Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

In the past two decades,there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification.Themajor research areas of this field include object detection and object recognition.Moreover,wireless communication technologies are presently adopted and they have impacted the way of education that has been changed.There are different phases of changes in the traditional system.Perception of three-dimensional(3D)from two-dimensional(2D)image is one of the demanding tasks.Because human can easily perceive but making 3D using software will take time manually.Firstly,the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization.Secondly,the computer labs in schools are now more common than ever.Thirdly,online classes have become a reality.However,transferring to online education or e-learning is not without challenges.Therefore,we propose a method for improving the efficiency of e-learning.Our proposed system consists of twoand-a-half dimensional(2.5D)features extraction using machine learning and image processing.Then,these features are utilized to generate 3D mesh using ellipsoidal deformation method.After that,3D bounding box estimation is applied.Our results show that there is a need to move to 3D virtual reality(VR)with haptic sensors in the field of e-learning for a better understanding of real-world objects.Thus,people will have more information as compared to the traditional or simple online education tools.We compare our result with the ShapeNet dataset to check the accuracy of our proposed method.Our proposed system achieved an accuracy of 90.77%on plane class,85.72%on chair class,and car class have 72.14%.Mean accuracy of our method is 70.89%.

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Crowd management becomes a global concern due to increased population in urban areas.Better management of pedestrians leads to improved use of public places.Behavior of pedestrian’s is a major factor of crowd management in public places.There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment.In this paper,we have proposed a new method for pedestrian’s behavior detection.Kalman filter has been used to detect pedestrian’s usingmovement based approach.Next,we have performed occlusion detection and removal using region shrinking method to isolate occluded humans.Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes.Gray Wolf Optimizer(GWO)has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient(XG)Boost classifier.Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets,where both have different environment.The mean achieved accuracies are 91.3%and 85.14%over the Avenue and UBI-Fight datasets,respectively.These results are more accurate as compared to other existing methods.

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

A New Multi Chaos-Based Compression Sensing Image Encryption

The advancements in technology have substantially grown the size of image data.Traditional image encryption algorithms have limited capabilities to deal with the emerging challenges in big data,including compression and noise toleration.An image encryption method that is based on chaotic maps and orthogonal matrix is proposed in this study.The proposed scheme is built on the intriguing characteristics of an orthogonal matrix.Gram Schmidt disperses the values of pixels in a plaintext image by generating a random orthogonal matrix using logistic chaotic map.Following the diffusion process,a block-wise random permutation of the data is performed using multi-chaos.The proposed scheme provides sufficient security and resilience to JPEG compression and channel noise through a series of experiments and security evaluations.It enables Partial Encryption(PE)for faster processing as well as complete encryption for increased security.The higher values of the number of pixels change rates and unified average change intensity confirm the security of the encryption scheme.In contrast to other schemes,the proposed approach can perform full and partial encryption depending on security requirements.

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%.

Automatic Anomaly Monitoring in Public Surveillance Areas

With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classification also increases.Although it has multiple applications,the problem is very challenging.In this paper,a novel approach for detecting nor-mal/abnormal activity has been proposed.We used the Gaussian Mixture Model(GMM)and Kalmanfilter to detect and track the objects,respectively.After that,we performed shadow removal to segment an object and its shadow.After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded humans.Fuzzy c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalflow are extracted for each identified silhouettes.Gray Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classification that is performed using the XG-Boost classifier.This system is applicable in any surveillance appli-cation used for event detection or anomaly detection.Performance of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of anomaly.The mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%respec-tively.These results are more accurate as compared to other existing methods.

Competitive Swarm Optimization with Encryption Based Steganography for Digital Image Security

Digital image security is a fundamental and tedious process on shared communication channels.Several methods have been employed for accomplishing security on digital image transmission,such as encryption,steganography,and watermarking.Image stenography and encryption are commonly used models to achieve improved security.Besides,optimal pixel selection process(OPSP)acts as a vital role in the encryption process.With this motivation,this study designs a new competitive swarmoptimization with encryption based stenographic technique for digital image security,named CSOES-DIS technique.The proposed CSOES-DIS model aims to encrypt the secret image prior to the embedding process.In addition,the CSOES-DIS model applies a double chaotic digital image encryption(DCDIE)technique to encrypt the secret image,and then embedding method was implemented.Also,the OPSP can be carried out by the design of CSO algorithm and thereby increases the secrecy level.In order to portray the enhanced outcomes of the CSOES-DIS model,a comparative examination with recent methods is performed and the results reported the betterment of the CSOES-DIS model based on different measures.

