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Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors' performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems. [ABSTRACT FROM AUTHOR]

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%. [Display omitted] • IoMT seamlessly integrates multimodal sensors, revolutionizing healthcare through real-time ADL monitoring. • Multimodal ADL data fusion provides comprehensive insights into elderly living patterns and events. • AI algorithms enable predictive analytics, for event generation, attribution score and anomaly detection. • Digital enhancements reduce hospital readmissions by enabling continuous, remote elderly care management. • AI and IoMT algorithms predicted wellness metrics with a 95% confidence level. [ABSTRACT FROM AUTHOR]

It proposes a Hybrid Secure Signcryption Algorithm (HySSA), a small-size block chain (BC), and a planned system that secures an electronic health record (EHR) exchange through enabled device transmissions with minimal encryption and signature overhead. HySSA has two stages of operation. Patients are fitted with proximity sensor nodes (PSNs), which establish a wireless personal area network (WBAN) in the first phase of the procedure. It is up to the nodes to decide which cluster head (CH) in their vicinity can send data to the WBAN's Gateway sensor nodes (GSN) containing EHR meta-data. Second, GSN implements a lightweight signcryption technique for authorized stakeholders that combines data encryption and signing in the second phase of its development. An interplanetary file system provides secure keys for access to the data, which is exchanged over open channels (IPFS). Data mining results are stored to lower computing expenses, and block ledgers are used in global chain architectures. Compared to other schemes, the proposed HySSA scheme is cheaper for transaction and signing expense parameters, throughput of transactions, and computational and communication expenses. It takes HySSA a standard of 3.32 s (s) to sign and 6.52 s (s) to verify in simulation. It takes 3.325 s to mine 200 blocks, compared to 7.8 s for traditional schemes. The throughput of transactions was 142.78 Mbps, as opposed to the standard 102.45 Mbps. Computing time (CC) is 45.80 ms, while communication time (CCM) is 97 bytes, indicating that the suggested approach is competitive with other current approaches in terms of security. [ABSTRACT FROM AUTHOR]

Blockchain-enabled Internet of Medical Things (IoMT) is a secure, transparent, and decentralized network of medical devices which facilitates a patient-centric healthcare system for distributed exchange and management of medical records. The existing consensus protocols designed for a blockchain network such as Proof-of-Work, Proof-of-Stake, Proof-of-Negotiation, Proof-of-Authority, and Proof-of-X-Repute lack fast processing and security. This paper presents a scalable blockchain-enabled IoMT framework that uses gateway nodes to improve the performance. A novel hybrid consensus mechanism namely Proof-of-Context (PoCt) is also proposed for blockchain-enabled IoMT environment. The proposed PoCt consensus is a fast, reliable, and secure mechanism for block mining which consists of a fuzzy-based miner selection and a block validation process with multiple parameters. Fuzzy-based miner selection is done on the basis of context factor of a node, which is computed using the impact score, vote count and trust value. The trusted miner nodes are responsible for block generation and validation. The most trusted and efficient miner node having the highest neighbour density is being selected for block generation. The other miner nodes are responsible for block validation to prevent various malicious activities. The performance analysis of the PoCt consensus shows that it is comparatively 24% more efficient, 36% faster, 1.8% more reliable, and 65% less traffic overhead than fuzzy-based super leader nomination in PoS consensus mechanism, 65% faster than random-voting and game-theory based miner selection, 25% faster than Proof-of-Negotiation consensus, 6.1% more reliable and 58% less traffic overhead than Proof-of-Authority consensus mechanism for the network of 500 nodes. Security analysis of PoCt consensus algorithm ensures its resilience against various known mining attacks. • Development of a patient-centric blockchain-enabled IoMT framework. • Design of a hybrid consensus procedure namely, Proof-of-Context (PoCt). • A fuzzy-based miner selection for PoCt has been proposed. • Security and performance of PoCt consensus has been analyzed. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Medical Things (IoMT) has presented numerous opportunities for the healthcare industry. It is anticipated to enhance the quality and efficiency of medical services, thus enhancing people's overall quality of life. However, frequently occurring medical data leakage makes the protection of medical data and privacy in IoMT become a critical issue. Among the solutions, attribute-based encryption (ABE) has been a very promising solution due to its flexible and fine-grained access control to encrypted data. However, the majority of current ABE schemes are based on bilinear pairing and are vulnerable to quantum attacks. The available of multi-authority ABE schemes over lattice only support a single policy such as threshold or AND gate, and lack the ability to implement user or attribute revocation in a flexible manner. For the special algebra structure of the lattice based scheme, how to overcome them is still a challenge at present. Aiming at the above, we propose a novel multi-authority key-policy attribute based encryption (RM-KP-ABE) based on the Ring Learning With Errors (RLWE) assumption. It supports multi-valued attributes and { 0 , 1 } -LSSS access policy. This scheme allows multiple authorities to participate in key distribution and enables attribute revocation when dynamic users change their situation. { 0 , 1 } -LSSS access policy makes the proposal get highly expressive which supports any monotonic boolean formula. Security analysis and performance evaluations demonstrate that our scheme is secure and efficient. [ABSTRACT FROM AUTHOR]

