Compressive Sensing for Multimodal Biomedical Signal: A Systematic Mapping and Literature Review

With recent advances in signal processing and very-low-power wireless communications, wireless body sensor networks (WBSNs) are gaining wide popularity. A WBSN consists of multiple miniaturized sensors that are placed on the person's body and are capable of measuring and communicating different physiological signals over time. This study focuses on WBSNs that rely on electroencephalogram (EEG) signals. EEG signals measure the electrical brain activity through a collection of non-invasive wireless sensors placed on a patient's scalp. Two applications are studied: the development of brain computer interfaces (BCIs), and the detection of epileptic seizures. A BCI is a direct interface between the brain and a machine. It can be used for purposes such as helping a patient perform a task by thought only, i.e. without performing any motor actions. In such a case, the BCI has to detect the presence of specific command signals in the EEG signals. A WBSN has the advantage of being minimally obtrusive to the patient. This is because the signals are transmitted wirelessly from the person's body; a person can therefore move freely without worrying about surrounding wires. However, in WBSN applications, the energy available in the battery-powered sensors is limited. Different solutions to minimize the number of computations carried out and the amount of data transmitted by the sensor are therefore highly desired. In this study, we present computationally-efficient data reduction techniques to reduce the energy consumption at the sensor node while keeping the salient information in the EEG signals. To efficiently compress EEG signals at the sensor node, we propose the use of a compressed sensing (CS) framework. The proposed CS scheme is simple, nonadaptive and yields higher energy efficiency than existing frameworks. To obtain a high compression ratio, our CS framework exploits not only the temporal correlation within EEG signals in each channel as is the case in existing frameworks, but also the inter-correlation amongst different EEG channels. When applied to a simple BCI system, our proposed framework resulted in important energy savings (up to 60%) at the expense of a slightly reduced classification accuracy. Existing BCIs require all the EEG signals as input. Therefore, the EEG signals must be reconstructed as perfectly as possible at the receiver side. For seizure detection however, the main aim is not to reconstruct the EEG signals but to detect the occurrence of a seizure. In addition to the above CS technique, we examined different data reduction techniques at the sensor side of an EEG seizure detection system. The extraction and transmission of certain features of the EEG signals were found to yield best results. The performance of these techniques was evaluated based on power consumption and seizure detection efficacy. Experimental results showed that by performing low-complexity feature extraction and transmitting only the features that are pertinent to seizure detection, considerable overall energy is saved. The battery life of the system is increased 14 times relative to the conventional approach of transmitting all the original EEG signals, while the same seizure detection performance is maintained (94.1% sensitivity).

From past few years, the Internet of things (IoT) is an emerging and encouraging technology that has gained prominence in the industries. Due to its increasing usages, a huge amount of data are exchanged within IoT architecture using the internet, which is why privacy and cyber-security are major issues. The heterogeneous nature of various technologies that are combined using IoT makes it problematic to provide security using prescriptive networking. The future of secure IoT depends on privacy issues. The research intends to improve security mechanisms based on intrusion and anomaly detection for IoT using deep learning. In this context, a systematic literature review (SLR) is conducted to identify ‘How to perform data transformation analysis of IoT dataset to detect anomaly detection for cyber IoT attacks? The SLR result found 24 datasets used for IoT analysis, 35 performance metrics to evaluate IoT problems, 6–42 features identified for detection, 42 preprocessing techniques have been used for transforming data, and 26 different methods and models were used to process the given problem. The SLR highlights further enhancement for the issue and identification of cyber-security in IoT. Anomaly detection can be done based on reinforcement deep learning after a thorough analysis of SLR.

Encoding and transmitting multimedia data over low-cost wireless sensors require a trade-off between energy and quality. Compressive Sensing (CS) has been proposed to transfer encoder complexity to the decoder and save energy at the sender, so it has been beneficial for recently developed Internet of Things (IoT) technologies. On the other hand, in practical applications of IoT, such as Low Power and Lossy Networks (LLNs), data is prone to error, which affects the quality at the receiver side. In order to solve this issue, error control mechanisms have been employed, while they are energy-consuming. Therefore, finding an efficient method for error protection in CS encoded videos is crucial in IoT lossy networks. This paper investigates the impact of Unequal Error Protection (UEP) on different frame types for a CS video setting in IoT lossy networks. Experimental results show that making a less complex and energy-efficient encoder can be done by not treating all frames with the same error control mechanism. Instead, having no or less complicated error control methods for non-key (P and B) frames but protecting I-frames is a better solution. Compared to equal error protection for all frame types, our method results in around 20% better SSIM (Structural Similarity Index) for CS video delivery over IoT LLNs.

On the need to update systematic literature reviews

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

Strengthening climate resilience is a global priority, essential for ensuring sustainable development and improving overall well-being. Digital technology and innovation play a pivotal role in enhancing this resilience. This paper aims to examine literature related to the role of technology in supporting climate resilience, as indexed in the Scopus database. A total of 22 articles were reviewed, employing systematic literature review and mapping study methods. The analysis covered publication sources, annual publication trends, contributing countries, research approaches, related issues, and the most cited papers, utilizing Excel 365 for data processing. Additionally, VOSviewer was used to visually analyze keyword occurrences. The review reveals a strong connection between technology and climate resilience, with digital technology being the most prominent focus. Key strategies for strengthening climate resilience include developing tools and methods that facilitate emission reductions, enhance resource efficiency, and improve climate risk management. The findings indicate a growing research trend on climate resilience, predominantly addressing carbon emissions, corporate finance, logistics, drought, asset management, and climate change mitigation. The 22 articles reviewed are categorized into various topics, with digital technology emerging as the most studied area, comprising 6 documents (27.27%). Following this, 4 papers (18.18%) explore the topic of smart agriculture. Other notable topics include energy transition and smart cities, each with 3 papers (13.64%). Additional research themes include green innovation, renewable energy sources, rural water providers, and the role of higher education institutions. Advancing climate resilience necessitates leveraging advanced technologies such as big data, artificial intelligence (AI), the Internet of Things (IoT), and digital twins. These technologies enable real-time analysis and environmental monitoring, fostering better adaptation and mitigation strategies to address the impacts of climate change effectively.

