computational overhead, without losing the contributions of the secondary memberships. The proposed semi-general type-2 fuzzy sets induced classifier yields superior performance in classification accuracy with respect to existing type-1, type-2 and other well-known classifiers. The brainunderstanding of a perceived base or noisy base colors is also obtained by exact low resolution electromagnetic topographic analysis (e-LORETA) software. This is used as the reference for our experimental results of the semi-general type-2 classifier in color perceptual-ability detection. Statistical tests undertaken confirm the superiority of the proposed classifier over its competitors. The proposed technique is expected to have interesting applications in identifying people with excellent color perceptual-ability for chemical, pharmaceutical and textile industries.
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
With the advancement of robotics in industry and healthcare, a key component of research is to de... more With the advancement of robotics in industry and healthcare, a key component of research is to develop sophisticated design automations to offer precise and accurate response of the system with the freedom to access the system easily. The paper proposes a novel arm-gesture-driven position control of a robot arm, which can be operated by any person even without specialized domain knowledge. The uncertainty in gestures is modeled by type-2 fuzzy membership functions. Type2 fuzzy rules are employed to recognize gestures. The robot learns different tasks from human demonstrations, and finally principles of non-monotonic reinforcement learning are used for mapping the gestures to the tasks. Over 20,000 gestures obtained from 10 subjects are used to validate the effectiveness of the gesture recognition scheme. One interesting parameter is employed in the learning dynamics to control the unlearning rate of old knowledge and learning rate of the new knowledge about gesture-to-action mapping...
Dance posture recognition has emerged as one of the most enriched and pragmatic research genre be... more Dance posture recognition has emerged as one of the most enriched and pragmatic research genre because of its wide applications for facilitating learning of dance by exploiting electronic means only. Ballet dance is one of the most ancient dance forms; moreover the artistic postures and unprecedented elegance of the ballet dance form fascinate the dance enthusiasts a lot. This motivated us to design a system enabling e-learning of ballet dance that allows a user to learn the art all by himself with the help of associative devices and to correct the postures by measuring the extent of correctness, thus not requiring the presence of any instructor to identify the flaws. In this paper, the principles of Type 1 Fuzzy Inference rules have been embedded in the correctness measurement phase of Self Organizing Feature Map. This paper primarily employs Self Organizing Feature Map because it serves the purpose of dance posture recognition and correctness measurement employing Type 1 Fuzzy rul...
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
The crucial role of working memory for cognitive performance based on temporary information proce... more The crucial role of working memory for cognitive performance based on temporary information processing has been studied for decades. Behavioral tests and functional magnetic resonance imaging studies have already established a relation between cortical activation and working memory performance. However, dearth of literature is observed on functional near infrared spectroscopy based assessment of working memory. This paper provides a novel study of cerebral oxygenation as a basis of working memory performance considering verbal working memory task. The recorded signal is filtered and processed for extraction of 96 dimensional features, which is reduced to 24 by means of a meta-heuristic optimization technique. Next, we transfer it to a type-2 fuzzy classifier for classifying into three different cognitive load classes: high, moderate and low. Experimental instances show that the type-2 fuzzy classifier with the proposed artificial bee colony optimization induced feature selection technique, yields high classification accuracy, which tends to reach above 86 percent. Additionally, experimental evidence suggests that low working memory performance is, possibly, due to poor activation of certain regions of frontal cortex.
The recently developed Kinect sensor has opened a new horizon to Human-Computer Interface (HCI) a... more The recently developed Kinect sensor has opened a new horizon to Human-Computer Interface (HCI) and its native connection with Microsoft's product line of Xbox 360 and Xbox One video game consoles makes completely hands-free control in next generation of gaming. Games that requires a lot of degree of freedoms, especially the driving control of a car in racing games is best suitable to be driven by gestures, as the use of simple buttons does not scale to the increased number of assistive, comfort, and infotainment functions. In this paper, we propose a Mamdani type-I fuzzy inference system based data processing module which effectively takes into account the dependence of actual steering angle with the distance of two palm positions and angle generated with respect to the sagittal plane. The FIS output variable controls the duration of a virtual “key-pressed” event which mocks the users pressing of actual keys assigned to control car direction in the original game. The acceleration and brake(deceleration) of the vehicle is controlled using the relative displacement of left and right feet. The proposed experimental setup, interfacing Kinect and a desktop based racing game, has shown that the virtual driving environment can be easily applied to any games belonging to this particular genre.