• Development of an innovative Interfering Input Classification Model (IICM) for remote patient monitoring. • Integration of whale optimization and DQN techniques for improved classification and connectivity identification. • Segregation of sensed data into discrete and continuous categories for enhanced accuracy and efficiency. • Reduction of classification errors and improvement in classification ratio, signal identification, and classification accuracy in remote patient monitoring systems. The Internet of Medical Things (IoMT)-based Remote Patient Monitoring (RPM) systems provide real-time data and insights about patients' conditions without the need for constant physical visits to healthcare facilities. The wearable sensors are responsible for sensing the psychological vitals of humans at different intervals to detect their precise health status. This article introduces an Interfering Input Classification Model (IICM) for addressing the continuous and discrete data extraction issues in RPM. The proposed model hybridizes whale optimization and deep-Q-learning for classification and connectivity identification between different interval data. The sensed information is segregated as discrete and continuous using the search process termination of the whales. Such classified data is validated for the maximum discreteness and its interconnection between the previous sequences. This interconnection is performed using Q-learning based on different state changes through continuous clinical range correlations. The proposed model exemplifies continuous and discrete signals (data) for abnormality detection and diagnosis recommendation. Thus the proposed model is reliable in improving the classification ratio and accuracy with fewer errors. The experimental values of the proposed model are as follows: IICM enhances classification by 6.65%, signal identification by 6.63%, and classification accuracy by 10.08%. This model reduces classification time by 9.51% and error by 10.14% for different ranges of inputs. [ABSTRACT FROM AUTHOR]

• Wearable Internet of Medical Things (IoMT) devices, such as wrist actigraphs, canprovides a more objective and effective approach to early-stage mental health diagnosis. • The methodology includes a custom imputation method, a novel combinatorial sampling method for sample expansion • a multi-branch deep learning architecture is employed with one branch fed with nighttime data and another branch with daytime data • The use of Gradient weighted Class Activation Map (Grad-CAM) technique aids in visualizing and comprehending the model's decision-making process. • The model achieved a commendable accuracy of 0.94 for distinguishing depressive and schizophrenic samples from control samples individually. Deep Learning (DL) holds immense potential in revolutionizing healthcare, offering robust support to clinicians and enhancing patient care. However, coming up with the right DL model is always challenging and depends on quality, quantity, and type of data. In this paper, two motor activity datasets are utilized: " Depresjon" dataset includes activity recordings from 32 healthy individuals (402 days) and 23 individuals with unipolar and bipolar depression (291 days) and the " Psykose" dataset consists of 22 schizophrenia subjects (285 days) and 32 healthy subjects (402 days). The motor activity data, represented as time-series signals, poses challenges due to variable lengths and non-uniform starting timestamps for each subject. Additionally, the daytime and nighttime distributions differ across samples, requiring explicit handling for Convolutional Neural Network models. To address this issue, a multi-branch DL architecture is employed with one branch fed with nighttime data and another with daytime data, capable of capturing features across various scales, accommodating patterns of different sizes. Moreover, the combined outputs of these branches are subjected to a self-attention-mechanism (MultiHeadAttention), which prioritizes essential features. The use of Gradient weighted Class Activation Map (Grad-CAM) technique aids in comprehending the model's decision-making process. The benchmark datasets were used to validate the model, which exhibited an accuracy of 0.94 for both classifying depressive and schizophrenic episodes from control subjects. An accuracy of 0.81 for classifying depressive episodes, and schizophrenics from control samples. This accuracy further increases when combining the control samples from both datasets, to 0.97 for depression and 0.98 for schizophrenia. [ABSTRACT FROM AUTHOR]

