SwePub - sökning: WFRF:(Ghayvat Hemant)

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1.	Ahmed, Tauheed, et al. (författare) FIMBISAE : A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem 2023 Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:7, s. 6259-6270 Tidskriftsartikel (refereegranskat)abstract Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.
2.	Akbarzadeh, Saeed, et al. (författare) A Simple Fabrication, Low Noise, Capacitive Tactile Sensor for Use in Inexpensive and Smart Healthcare Systems 2022 Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 22:9, s. 9069-9077 Tidskriftsartikel (refereegranskat)abstract Tactile sensors are among the most important devices used in industrial and biomedical fields. Sensors' profiles are significantly affected by their structures and material used. This article presents a robust, low-cost, low noise, accurate and simple fabrication capacitive tactile sensor as a single taxel fabricated on foam. This highly scalable design provides excellent noise immunity, accuracy, and due to a unique printable elastic conductor, it is flexible and stretchable with more than 200% strain. Furthermore, the taxel is based on the capacitive Wheatstone bridge. As a result, noise immunity and stability in case of temperature fluctuation is accomplished. Additionally, the sensor's innovative, simple fabrication, made of Polyurethane foam and printable elastic conductor, allows the system to adapt and achieve relevant results necessary for the purpose of the sensor's application. Therefore, the proposed sensor has potential applications in industrial and biomedical contexts, such as sleep monitoring, etc.
3.	Awais, Muhammad, et al. (författare) Healthcare Professional in the Loop (HPIL) : Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging 2020 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:20 Tidskriftsartikel (refereegranskat)abstract Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
4.	Bhatt, Dulari, et al. (författare) CNN Variants for Computer Vision : History, Architecture, Application, Challenges and Future Scope 2021 Ingår i: Electronics. - : MDPI. - 2079-9292. ; 10:20 Forskningsöversikt (refereegranskat)abstract Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN's components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction.
5.	Borah, Jintu, et al. (författare) AiCareAir : Hybrid-Ensemble Internet-of-Things Sensing Unit Model for Air Pollutant Control 2024 Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 24:13, s. 21558-21565 Tidskriftsartikel (refereegranskat)abstract The detrimental effects on human health caused by air pollution show that being able to predict air quality is a task of utmost significance. The application of artificial intelligence (AI) and the Internet of Things (IoT) is seen as promising in this domain. The performances of state-of-the-art models in terms of prediction accuracy vary with different pollutants and are acceptable only for certain pollutants. This article uses machine learning (ML) and deep learning (DL) models to predict the concentrations of six major air pollutants. Data are collected over eight months with 1400 daily instances from sensors deployed in Kuala Lumpur, Malaysia. As an intelligibly robust system, in this article a hybrid-ensemble model is proposed using a combination of ML models, specifically random forest, K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and neural network (NN) models, namely, long short-term memory (LSTM), gated recurrent units (GRUs), and convolutional NNs (CNNs). Here, a hybrid-ensemble learning model is created using five various ML models as weak learners. In previous ensemble models, a homogeneous group of weak learners are used; however, this work uses a heterogeneous group of weak learners. The prediction accuracy is compared using R2 score, absolute, squared, and root-mean-squared errors (RMSEs).
6.	Borah, Jintu, et al. (författare) AiCareBreath : IoT Enabled Location Invariant Novel Unified Model for Predicting Air Pollutants to Avoid Related Respiratory Disease 2024 Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:8, s. 14625-14633 Tidskriftsartikel (refereegranskat)abstract This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a Light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a Random Forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast dataset of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive dataset containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on RNNs and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.