This article proposes a novel framework for robot navigation in unstructured environments based u... more This article proposes a novel framework for robot navigation in unstructured environments based upon gestural features. In such an environment, it is not possible for a human being to be present in person to accomplish any task. Hence it is a good alternative to direct a robot to navigate to a specific position and execute a specific task. Without efficient instructions there is a high probability of the task remaining incomplete due to the robot not reaching its goal or a possible damage of the robot because of the unknown environment. Gestures are considered to be a wise modality for robot control in unstructured environment because of the high accuracy that the task calls for. For this purpose, an Interval Type 2 Fuzzy based gesture recognition scheme has been employed for the classification phase. This is done to register the uncertainty quotient in the extracted gestural features from different subjects at different time instances. This article addresses an extremely important issue of robot navigation in perilous environments which are not accessible to the human beings. The scheme has shown promising potential for future applications in terms of performance metrics as compared to other existing state of the art techniques.
Studies in computational intelligence, Jul 6, 2017
This work aims at designing a fuzzy matching algorithm that would automatically recognize an unkn... more This work aims at designing a fuzzy matching algorithm that would automatically recognize an unknown ballet posture from seventeen fundamental ballet dance primitives. A novel and simple 7-stage system is proposed to achieve the desired objective. Minimized skeletons of the dance postures are generated after performing skin color segmentation on them. Straight line approximation on the minimized skeletons with the help of chain code and sampling generate their equivalent stick figure diagrams. Significant straight lines from the stick figure diagrams are considered, their fuzzy membership with respect to the 4 quadrants are evaluated. Finally with the help of the evaluated data, a fuzzy T-norm operator determines the proximity of a generated dance posture with the seventeen fundamental dance primitives.
This paper presents a novel fuzzy based approach to gesture driven human robot interaction. Now a... more This paper presents a novel fuzzy based approach to gesture driven human robot interaction. Now a day, gestures are considered to be the most effective communicative medium for remotely controlling a robot. In this work, the gestures are employed to instruct a Khepera II robot to move from a specific starting position to a goal position following a specific path. The main highlight is the determination of exact degree of rotation with proper application of acceleration and brake in order to reach the specified goal position without hitting the obstacles. A Takagi-Sugeno-Kang based fuzzy model with two antecedents (type-2 fuzzy sets) and one consequent (crisp value) has been employed to determine the angle of rotation. The performance of the proposed framework has been tested in terms of a number of parameters like accuracy, precision, error rate etc. And in each case, the formulated strategy has proved its worth.
The paper introduces a novel approach to short term memory modeling using electroencephalographic... more The paper introduces a novel approach to short term memory modeling using electroencephalographic signals acquired from occipital, prefrontal and parietal lobes of the subjects during the recognition of asymmetric two-dimensional object shapes. Each subject is presented with a two-dimensional object of irregular geometry and is asked to remember it for a short period until he/she is instructed to draw the two-dimensional shape of the object. After the drawing is complete, the reference two-dimensional shape and the one produced by the subject are compared. The paper attempts to model Short term memory using unsupervised Hebbian Learning, considering functional invariance of the other relevant brain modules lying on the encoding and recall pathways of the memory. An error norm is defined to measure the magnitude of the error between the input and reproduced shape pattern, and thereby is used to adapt the short term memory. A small value in error indicates proper learning of the proposed short term memory model, and thus can be used to discriminate people with memory failure. Experiments undertaken reveal that the error metric could be used successfully to detect memory failures in five patients, two of whom were in early stages of Alzheimer's disease, two of whom suffer from amnesia, and one patient with parietal lobe damage. The proposed short term memory model thus is expected to have applications in detecting memory failures in shape-recognition tasks.