The escalating demand for biomedical systems that can precisely diagnose and manage critical diseases underscores the need for innovative solutions. A non-invasive and intelligent Internet of Medical Things (IoMT) system emerges as a promising technology, potentially enabling physicians to assess patients with reduced health risks. The respiratory rate is a pivotal vital sign among the primary clinical assessments. The allure of Radio Frequency (RF) sensing lies in its ability to monitor respiratory patterns without direct contact. However, the practical implementation of such systems often necessitates supplementary hardware to manage the extensive data and radio functionalities, leading to concerns related to cost and feasibility. Software-Defined Radio (SDR) technology presents itself as a viable solution to these challenges. This research introduces a comprehensive framework for the IoMT system, aiming to diagnose respiratory abnormalities early through RF sensing and SDR technology. We employ a deep learning framework and compare its performance with traditional machine learning models to ensure reliable and precise classification of respiratory abnormalities. The achieved results underscore the superiority of deep learning frameworks over conventional machine learning models in classifying respiratory anomalies. Specifically, the deep learning framework exhibits exceptional performance in discerning the temporal dependencies and patterns inherent in respiratory abnormalities, achieving an average accuracy exceeding 98% for each respiratory abnormality classification. [Display omitted] • Proposed innovative IoMT framework, which harnesses the power of Radio Frequency (RF) sensing and Software Defined Radio (SDR) technology to provide an early and contactless diagnosis of respiratory abnormalities, including sleep apnea. This system is designed with the patient's comfort in mind, eliminating the need for invasive sensors or tethered devices. • To Ensure the accuracy and reliability of the data, we employ advanced signal-processing algorithms. These algorithms refine the raw data, eliminating noise and artifacts, thereby enhancing the accuracy of respiratory rate measurements. • The proposed approach is the implementation of a deep learning framework, specifically designed for classifying respiratory patterns. In comparative analyses, this framework consistently outperforms conventional machine learning models, highlighting its effectiveness and potential to revolutionize respiratory health monitoring. [ABSTRACT FROM AUTHOR]

• This paper investigates data fusion in IoMT, its associated issues and potential solutions. • ESDNB algorithm detected epileptic seizures in IoMT networks most accurately, 99.53 % to 99.99 %. • Architecture and security standardization may improve security threat detection. • Cryptography and blockchain technology have shown to be promising ways to improve IoMT security. • Future research could address IoMT system heterogeneity by detecting malware across platforms. The Internet of Medical Things (IoMT) has created a wide range of opportunities for knowledge exchange in numerous industries. The opportunities include patient empowerment, healthcare collaboration, medical education and training, remote monitoring and telemedicine, customized treatment plans, data sharing for innovation, continuous medical learning, supply chain management, public health initiatives, wearable health devices, and quality improvement initiatives. However, the adoption of IoMT faces numerous challenges regarding interoperability, data privacy, security, regulatory, and infrastructure costs. This paper aims to address the implications of data fusion in IoMT, as well as the associated security challenges and their potential solutions, which are lacking in the literature. Data collected from IoMT devices has a direct impact on the accuracy of predictions because of its quality, quantity, and relevance. With an accuracy of 99.53 % to 99.99 %, the Epilepsy seizure detector-based Naive Bayes (ESDNB) algorithm is found to be the most effective for detecting epileptic seizures in IoMT networks. However, the way data are stored must also undergo a major revolution, and all phases—collection, protection, and storage—need to be improved. The standardization of architecture and security measures may improve the detection of security threats and compromises. Methods to detect malware in cross platforms is also an avenue for future research that can effectively tackle the heterogeneity of the IoMT systems. Cryptography and blockchain technology have shown to be promising ways to increase the security of an IoMT-based system. The findings of this review will assist a wide variety of stakeholders in the healthcare ecosystem. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Medical Things (IoMT), medical institutions are accumulating vast amounts of medical data and aiming to utilize this data to train high-quality deep-learning models for assisting doctors in diagnosis. However, due to the highly sensitive nature of medical data, data fusion has always been problematic. Recently, Federated Learning (FL) has gained significant attention as it allows for model training without exposing raw data. Nevertheless, existing research demonstrates that FL still brings the risk of privacy breaches. Furthermore, within the FL framework, a malicious server could potentially provide forged aggregated results, leading to disastrous consequences for the medical system. In this paper, we propose the RFLPV, a robust FL scheme with privacy preservation and verifiable aggregation to enable collaboration among medical institutions and facilitate the fusion of medical data. Specifically, we preserve gradient privacy by adding pairwise masks. Additionally, we design an efficient verification mechanism to tackle the threat posed by a malicious server. Furthermore, we develop a customized local training strategy to accommodate the heterogeneity of medical data. Finally, we demonstrate the superior performance of RFLPV using a real medical dataset. • RFLPV is used to solve the privacy challenges of data fusion. • The gradient masking method is used for privacy improvement. • Homomorphic hashing is used to ensure the correctness of aggregation results. • Experiments show the superior performance of our scheme. [ABSTRACT FROM AUTHOR]