Compressed sensing (CS) is applied in wireless body sensor network (WBSN) to reduce the data rate and minimize the power consumption of the sensor nodes. However, as the CS encoder and decoder are tightly coupled, a model of the overall acquisition chain is required in the first stages of development and validation. To overcome this issue, we propose a virtual prototyping of WBSN based on CS with SystemC-AMS 1.0. The proposed model consists of three sensor nodes which capture electrocardiogram (ECG), electromyogram (EMG) and respiration (RESP) signals. The proposed virtual prototype had allowed a functional verification of WBSN at system level and a rapid exploration of the impact of compression ratio on the quality of reconstruction. Results show how to tailor the measurement matrix for a best tradeoff between the compression ratio, the quality of reconstruction, and the energy consumption.

In this paper, we propose the use of compressed sensing (CS) that is preceded by an energy-efficient, cross-product based independent component analysis (ICA) preprocessing method to efficiently compress electroencephalogram (EEG) signals in the context of a wireless body sensor network (WBSN). In WBSNs, the battery life puts a strict energy constraint at each sensor node. By providing a simple, nonadaptive compression scheme at the sensor nodes, CS offers an efficient solution to compress EEG signals in WBSNs. Through simulations, we demonstrate that our method requires less energy than other state-of-the-art methods using ICA, with a reduction in computations that can reach up to 94%. We also demonstrate that for a fixed compression ratio, the achievable reconstruction error is similar to the state-of-the-art method using ICA, and is much lower than when CS is used alone.

Internet of Medical Things (IoMT) technologies provide suitability among physicians and patients because they are useful in numerous medical fields. Wireless body sensor networks (WBSNs) are one of the most crucial technologies from within the IoMT evolution of the healthcare system, whereby each patient is monitored by low-powered and lightweight sensors. When the WBSNs are integrated into IoMT networks, they are quite likely to overlap each other; thus, cooperation between WBSN sensors is possible. In this paper, we consider communication between WBSNs and beyond their communication range. Therefore, we propose inter-WBAN cooperation for the IoMT system, which is also known as inter-WBAN cooperation in an IoMT environment (IWC-IoMT). In this paper, first, a proposed architecture for the IoT health-based system is investigated. Then, a mathematical model of the outage probability for the IWC-IoMT is derived. Finally, the energy efficiency of the IWC-IoT is analysed and inspected. The simulation and numerical results show that the IWC-IoMT (cooperative IoMT) system provides superior performance compared to the non-cooperative system.

Rapid human motion pose tracking has extensive applications in fields such as motion capture, intelligent monitoring, sports training, and physical health management. It can provide accurate data support, enhance safety monitoring, optimize training outcomes, and promote physical health. Traditional human pose tracking methods predominantly rely on either sensors or images for tracking, which often results in issues like low tracking accuracy and slow tracking speed. To address these problems, a rapid human motion pose tracking method based on improved deep reinforcement learning and multimodal fusion is proposed. First, this paper designs an overall architecture for rapid human motion pose tracking and utilizes a combination of monocular vision and sensors to extract and collect human motion data. Second, it constructs a complementary filter-based multimodal data fusion method to merge the multimodal data and extract the fused features. Finally, a multi-level attention network is employed to enhance the deep reinforcement learning network, using the fused features as input for training to achieve rapid human motion pose tracking. The results show that the proposed method can achieve efficient and stable human motion pose tracking in complex scenes, with a tracking accuracy of up to 85% and a shortest tracking time of 72[Formula: see text]ms, which has practical application value.

In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is further to be improved. In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization algorithm (ROA). With the optimal sensing matrix, the compressed signal is reconstructed using Step Size optimized Sparsity Adaptive Matching Pursuit algorithm (SAMP). The results of this work demonstrate that the optimised CS framework achieves a higher compression rate and probability of reconstruction than the standard CS framework.

On the interplay of Internet of Things and Cloud Computing: A systematic mapping study

The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of literature poses an obstacle to a complete understanding in this area of interest. The methodologies studied were systematic mapping and systematic literature review. For each article, the journal, conference, year of publication, dataset, breast cancer characteristics, PET/CT processing techniques, metrics and diagnostic yield results were identified. Sixty-four articles were analyzed, 44 (68.75%) belong to journals and 20 (31.25%) belong to the conference category. A total of 102 techniques were identified, which were distributed in preprocessing with 7 (6.86%), segmentation with 15 (14.71%), feature extraction with 15 (14.71%), and classification with 65 (63.73%). The techniques with the highest incidence identified in each stage are: Gaussian Filter, SLIC, Local Binary Pattern, and Support Vector Machine with 4, 2, 7, and 35 occurrences, respectively. Support Vector Machine is the predominant technique in the classification stage, due to the fact that Artificial Intelligence is emerging in medical image processing and health care to make expert systems increasingly intelligent and obtain favorable results.

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.

Model driven flexible design of a wireless body sensor network for health monitoring