7.	Cheng, Liu, et al. (författare) EEG-CLNet : Collaborative Learning for Simultaneous Measurement of Sleep Stages and OSA Events Based on Single EEG Signal 2023 Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 72 Tidskriftsartikel (refereegranskat)abstract Sleep-stage and apnea-hypopnea index (AHI) are the most important metrics in the diagnosis of sleep syndrome disease. In previous studies, these two tasks are usually implemented separately, which is both time- and resource-consuming. In this work, we propose a novel single electroencephalogram (EEG)-based collaborative learning network (EEG-CLNet) for simultaneous sleep staging and obstructive sleep apnea (OSA) event detection through multitask collaborative learning. The EEG-CLNet regards different tasks as a common unit to extract features from intragroups via both local parameter sharing and cross-task knowledge distillation (CTKD), rather than just sharing parameters or shortening the distance between different tasks. Our approach has been validated on two datasets with the same or better performance than other methods. The experimental results show that our method achieves a performance gain of 1%-5% compared with the baseline. Compared to previous works where two or even more models were required to perform sleep staging and OSA event detection, the EEG-CLNet could reduce the total number of model parameters and facilitate the model to mine the hidden relationships between different task semantic information. More importantly, it effectively alleviates the task bias problem in hard parameter sharing. As a consequence, this approach has notable potential to be a solution for a lightweight wearable sleep monitoring system in the future.
8.	Ghayvat, Hemant, et al. (författare) AI-enabled radiologist in the loop : novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia 2023 Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35, s. 14591-14609 Tidskriftsartikel (refereegranskat)abstract A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
9.	Ghayvat, Hemant, et al. (författare) AiCarePWP : Deep learning-based novel research for Freezing of Gait forecasting in Parkinson 2024 Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier. - 0169-2607 .- 1872-7565. ; 254 Tidskriftsartikel (refereegranskat)abstract Background and objectives: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention.Method: The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG.Results: Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained.Conclusion: This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.
10.	Ghayvat, Hemant, et al. (författare) CP-BDHCA : Blockchain-Based Confidentiality-Privacy Preserving Big Data Scheme for Healthcare Clouds and Applications 2022 Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 26:5, s. 1937-1948 Tidskriftsartikel (refereegranskat)abstract Healthcare big data (HBD) allows medical stakeholders to analyze, access, retrieve personal and electronic health records (EHR) of patients. Mostly, the records are stored on healthcare cloud and application (HCA) servers, and thus, are subjected to end-user latency, extensive computations, single-point failures, and security and privacy risks. A joint solution is required to address the issues of responsive analytics, coupled with high data ingestion in HBD and secure EHR access. Motivated from the research gaps, the paper proposes a scheme, that integrates blockchain (BC)-based confidentiality-privacy (CP) preserving scheme, CP-BDHCA, that operates in two phases. In the first phase, elliptic curve cryptographic (ECC)-based digital signature framework, HCA-ECC is proposed to establish a session key for secure communication among different healthcare entities. Then, in the second phase, a two-step authentication framework is proposed that integrates Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES), named as HCA-RSAE that safeguards the ecosystem against possible attack vectors. CP-BDAHCA is compared against existing HCA cloud applications in terms of parameters like response time, average delay, transaction and signing costs, signing and verifying of mined blocks, and resistance to DoS and DDoS attacks. We consider 10 BC nodes and create a real-world customized dataset to be used with SEER dataset. The dataset has 30,000 patient profiles, with 1000 clinical accounts. Based on the combined dataset the proposed scheme outperforms traditional schemes like AI4SAFE, TEE, Secret, and IIoTEED, with a lower response time. For example, the scheme has a very less response time of 300 ms in DDoS. The average signing cost of mined BC transactions is 3,34 seconds, and for 205 transactions, has a signing delay of 1405 ms, with improved accuracy of approximate to 12% than conventional state-of-the-art approaches.