Depression is now a prevalent mental illness and multimodal data-based depression detection is an essential topic of research. Internet of Medical Things devices can provide data resources such as text, audio, and vision, which is valuable for depression detection. Moreover, previous studies have concentrated on using single characteristics of each modality, such as low-dimensional pre-designed features and high-level deep representation, which cannot completely capture the emotional information included in the data. Against this background, we design an intra-modal and inter-modal fusion framework called IIFDD for Corpus-based depression detection. Intra-modal fusion module is designed to integrate low-dimensional pre-designed features and high-dimension deep representation from the same modality for better learning of the semantics information. Then, the inter-modal fusion module is proposed to fuse features from different modalities with attention mechanisms and use the fused result to complete the depression classification. Experiments on two Chinese depression corpus datasets with acoustics, textual, and visual features show that IIFDD can achieve state-of-the-art performance for depression detection. [Display omitted] An illustrative example of depression detection based on Internet of Medical Things (IoMT)-enabled multi-modal information fusion. Internet of Medical Things devices can provide data resources such as text, audio, and vision, which is valuable for depression detection. This work designs an intra-modal and inter-modal fusion framework called IIFDD for Corpus-based depression detection. Intra-modal fusion module is designed to integrate low-dimensional pre-designed features and high-dimension deep representations from the same modality for better learning of semantic information. Then, the inter-modal fusion module is proposed to fuse features from different modalities with attention mechanisms and use the fused result to complete depression classification. • Proposed Intra-modal fusion can bring improvement to depression detection. • The designed improved-MulT performs better than the original MulT. • Considering intra-modal and inter-modal fusion together achieves best performance. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) has emerged as a combination of sensors, healthcare devices, and Internet of Things (IoT) to deliver better and intelligent healthcare services. However, security and privacy protection issues have hindered its wide integration resulting in lack of quality IoMT dataset. Federated Learning (FL) is an emerging distributed learning method that can be helpful for IoMT networks and smart healthcare systems (SHS) due to enhanced security and privacy. The use of intrusion detection systems (IDS) has become vital for contemporary critical networks to protect against evolving cyber-attacks. Existing security models are often challenging and computationally expensive to apply to resource-limited medical IoT devices. To this end, this research work proposes a privacy-preserving FL-based IDS model named Fed-Inforce-Fusion to identify cyber-attacks from IoMT networks. Specifically, the proposed Fed-Inforce-Fusion model uses reinforcement learning technique to learn the latent relationships of medical data. Next, a FL-based system is designed allowing distributed SHS nodes to collaboratively train a comprehensive IDS model in a privacy preserving manner. Then, a fusion/aggregation strategy is adopted to improve the performance of detection model and reduce communication overhead by allowing participating clients to involve in the federation process dynamically. Theoretical study and experiment analysis show that the proposed Fed-Inforce-Fusion is better and effective in detecting complex attack vectors in comparison to existing benchmark IDS methods. Thus, proposing its efficacy and suitability in real-world IoMT networks. • Development of a collaborative IDS design for the detection of intrusions in IoMT networks. • Development of localized models to facilitates data processing at IoMT, enabling productive preservation of data privacy. • Development of reinforcement-based security model to learn latent relationships distinguish complex attack vectors. • Development of a fusion strategy to improve the performance of detection model and reduce communication overhead. • Experimental evaluation on real data validates the superiority of proposed model against compelling benchmark approaches. [ABSTRACT FROM AUTHOR]