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
The paper attempts to model human working memory using fuzzy relational equation with an aim to r... more The paper attempts to model human working memory using fuzzy relational equation with an aim to retrieve the relevant stored information in the memory from the partial input using the model. Psycho-physiological experiments have been developed to validate the model to match the model generated memory-response with the actual memory response using EEG signals acquired during memory encoding and recall phases. The fuzzy relational equation developed here represents brain connectivity in the fuzzy space between the encoding and the recall instances. The paper introduces a novel approach to compute inverse fuzzy relation with respect to max-min composition operator to determine the short-term memory information from the working memory, when the latter is stimulated with partial faces of people already encoded in the short-term memory. An error metric is defined to measure the error amplitude between the model-predicted encoding pattern and the actual pattern encoded in the short-term memory. A small value in error indicates a good accuracy of the proposed working memory model, and thus can be used to discriminate people with memory failure. Experiments undertaken reveal that the error metric could be used successfully to detect memory failures in five patients, two of which suffer from Parkinson, two from the early Alzheimer's disease and one from frontal lobe damage.
2018 IEEE Congress on Evolutionary Computation (CEC), 2018
The proposed system aims at automatic identification of an unknown dance posture referring to the... more The proposed system aims at automatic identification of an unknown dance posture referring to the 34 primitive postures of ballet, simultaneously measuring the proximity of an unknown dance posture to a known primitive. A simple and novel seven stage algorithm achieves the desired objective. Skin color segmentation is performed on the dance postures, the output of which is dilated and edge is detected. From the boundaries of the postures, connected components are identified and the boundary is piecewise linearly approximated using modified artificial bee colony algorithm. Here, lies the novelty of our work. From the approximated boundary, features are extracted in terms of internal angles. This whole procedure is repeated for all the training images as well as testing image. The classification of the training image containing ballet posture is done using Euclidean distance matching.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
We study the vowels detection from brain activation due to vowel sound imageries. At first we exp... more We study the vowels detection from brain activation due to vowel sound imageries. At first we experimentally determine the maximum and relatively longer activation that takes place in the frontal or pre frontal lobe during vowel sound Imagination using acquired electroencephalographic signal analysis. Then we capture pre-frontal or frontal vowel sound imagery using a functional near infrared device to extract certain statistical features. Differential evolution based feature selection is used to for dimensionality reduction. The reduced feature set is then used to design an interval type 2 fuzzy classifier to classify the vowels from the pre frontal or frontal f-NIRs response to vowel sound imagination. Experiments undertaken confirm that the proposed classifier outperforms its competitors in classification accuracy for each vowel sound imagery class. They further confirm that the f-NIRs based classification outperforms EEG based modality for better capture of brain activations. Consonants are encoded with two vowel sounds with a space between them. Thus the proposed technique can effectively be used for mind driven type writing of vowels and consonants, serving people suffering from vocal deficiency.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P3... more The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P300 and N400 event related potentials during the learning phase of a simulated car-driving task. A set of driving stimuli, including braking on sudden appearance of bumpers, steering control at the sharp bending points, and the like, are prepared to experimentally classify the learning skill of the subjects in three distinct classes: Low, Medium and High. The complexity in classifying the subjective learning-skill arises because of intra- and inter-session variations in brain signals due to temporal fluctuation in activation levels of the involved brain lobes. A novel z-slice based model of general type-2 fuzzy set is proposed to represent the intra- and inter-session variations by the footprint of uncertainty and their composite variations by z-slices at different values of a given feature. Further, a novel technique for z-slice based classifier is proposed to classify the learning-skill under intra-and inter-session uncertainty. The proposed classifier is found to outperform traditional and well-known type-2 classifiers for the present learning-skill classification task in Experiments undertaken confirm the frontal and parietal theta (4-7 Hz) activation in discrete learning steps. The proposed research outcome may directly be utilized to certify driving learners with required learning skills.
Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2018
This chapter introduces a novel approach to examine the scope of tactile sensory perception as a ... more This chapter introduces a novel approach to examine the scope of tactile sensory perception as a possible modality of treatment of patients suffering from certain mental disorder using a Support Vector Machines with kernelized neural network. Experiments are designed to understand the perceptual difference of schizophrenic patients from normal and healthy subjects with respect to three different touch classes, including soft touch, rubbing, massaging and embracing and their three typical subjective responses. Experiments undertaken indicate that for normal subjects and schizophrenic patients, the average percentage accuracy in classification of all the three classes: pleasant/acceptable/unpleasant is comparable with their respective oral responses. In addition, for schizophrenic patients, the percentage accuracy for acceptable class is very poor of the order of below 12%, which for normal subjects is quite high (42%). Performance analysis reveals that the proposed classifier outperf...