11.	Ghayvat, Hemant, et al. (författare) Deep Learning Model for Acoustics Signal Based Preventive Healthcare Monitoring and Activity of Daily Living 2020 Ingår i: <em>2nd International Conference on Data, Engineering and Applications, IDEA 2020</em>. - : IEEE. Konferensbidrag (refereegranskat)abstract To cope with the increasing healthcare costs and nursing shortages in the Aging Society the care system is transferred, as much as possible, to the home environment, making use of ambient assisted living (AAL) monitoring and communication possibilities and to actively involve informal cares to fill in large part of the care that is needed. The proposed system is the AAL based, acoustics sensing system ready to dissect, recognize, and distinguish specific acoustic events occurring in day-by-day life situations, which empowers not only the individual subjects but also the healthcare professionals to remotely follow the status of each individual continuously. This system only processes the background acoustics related to the activity of daily living (ADL) for preventive healthcare. The novel contribution of the research is based on prototype development, audio signal processing algorithms and deep learning algorithms to satisfy the research gap.
12.	Ghayvat, Hemant, et al. (författare) Guest Editorial AIoPT (Artificial Intelligence of Paediatric Things) : Informatics in Meeting Paediatric Needs and Patient Monitoring 2023 Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 27:6, s. 2600-2602 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract Medical (health) informatics broadly encompasses the cognitive, information processing, and communication tasks inherent in medical practice, education, and research, with a particular emphasis on the development of computer-based patient records, decision support systems, information standards, data aggregation systems, communication systems, and educational programs for patients and health providers. In addition, this rapidly growing area is confronted with developing technological solutions sensitive to special populations' specific requirements, i.e., Preventive, Assistive, and Medical Children Health Informatics . First, children have distinct physiology, come from diverse backgrounds, and are disproportionately affected by illnesses. Thus, children are not little adults, as a famous adage among child health experts. These distinctions have been extensively discussed and are frequently called the four D's. Second, children depend on their parents and extended relatives to access necessary health care. Thus, plans must include gathering and distributing information to many patients. Third, childhood is defined by a developmental trajectory marked by fast change and the emergence of capacities for health information utilization. Fourth, children's health is defined by distinct epidemiology characterized by fewer significant chronic diseases, a high prevalence of acute illnesses, and reliance on preventative interventions. Finally, since children are the poorest and most varied in our society, they exhibit distinct demographic trends.
13.	Ghayvat, Hemant, et al. (författare) Healthcare-CT : SoLiD PoD and Blockchain-Enabled Cyber Twin Approach for Healthcare 5.0 Ecosystems 2024 Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:4, s. 6119-6130 Tidskriftsartikel (refereegranskat)abstract The healthcare personals often use stored healthcare data to make crucial decisions, assess risk, and care for patients. The extraction of the required information from the saved healthcare data needs a healthcare ecosystem that can guarantee reliable data delivery. The reliability of cyber-physical data needs to be cross-examined using several sources of data of overlapping nature. The cross-examined data can be saved on blockchain and SOLID POD (SP) to preserve its reliability and privacy. Once the reliable healthcare data is stored on the blockchain and SP, the patients’ medical history can be delivered to data-operated systems to monitor, diagnose, and detect augmented healthcare anomalies. Cyber twins (CT) combine the specific cyber-physical objects with digital tools portraying their actual settings. The creation of a live model for the delivery of healthcare services presents a novel opportunity in patient care comprising better evaluation of risk and assessment without hampering the activities of daily living. The introduction of blockchain technology can improve the notion of CTs by certifying transparency, decentralized data storage, data irreversibility, and person-to-person industrial communication. The storage and exchange of CT data in the healthcare ecosystem depend on disseminated ledgers and decentralized databases for storing and processing data to avoid single point reliance. The present study develops an owner-centric decentralized sharing technique to fulfil the decentralized distribution of CT data.