The IoT devices with embedded mobile devices create the Internet of Mobile Things (IoMT) paradigm. Mobility is not supported by the routing protocol for low-power and lossy networks (RPL) created for static networks. IoMT has raised routing challenges such as link failure, instability, energy depletion, packet loss, and handover delay in the network. In this context, IoMT Fuzzy-based RPL (IoMT-FRPL) is proposed in this paper to enhance routing performance. Receiving Signal Strength Indicator (RSSI), Euclidean distance, Hop Count, and Expected Transmission Count (ETX) metrics are built into the fuzzy interface system for the mobile nodes in the network to conserve energy. The IoMT-FRPL consists of the following three key steps: The first steps are data transmission and motion investigation, the second is fuzzy-based prediction of a new static parent for the mobile node, and the third is verifying the unique attachment point. When conventional RPL, mRPL, and EMA-RPL were compared to IoMT-performance FRPL's in Cooja/Contiki 2.7, the simulation results revealed improvements in energy consumption, handover delay, packet delivery rate (PDR), and signaling cost. [ABSTRACT FROM AUTHOR]

• A theoretical analysis of IoMT four-layer structure. • An extensive systematic survey of IoMT domains and technologies. • More than 100 references from top journals are analyzed. • Future challenges of IoMT are fully addressed. Internet of Medical Things (IoMT) refers to applying Internet of Things (IoT) into the medical field. The IoMT enables a medical system to connect various smart devices, such as wearable sensors, medical examination instruments, and hospital assets, for establishing an information platform. These smart devices act as the basic nodes in an IoMT system, collecting or generating health data and transmitting the data to the server for further processing and analysis. Physicians apply health data to make better medical decisions. Recently, the IoMT has been widely applied in many areas, including smart hospital, remote health monitoring, disease diagnosis, and infectious disease tracking. In this review, we investigated the IoMT from its concept and theory to its deployment domains, adopted technologies, and applications. We provided theoretical explanations with various examples and more than one hundred representative references. We also presented a cutting-edge discussion for the challenges and directions of the IoMT. We hoped that this systematic review would be beneficial to readers of all levels and backgrounds, including industry beginners, medical institution administrators, policy makers, and experienced researchers. [ABSTRACT FROM AUTHOR]

Multimodal data fusion is a critical element of fall detection systems, as it provides more comprehensive information than single-modal data. Yet, data heterogeneity between sources has posed a challenge for the effective fusion of such data. This paper proposes a novel multimodal data fusion method under a federated learning (FL) framework that addresses the privacy concerns of users while exploiting the complementarity of such data. Specifically, we fuse time-series data from wearable sensors and visual data from cameras at the input level, where the data is first transformed into images using the Gramian Angular Field (GAF) method. Moreover, each user is treated as a private client in the FL system whereby the fall detection model is trained without requiring the sharing of user data. The proposed method is evaluated using the UP-Fall dataset, where we perform different fall detection tasks: binary classification for fall and non-fall detection yields a remarkable accuracy of 99.927%, while multi-classification for different fall activity recognition attains an accurate result of 89.769%. • A method for fusing input-level data is proposed. • A user privacy-preserving FL framework is proposed to ensure data security. • Multimodal data fusion enhances fall detection performance when compared to single-modal data. [ABSTRACT FROM AUTHOR]

Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks. [ABSTRACT FROM AUTHOR]