2017 IEEE Congress on Evolutionary Computation (CEC), 2017
We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranki... more We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranking policy, instead of the Pareto ranking, supposing that a solution is unlikely to perform well for all objectives in a MaOO problem. A solution is thus evolved with respect to a specific objective only, which it may proficiently optimize. First, all objectives of the MaOO problem are individually optimized by evolutionary algorithms in parallel. The second step is concerned with judiciously selecting and filtering the quality solutions obtained by individual optimization of all objectives in parallel. A unique ranking policy is proposed to grade the members of the union set of quality solutions based on their extent of optimization of individual objectives. The evolutionary algorithm used for parallel optimization of all objectives in a MaOO here has been realized with differential evolution (DE). The mutation strategy of DE is also amended here with an aim to allow controlled communication between population members, concerned with parallel optimization of different objectives of a MaOO problem. Experiments undertaken with DTLZ and WFG test suits reveal that the proposed algorithm outperforms the state-of-art techniques with respect to inverted generational distance and hypervolume metrics.
2017 International Joint Conference on Neural Networks (IJCNN), 2017
Multi-robot cooperation entails planning by multiple robots for a common objective, where each ro... more Multi-robot cooperation entails planning by multiple robots for a common objective, where each robot/agent actuates upon the environment-based on the sensory information received from the environment. Multi-robot cooperation employing equilibrium-based reinforcement learning is optimal in the sense of system resource (time and/or energy) utilization, because of the prior adaption of the environment by the robots. Unfortunately, robots cannot enjoy such benefit of reinforcement learning in presence of multiple types of equilibria (here Nash equilibrium or correlated equilibrium). In the above perspective, robots need to adapt with a strategy, so that robots can select the optimal equilibrium in each step of the learning. The paper proposes consensus-based multi-agent Q-learning to address the bottleneck of the optimal equilibrium selection among multiple types. An analysis reveals that a consensus (joint action) is coordination type pure strategy Nash equilibrium as well as pure stra...
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
This paper proposes an Artificial Bee Colony optimization based static video summarization techni... more This paper proposes an Artificial Bee Colony optimization based static video summarization technique that represents the synopsis of a video by the most vital frames present in it. First the pixel clusters or Regions of Interest of the video frames which capture the most significant content variations are identified. A novel set of features measured in terms of the average hue values of each of these regions is then used to characterize the frames. Based on these features, the sequence of frames of the video is divided into segments by the Artificial Bee Colony optimization algorithm. The lengths of the segments are so optimized that all the frames of a particular segment have similar features, whereas, the middle frames of different segments are significantly different from each other. These middle frames are treated as the key-frames of the video. Any redundancy present in the selected key-frames is eliminated by comparing their hue histograms. The proposed work is validated on th...
The paper attempts to determine the brain connectivity among different brain regions/lobes during... more The paper attempts to determine the brain connectivity among different brain regions/lobes during subjective engagement in color perception. Traditionally, Granger causality analysis is employed to handle the problem. However, due to the applicability of Granger causality analysis for purely stochastic systems only, its suitability of applications in brain connectivity analysis is limited due to the presence of both deterministic and stochastic characteristics in the human brain. Additionally, because of the high parameter sensitivity of Granger causality technique, identifying the right set of parameters itself poses an additional problem. Convergent cross mapping overcomes the above limitations and thus has immense scope for exploitation in brain connectivity analysis. This paper makes an honest attempt to determine brain connectivity during subjective engagement in color perception using convergent cross mapping technique. Experimental results envisage that the proposed technique...
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
Although there exist recent works on fMRI based cognitive learning, there is a dearth of literatu... more Although there exist recent works on fMRI based cognitive learning, there is a dearth of literature on fNIRs based studies on learning and memory. This paper provides a novel study on the cognitive load detection of subjects engaged in symbol-meaning associative learning tasks from the direct measurement of the hemodynamic response of the brain. The hemodynamic response collected during symbol-meaning associative learning tasks by subjects are pre-processed (filtered from artifacts) for extraction of 112-dimensional features, which are reduced to 20 dimensions by a meta-heuristic optimization algorithm for subsequent transfer to a interval type-2 fuzzy classifier to classify three levels of cognitive loads (High, Low and Moderate) borne by the subjects at different time slots of the learning task. Analysis undertaken reveals that the type-2 fuzzy classifier with the proposed feature selection mechanism has a high performance in classification of the cognitive loads over 89%. Experimental analysis further reveals that the transfer of brain activation from orbitofrontal to ventrolateral prefrontal cortex takes place during transition of cognitive load from high to low. In addition, the activation of dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. These findings would offer justification of inability to handle high cognitive loads by people with under-developed/damaged orbitofrontal and dorsolateral prefrontal cortex.