14.	Ghayvat, Hemant, et al. (författare) ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT) : A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence 2021 Ingår i: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 69 Tidskriftsartikel (refereegranskat)abstract Human movement is a significant factor in extensive spatial-transmission models of contagious viruses. The proposed COUNTERACT system recognizes infectious sites by retrieving location data from a mobile phone device linked with a particular infected subject. The proposed approach is computing an incubation phase for the subject's infection, backpropagation through the subjects’ location data to investigate a location where the subject has been during the incubation period. Classifying to each such site as a contagious site, informing exposed suspects who have been to the contagious location, and seeking near real-time or real-time feedback from suspects to affirm, discard, or improve the recognition of the infectious site. This technique is based on the contraption to gather confirmed infected subject and possibly carrier suspect area location, correlating location for the incubation days. Security and privacy are a specific thing in the present research, and the system is used only through authentication and authorization. The proposed approach is for healthcare officials primarily. It is different from other existing systems where all the subjects have to install the application. The cell phone associated with the global positioning system (GPS) location data is collected from the COVID-19 subjects.
15.	Ghayvat, Hemant, et al. (författare) Revolutionizing healthcare : IoMT-enabled digital enhancement via multimodal ADL data fusion 2024 Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 111 Tidskriftsartikel (refereegranskat)abstract The present research develops a framework to refine the classification of an individual's activities and recognize wellness associated with their routine. The framework improves the accuracy of the classification of routine activities of a person, the activation time data of sensors fixed on objects linked with the routine activities of the person, and the aptness of an incessant activity pattern with the routine activities. The existing techniques need continuous monitoring and are non-adaptive to a person's persistent habitual variations or individualities. The research involves applying Internet of Medical Things (IoMT)-based sensor information fusion to the novel multimodel data analytics to develop Activities of Daily Living (ADL) pattern, behavioral pattern generation and anomaly recognition. The novel multimodel data analytics approach is named AiCareLiving. AicareLiving is an IoMT and artificial intelligence (AI) enabled approach. The research work describes activity data using an individual's activities within a specified area before evaluating the activity data to detect the existence of an anomaly by identifying the deviation of the activity data from the activity profile, which indicates the anticipated behavior and activity of the person. This wellness information would be shared to the caregivers, related healthcare professionals, care providers and municipalities through the secured healthcare information exchange protocol and IoMT. AiCareLiving framework aims to least false positive in terms of anomaly detection and forecasting; the high precision is close to the confidence level of 95%.
16.	Ghayvat, Hemant, et al. (författare) SHARIF : Solid Pod-Based Secured Healthcare Information Storage and Exchange Solution in Internet of Things 2022 Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE. - 1551-3203 .- 1941-0050. ; 18:8, s. 5609-5618 Tidskriftsartikel (refereegranskat)abstract The recent development has enlightened health informatics on the Internet of medical Things (IoT) 5.0. Healthcare services have seen greater acceptance of information and communications technology (ICT) in recent years; in light of the increasing volume of patient data, the traditional way of storing data in physical files has eventually moved to a digital alternative such as electronic health record (EHR). However, conventional healthcare data systems are plagued with a single point of failure, security issues, mutable logging, and inefficient methods to retrieve healthcare records. Social linked data (Solid) has been developed as a decentralized technology to alter digital data sharing and ownership for its users radically. However, Solid alone cannot address all the security issues posed to data exchange and storage. Present research combines two decentralized technologies, Solid ecosystem and blockchain technology, to tackle all potential security issues using solidity-based smart contracts, thereby providing a secure patient-centric design for the complex under developing EHR data exchange.
17.	Ghayvat, Hemant, et al. (författare) Smart aging monitoring and early dementia recognition (SAMEDR) : uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI 2023 Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:33, s. 23739-23751 Tidskriftsartikel (refereegranskat)abstract Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.
18.	Ghayvat, Hemant, et al. (författare) Smart Aging System : Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection 2019 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:4 Tidskriftsartikel (refereegranskat)abstract Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% +/- 0.95) from (80.81% +/- 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).
19.	Ghayvat, Hemant, et al. (författare) STRENUOUS : Edge-Line Computing, AI, and IIoT Enabled GPS Spatiotemporal Data-Based Meta-Transmission Healthcare Ecosystem for Virus Outbreaks Discovery 2023 Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:4, s. 3285-3294 Tidskriftsartikel (refereegranskat)abstract COVID-19 is not the last virus; there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims a HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. Proposed STRENUOUS system discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject’s cross-path can be infected and suggest quarantine followed by testing. The Proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.