The current industrial scenario has witnessed the application of several artificial intelligence-based technologies for mining and processing IoMT-based big data. An emerging distributed machine learning paradigm, Federated Learning (FL), has been widely applied in IoMT-based systems as a measure to overcome the issues associated with incorporating AI into such lightweight distributed computing systems while tackling privacy issues as well. However, extensive research has identified that classical FL is still prone to privacy threats due to data leakage and the chances of adversarial attacks during gradient transfer operations. Inspired by these issues, we propose a privacy-preserving framework (F e d _ s e l e c t) that ensures user anonymity in IoMT-based environments for big data analysis under the FL scheme. Fed_Select utilizes alternative minimization to limit gradients and participants in system training to decrease points of system vulnerability. The framework works on an edge computing-based architecture which ensures user anonymity via the employment of hybrid encryption techniques along with added benefits of load reduction at the central server. Also, a Laplacian noise-based differential privacy is employed on the shared attributes for security enhancement that adds confidentiality to the transferred data even during adversarial scenarios. Experimental results on standard datasets showcase that the change in the volume of gradients shared and the number of participants is not proportional to the variation in various system performance parameters. Specifically, an idealistic range of client and gradient-sharing fractions along with the appropriate value of noise for differential privacy implementation is determined. Additionally, we analyze the system from a security perspective as well as compare it with other schemes. • The proposed model addresses privacy preservation issues in FL for IoMT based big data analytics. • Load reduction and user anonymity preservation is attained via Edge computer based multitier FL architecture. • Alternative minimization based partial gradient transfer and limited participant scheme for security enhancement in FL. • Laplacian noise based differential privacy employment for privacy preservation. [ABSTRACT FROM AUTHOR]

• Security of information in internet of medical things systems is paramount. • Authentication mechanism is enhanced by machine learning and blockchain. • Smart contracts verify the valid access to information or revoke it otherwise. • Computational time is reduced when machine learning algorithm is used. Due to the fast-pacing development of technology in the healthcare domain, many problems arise surrounding the security and privacy preservation of medical data. Secure authentication on the Internet of Medical Things (IoMT) is essential. The lack of security in critical and sensitive information of IoMT may lead to high-risk issues in patient privacy. When new data is transmitted from the sensor node, it cannot be assured as authenticated data. Therefore, a blockchain-based system is needed. Such a system allows healthcare providers to access the health records of patients in a more secured authentication-based approach across various network connections. In this paper, a new secure authentication approach using machine learning is proposed. To identify the dynamic time attack detection and authentication in an IoMT environment, this work implements K-Nearest neighbour (KNN) and machine learning using smart contract (KNN-MLSC). It improves security, reduces latency, and maintains health data privacy for both physicians and patients. The accuracy of KNN-MLSC got 0.96 compared with KNN using a smart contract. Also, the results showed that KNN-MLSC has the minimum computation time. [Display omitted] [ABSTRACT FROM AUTHOR]

In the wake of the COVID-19 outbreak, automated disease detection has become a crucial part of medical science given the infectious nature of the coronavirus. This research aims to introduce a deep ensemble framework of transfer learning models for early prediction of COVID-19 from the respective chest X-ray images of the patients. The dataset used in this research was taken from the Kaggle repository having two classes—COVID-19 Positive and COVID-19 Negative. The proposed model achieved high accuracy on the test sample with minimum false positive prediction. It can assist doctors and technicians with early detection of COVID-19 infection. The patient's health can further be monitored remotely with the help of connected devices with the Internet, which may be termed as the Internet of Medical Things (IoMT). The proposed IoMT-based solution for the automatic detection of COVID-19 can be a significant step toward fighting the pandemic. [Display omitted] • An ensemble of transfer learning models is suggested for COVID-19 patient prediction. • State-of-the-art literature on COVID-19 disease prediction is discussed. • The model may be implemented in smart devices for early COVID-19 patient prediction. [ABSTRACT FROM AUTHOR]

Discernible patterns of a person's daily activities can be utilized to detect behavioral symptomatology of mental illness at early stages. Wearable Internet of Medical Things (IoMT) devices with sensors that collect motion data and provide objective measures of physical activity can help to better monitor and detect potential episodes related to the mental health conditions at earlier, more treatable stages. This research puts forward a neuro-symbolic model which uses learnable parameters with integrated knowledge for detection of depression episodes using IoMT based actigraphic input. A novel deep fuzzy model, Depress-DCNF is a hybrid of convolutional neural network (CNN) and an adaptive neuro fuzzy inference system (ANFIS) where CNN is used to extract high-level features from the motor activity recordings which are eventually combined with the discriminative statistical features to produce an optimized feature map. This optimized feature map is finally used to train the ANFIS model which accurately performs the depression classification task. The model is validated on the Depresjon benchmark dataset and compares favorably to state-of-the-art approach giving a superior performance accuracy of 85.10%. • Evaluation of the effectiveness of combination of deep learning extracted features with statistical features in depression assessment. • Design of neuro-symbolic classification model for depression detection based on actigraphic inputs from IoMT. • Exploration of the capabilities of neuro-fuzzy model for handling uncertainties in the motor activity data. • Use of Adaptive Neuro Fuzzy Inference System to better classify the data in extracted feature space. [ABSTRACT FROM AUTHOR]