VacChain:A Blockchain-Based EMR System to Manage Child Vaccination Records

The digitalization of healthcare-related information service systems has become a trend across the world.However,several crucial services are still provided manually due to a lack of trust in digital solutions.One such service is keeping records of children’s vaccination,which still relies on a paper-based file system in most parts of the world.This approach causes serious data integrity problems.Recently,healthcare has become a potential application area of the blockchain,as it can preserve and protect highly sensitive private medical records while sharing these records in a decentralized manner without losing personal ownership.Therefore,we propose a new digital model to track a child’s vaccination records using blockchain.In particular,this proposed application helps improve the vaccination record-keeping process by ensuring the integrity of the preserved data in a more secure way.In an emerging pandemic situation,our approach can be extended to manage the overall vaccination process effectively.

Statistical Analysis with Dingo Optimizer Enabled Routing for Wireless Sensor Networks

Security is a vital parameter to conserve energy in wireless sensor networks(WSN).Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission.But the available routing techniques do not involve security in the design of routing techniques.This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme(SADO-RRS)for WSN.The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN.In addition,the presented SADORRS technique derives a new statistics based linear discriminant analysis(LDA)for attack detection,Moreover,a trust based dingo optimizer(TBDO)algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN.Besides,the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN.For demonstrating the enhanced outcomes of the SADO-RRS technique,a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.

A Secure and Lightweight Chaos Based Image Encryption Scheme

In this paper,we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm.The method works on the principle of confusion and diffusion.The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes.Initially,an image(red,green,and blue components)is partitioned into blocks with an equal number of pixels.Each block is then processed with Tinkerbell Chaotic Map(TBCM)to get shuffled pixels and shuffled blocks.Composite Fractal Function(CFF)change the value of pixels of each color component(layer)to obtain a random sequence.Through the obtained random sequence,three layers of plain image are encrypted.Finally,with each encrypted layer,Brownian Particles(BP)are XORed that added an extra layer of security.The experimental tests including a number of statistical tests validated the security of the presented scheme.The results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature.

Intelligent Sign Language Recognition System for E-Learning Context

In this research work,an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines.This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way.The hand gesture recognition based on these features has been implemented for usage in real-time.The feature set used hand landmarks,which were generated using media-pipe(MediaPipe)and open computer vision(openCV)on each frame of the incoming video.The overall algorithm has been tested on two well-known ASLalphabet(American Sign Language)and ISL-HS(Irish Sign Language)sign language datasets.Different machine learning classifiers including random forest,decision tree,and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been compared.Since the random forest classifier performed better,it has been selected as the base classifier for the proposed system.It showed 96.7%accuracy with ISL-HS and 93.7%accuracy with ASL-alphabet dataset using the extracted features.

Pedestrian Physical Education Training Over Visualization Tool

E-learning approaches are one of the most important learning platforms for the learner through electronic equipment.Such study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods.Many of them are currently working on this domain to fulfill the requirements of industry and the environment.In this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural network.For input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is applied.Deep flow features are extracted to find more accurate information.Furthermore,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior mining.The proposed method shows 84.50%of mean accuracy and a 15.50%of error rate.Therefore,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).

Self-Care Assessment for Daily Living Using Machine Learning Mechanism

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders.Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity recognition.In this paper,an RGB-D-based ADL recognition system has been presented.Initially,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.Based on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)algorithm.Finally,random forest classifier has been used to classify activities into different categories.The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or offices.Hence,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of patients.We propose a robust system comprising both RGB(red,green,blue)and depth information.In particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map.To track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector quantization.Then,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for classification.There are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity dataset.Furthermore,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI datasets.The results proved the validity of the proposed system.

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

The Impact of Check Bits on the Performance of Bloom Filter

Bloom filter(BF)is a space-and-time efficient probabilistic technique that helps answermembership queries.However,BF faces several issues.The problems with traditional BF are generally two.Firstly,a large number of false positives can return wrong content when the data is queried.Secondly,the large size of BF is a bottleneck in the speed of querying and thus uses large memory.In order to solve the above two issues,in this article,we propose the check bits concept.From the implementation perspective,in the check bits approach,before saving the content value in the BF,we obtain the binary representation of the content value.Then,we take some bits of the content value,we call these the check bits.These bits are stored in a separate array such that they point to the same location as the BF.Finally,the content value(data)is stored in the BF based on the hash function values.Before retrieval of data from BF,the reverse process of the steps ensures that even if the same hash functions output has been generated for the content,the check bits make sure that the retrieval does not depend on the hash output alone.This thus helps in the reduction of false positives.In the experimental evaluation,we are able to reduce more than 50%of false positives.In our proposed approach,the false positives can still occur,however,false positives can only occur if the hash functions and check bits generate the same value for a particular content.The chances of such scenarios are less,therefore,we get a reduction of approximately more than 50%false positives in all cases.We believe that the proposed approach adds to the state of the art and opens new directions as such.

Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the game.Further,the sensor data is sent to a machine learning model,which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational sector.Extensive experimentation proved the validity of the proposed system.

A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)etc.Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way.Instead of the classical random walk,this paper proposes the quantum walk to achieve high image security.Classical random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave functions.The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity etc.All experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally efficient.In the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively.

An Intelligent Framework for Recognizing Social Human-Object Interactions

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.