20.	Khan, Muhammad Ahmed, et al. (författare) A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery 2023 Ingår i: Frontiers in Neurology. - : Frontiers Media S.A.. - 1664-2295. ; 14 Forskningsöversikt (refereegranskat)abstract Background: Stroke is one of the most common neurological conditions that often leads to upper limb motor impairments, significantly affecting individuals' quality of life. Rehabilitation strategies are crucial in facilitating post-stroke recovery and improving functional independence. Functional Electrical Stimulation (FES) systems have emerged as promising upper limb rehabilitation tools, offering innovative neuromuscular reeducation approaches.Objective: The main objective of this paper is to provide a comprehensive systematic review of the start-of-the-art functional electrical stimulation (FES) systems for upper limb neurorehabilitation in post-stroke therapy. More specifically, this paper aims to review different types of FES systems, their feasibility testing, or randomized control trials (RCT) studies.Methods: The FES systems classification is based on the involvement of patient feedback within the FES control, which mainly includes "Open-Loop FES Systems" (manually controlled) and "Closed-Loop FES Systems" (brain-computer interface-BCI and electromyography-EMG controlled). Thus, valuable insights are presented into the technological advantages and effectiveness of Manual FES, EEG-FES, and EMG-FES systems.Results and discussion: The review analyzed 25 studies and found that the use of FES-based rehabilitation systems resulted in favorable outcomes for the stroke recovery of upper limb functional movements, as measured by the FMA (Fugl-Meyer Assessment) (Manually controlled FES: mean difference = 5.6, 95% CI (3.77, 7.5), P < 0.001; BCI-controlled FES: mean difference = 5.37, 95% CI (4.2, 6.6), P < 0.001; EMG-controlled FES: mean difference = 14.14, 95% CI (11.72, 16.6), P < 0.001) and ARAT (Action Research Arm Test) (EMG-controlled FES: mean difference = 11.9, 95% CI (8.8, 14.9), P < 0.001) scores. Furthermore, the shortcomings, clinical considerations, comparison to non-FES systems, design improvements, and possible future implications are also discussed for improving stroke rehabilitation systems and advancing post-stroke recovery. Thus, summarizing the existing literature, this review paper can help researchers identify areas for further investigation. This can lead to formulating research questions and developing new studies aimed at improving FES systems and their outcomes in upper limb rehabilitation.
21.	Pandya, Sharnil, et al. (författare) Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence 2021 Ingår i: Advanced Engineering Informatics. - : Elsevier. - 1474-0346 .- 1873-5320. ; 47 Tidskriftsartikel (refereegranskat)abstract In recent times, Ambient Assisted Living has emerged as Smart Living. Smart living is a subset of ambient intelligence, which uses the latest technologies, intellectual processes, and ambient intelligent methodologies to enable house residents to live independently with a virtual companion 24 x 7. Typically, these residents are highly engrossed in the daily routine activities that they tend to ignore certain acoustic events attributing them to the white noise caused due to tap water leakage, flush water leakage, the acoustics of door opening/closing, cupboard opening/closing, curtain opening/closing, television, shower, radio, chair and many more. These unattended events lead to a waste of critical energy resources such as electricity, water, and gas and may cause accidents in some cases. For the conducted experiments, a customized dataset termed as "unknown-2000" and ESC-50 has been used, which has more than 2000 audio sound classification samples. The customized dataset is used for the conducted experiments, consisting of various length acoustic events ranging from 2 s to 10 s. In the proposed review, we have identified, analyzed, and evaluated resident acoustic events using Librosa machine learning libraries, texture analysis using LBP methodology, LSTM-CNN, SVM, KNN, LSTM, Bi-LSTM, and Decision Tree-based classification approaches. Furthermore, in the proposed approach, based on the conducted rigorous and detailed analysis, we are also envisioning the prospective ways to enhance smart living concepts by proposing a novel Acoustic Event Detection and Classification System. The investigation results validate the success of the proposed approach. The obtained results indicate that the customized version of the LSTM-CNN based classification approach used in