In this paper, a metamaterial-based resonator is proposed for the detection of dengue infection. The replaceable top layer can measure the dielectric characteristics of blood samples to detect the dengue virus. The sensor observed a shift in the resonant frequency towards higher frequencies with the reduction in the blood's permittivity. The sensor showed a 0.325 GHz shift in resonance per unit change in the dielectric permittivity. To corroborate the sensor's practical sensing capability, a sensor prototype was fabricated and validated with alcohol-aqueous solutions. The sensor exhibited shifting of resonance towards lower frequencies with the addition of water contents in methylated alcohol. With the enhanced sensitivity and repeatable measuring capability, the results suggested that this sensor can be applied with real-time applications on the Internet of Medical Things (IoMT) for early remote detection of the dengue outbreak. [ABSTRACT FROM AUTHOR]

• A fusion-based watermarking algorithm to secure digital medical records. • Objective measures confirm that the strength of the suggested algorithm since it is also suitable for cloud environment. • The suggested algorithm offers better robustness in best case over the traditional schemes. The healthcare digital records include personal and sensitive information about the patient. These records are easily being distributed via different platform for clinical diagnosis purpose. This may lead to compromise with the security of the transmitted records. A fusion-based watermarking algorithm to secure medical records using non-subsampled contourlet transforms (NSCT), fast Walsh-Hadamard transform (FWHT) and Schur-decomposition is proposed in this paper. In the first part of the paper, mark images are fused using the NSCT algorithm, which generate final mark image. In the second part, using NSCT, FWHT and Schur-decomposition hide the fused-mark into the host medical image. The final part is marked data encryption based on hyperchaotic and DNA to provide the integrity and authenticity of the record. Objective measures confirm that the strength of the suggested algorithm since it is also suitable for cloud environment. Further, it is demonstrated that the proposed algorithm has a strong ability to extract hidden mark than conventional ones. [ABSTRACT FROM AUTHOR]

• We develop a robust and secure watermarking algorithm to solve the issue of violation of copyright and integrity of medical records. • Fuzzy optimization scheme is used to compute the optimal embedding strength for the embedding of mark data. • Encryption is considered to enhance the security of our algorithm. • The suggested algorithm is suitable for cloud-based healthcare applications. The healthcare records include personal and sensitive information about the patient. These records are easily distributed via different platform for clinical diagnosis purpose. Presently, large volumes of medical data are stored on cloud platform. However, outsourcing medical data to these popular platforms may introduce security issues. Thus, a secure watermarking algorithm based on integer wavelet transform (IWT)-Schur-Randomized Singular Value Decomposition (RSVD) is put forward for medical records sharing in the cloud environment. To ensure high level of authentication, discrete wavelet transform (DWT) based embedding is conducted to achieve dual watermarking by concealing system MAC address in the logo image. Prior to this, the system address is encoded via turbo code to reduce /eliminate the channel noise, if any. Further, both invisibility and robustness are achieved by fuzzy inference system (FIS), and the marked image is encrypted using chaotic encryption scheme to further improve the security. Furthermore, experimental results is reported and compared with the existing works which demonstrated promising outcomes, indicating its potential towards realization of a value-added tool for smart healthcare applications. [ABSTRACT FROM AUTHOR]