2017 International Joint Conference on Neural Networks (IJCNN), 2017
Currently, gesture recognition from continuous video sequences is one of the most exciting resear... more Currently, gesture recognition from continuous video sequences is one of the most exciting research areas. This paper proposes a novel HMM-based gesture recognition scheme that can be implemented for developing an improved HCI system capable of providing enhanced performance. This framework explores the high potential of Microsoft's Kinect sensor in gesture recognition by utilizing it in the data acquisition phase. The primary novelty of the work lies in the choice of an active difference signature-based feature descriptor that contains time-warped information in a single sequence over the classically used geometric features. The discussed framework has been tested for 12 distinct gestures embodied by 60 different subjects and it is important to note that for all the gestures the proposed scheme has attained a fairly high recognition rate of nearly 90% which proves the worth of the present work in real time applications. Further, to check the efficacy of the newly formulated framework the performance of the same has been validated against the existing standard technologies.
IEEE Transactions on Cognitive and Developmental Systems, 2019
Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This pap... more Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This paper deals with color perceptual-ability measurement of subjects using brain response to basic color (red, green and blue) stimuli. It also attempts to determine subjective ability to recognize the base colors in presence of noise tolerance of the base colors, referred to as recognition tolerance. Because of intra-and inter-session variations in subjective brain signal features for a given color stimulus, there exists uncertainty in perceptual-ability. In addition, small variations in the color stimulus result in wide variations in brain signal features, introducing uncertainty in perceptual-ability of the subject. Type-2 fuzzy logic has been employed to handle the uncertainty in color perceptual-ability measurements due to a) variations in brain signal features for a given color, and b) the presence of colored noise on the base colors. Because of limited power of uncertainty management of interval type-2 fuzzy sets and high computational overhead of its general type-2 counterpart, we developed a semi-general type-2 fuzzy classifier to recognize the base color. It is important to note that the proposed technique transforms a vertical slice based general type-2 fuzzy set into an equivalent interval type-2 counterpart to reduce the computational overhead, without losing the contributions of the secondary memberships. The proposed semi-general type-2 fuzzy sets induced classifier yields superior performance in classification accuracy with respect to existing type-1, type-2 and other well-known classifiers. The brainunderstanding of a perceived base or noisy base colors is also obtained by exact low resolution electromagnetic topographic analysis (e-LORETA) software. This is used as the reference for our experimental results of the semi-general type-2 classifier in color perceptual-ability detection. Statistical tests undertaken confirm the superiority of the proposed classifier over its competitors. The proposed technique is expected to have interesting applications in identifying people with excellent color perceptual-ability for chemical, pharmaceutical and textile industries.
Due to some diseases or spinal cord injury, sensory, motor and autonomous function for the limb m... more Due to some diseases or spinal cord injury, sensory, motor and autonomous function for the limb movement is completely destructed. BCI (Brain computer Interface) provides a new communication pathway for those patients. Imagination of limb movements is used to operate a BCI. With analysis of acquired EEG signal due to motor imagery controlling of an artificial limb is possible. For this technique motor imagery EEG signal is classified and the classified part is fed to a controller to execute exactly that movement. State feedback PI controller can be used to control an artificial limb. With help of this controller not only position but also velocity can be controlled. In this paper, a simulated model of EEG driven artificial limb control using state feedback PI controller is presented. For this study, EEG data for motor imagery was taken from five healthy subjects. The wavelet coefficients are calculated from that EEG signals as features and the obtained features are classified by QDA classifier to determine the part of the limb the user wants to move. The initial and target position are fed to the controller and the controller move the artificial limb to reach the target position at the classified direction. The overall control procedure is done using Matlab 7.6.
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Papers by Amit Konar