the conducted experiment has outperformed all the other customized classification approaches, such as SVM, KNN-based classification, C4.5 decision tree-based classification, LSTM, and BiLSTM based classification. The LSTM-CNN based classification model has achieved an average value of approximately 0.77 and a standard deviation of 0.2295. Furthermore, the obtained experiential results show that the proposed approach has produced a good performance in various noisy conditions such as SNR0, SNR3, SNR6, SNR9, SNR12, and SNR15. The system classification accuracy has been enhanced to 77% for various acoustic events of a residence. In the end, a detailed comparison of LBP and without LBP approaches has been carried out, which proves that the combination of LBP and LSTM-CNN classification approach provides better results than without the LBP classification approach. The proposed Ambient Acoustic Event Assistive Framework is a costeffective alternative due to the use of low-cost microphone sensors in the conducted experiments.
22.	Pandya, Sharnil, Researcher, 1984-, et al. (författare) COUNTERSAVIOR : AIoMT and IIoT enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics 2023 Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:4, s. 4202-4212 Tidskriftsartikel (refereegranskat)abstract In the current Pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes GPS spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behaviour, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behaviour patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3D tracker movements of individuals, 3D contact analysis of COVID-19 and suspected individuals for 24 hours, forecasting and risk classification of COVID-19, suspected and safe individuals.
23.	Pandya, Sharnil, Researcher, 1984-, et al. (författare) Pollution Weather Prediction System : Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living 2020 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:18 Tidskriftsartikel (refereegranskat)abstract Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
24.	Pandya, Sharnil, Researcher, 1984-, et al. (författare) Smart Home Anti-Theft System : A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol 2018 Ingår i: Applied System Innovation. - : MDPI. - 2571-5577. ; 1 Tidskriftsartikel (refereegranskat)abstract The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.
25.	Patel, Chirag, et al. (författare) DBGC : Dimension-Based Generic Convolution Block for Object Recognition 2022 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:5 Tidskriftsartikel (refereegranskat)abstract The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.
26.	Patel, Chirag I., et al. (författare) Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences 2020 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:24 Tidskriftsartikel (refereegranskat)abstract Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.
27.	Patel, Warish D., et al. (författare) NXTGeUH : LoRaWAN based NEXT Generation Ubiquitous Healthcare System for Vital Signs Monitoring & Falls Detection 2018 Ingår i: <em>1st International Conference on Data Science and Analytics, PuneCon 2018 - Proceedings</em>. - : IEEE. - 9781538672785 Konferensbidrag (refereegranskat)abstract The challenge for deployment of low-cost and high-speed ubiquitous Smart Health services has prompted us to propose new framework design for providing excellent healthcare to humankind. So, there exists a very high demand for developing an Internet of Medical Things (IoMT) based Ubiquitous Real-Time LoRa (Long Range) Healthcare System using Convolutional Neural Networks (CNN) to agree if a sequence of frames contains a person falling. To model the video motion and make the system scenario sovereign, in this research, we use optical flow images as input to the networks. Right now hospital and home falls are a noteworthy medical services concern overall on account of the aging populace. Current observational information, vital signs and falls history give the necessary data identified with the patient's physiology, and movement information give an additional utensil in falls risk evaluation. The proposed framework utilizes Real-Time Vital signs monitoring and emergency alert message to caregivers or doctors. In this context, we introduce "LoRaWAN based Next Generation Ubiquitous Healthcare System (NXTGeUH), an intelligent middleware platform. In addition, this proposed method is evaluated with different public hospital datasets achieving the state-of-The-Art outcomes in all aspects.

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