• We have designed a device-to-device direct authentication scheme for IoMT. • In the proposed scheme, each device uses ECC based secure certificate for authentication purposes. • In addition to the provision of traditional security requirements, the proposed scheme resists physical capturing and related attacks. • The proposed scheme is secure under the formal RoR model. The Internet of Medical Things (IoMT) is structured upon both the sensing and communication infrastructure and computation facilities. The IoMT provides the convenient and cheapest ways for healthcare by aiding the remote access to the patients' physiological data and using machine learning techniques for help in diagnosis. The communication delays in IoMT can be very harmful to healthcare. Device to device (D2D) secure communication is a vital area that can reduce communication delays; otherwise, caused due to the mediation of a third party. To substantiate a secure D2D communication framework, some schemes were recently proposed to secure D2D based communication infrastructure suitable for IoMT-based environments. However, the insecurities of some schemes against device physical capture attack and non-provision of anonymity along with related attacks are evident from the literature. This calls for a D2D secure access control system for realizing sustainable smart healthcare. In this article, using elliptic curve cryptography, a certificate based D2D access control scheme for IoMT systems (D2DAC-IoMT) is proposed. The security of the proposed D2DAC-IoMT is substantiated through formal and informal methods. Moreover, the performance analysis affirms that the proposed scheme provides a good trade-off between security and efficiency compared with some recent schemes. [ABSTRACT FROM AUTHOR]

IoMT sensors such as wearables, moodables, ingestible sensors and trackers have the potential to provide a proactive approach to healthcare. But grouping, traversing and selectively tapping the IoMT data traffic and its immediacy makes data management & decision analysis a pressing issue. Evidently, the selection process for real-world, time-constrained health problems involves looking at multivariate time-series data generated simultaneously from various wearables resulting in data overload and accuracy issues. Computational intelligence of edge analytics can extend predictive capability by quickly turning digital biomarker data into actions for remote monitoring and trigger alarm during emergency incidents without relying on backend servers. But the pervasive generation of data streams from IoMT levies significant issues in data visualization and exploratory data analysis. This paper presents a genetically optimized Fuzzy C-means data clustering technique for affective state recognition on the edge. Clustering segregates the biomarker data in chunks and generates a summarized data for each subject which is then genetically optimized to avoid stagnation in local optima. A multi-level convolution neural network is finally used to classify the affective states into the baseline, stress and amusement categories. The model is evaluated on the publicly available WESAD dataset and compares favorably to state-of-the-art with less time complexity. It demonstrates the use of data clustering technique for numerosity reduction of real-time data streams in intelligent edge analytics which facilitates fast analysis of affective state of the user. • Continuous IoMT-based data analysis showcasing computational intelligence on the edge. • Extending predictive capabilities for turning digital biomarker data into actions. • Optimized Fuzzy C-means data clustering for data summarization. • Deep hierarchical model for affective state detection on edge using optimized data. • Validated on publicly available WESAD dataset IoMT biomarker data • Compares favorably to state-of-the-art with less time complexity. [ABSTRACT FROM AUTHOR]

• A novel actigraphy encoding, segmentation and analysis system is proposed for processing vibroarthrography data to identify cartilage degeneration. • The proposed algorithm includes two unique contributions in physiological signal pre-processing and adaptive segmentation. • Two new actigraphy-specific features are introduced in this research which characterize limb movements using accelerometers. • The proposed system in this research work has the potential to be applied in a telehealth framework. Hectic lifestyle coupled sometimes with deficient choices in food and exercise, has led to an increase in the number of individuals being diagnosed with joint generation disorders. In addition, a substantial number of individuals in specialized occupations (such as army) or sports suffer from joint related injuries, thereby leading to surgical replacements and in some severe cases as disability. Standard methods of detecting joint disorders or disabilities include arthroscopy and arthrogram. Vibroarthrography (VAG) is a non-invasive technique wherein the specialist listens to sounds generated from joint movements recorded using actigraphs. In this study, we propose a system which encodes, analyses and segments actigraphy-based VAG data for assessing the severity of cartilage degeneration, and highlighting instances of activity which causes crackling sounds during limb movements. The proposed system encapsulates IoMT (Internet of Medical Things) requirements by providing efficient data compression and analysis at the source. Using an actigraphy dataset from 89 participants, our experiments yielded that the system is able to compress the actigraphy data into 3-bits per sample, thereby reducing the signal size by about 88%, without losing any vital limb movement information. This has been further validated using a simple pattern classification of 3-bit quantized VAG data from participants with healthy and unhealthy knee joints. The method, which yielded a recognition accuracy of 84.6%, as compared to raw VAG data which yielded 80% accurate results. In addition, the proposed adaptive segmentation scheme in the system, leverages the 3-bit encoding by correctly identifying 90% of the segments of interest from each VAG signal. [ABSTRACT FROM AUTHOR]