Search Results (21)

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications. Full article

Continuous cardiovascular monitoring is essential for the early detection of cardiac events, but conventional electrode-based ECG systems cause skin irritation and are unsuitable for long-term wear. We propose an electrode-free ECG monitoring approach that leverages synchronized phonocardiogram (PCG) and seismocardiogram (SCG) signals captured by wireless mechano-acoustic sensors. PCG provides precise valvular event timings, while SCG provides mechanical context, enabling the robust identification of systolic/diastolic intervals and pathological patterns. A deep learning model reconstructs ECG waveforms by intelligently combining mechano-acoustic sensor data. Its architecture leverages specialized neural network components to identify and correlate key cardiac signatures from multimodal inputs. Experimental validation on an IoT sensor dataset yields a mean Pearson correlation of 0.96 and an RMSE of 0.49 mV compared to clinical ECGs. By eliminating skin-contact electrodes through PCG–SCG fusion, this system enables robust IoT-compatible daily-life cardiac monitoring. Full article

Cardiovascular diseases pose a significant global health threat, with atrial fibrillation representing a critical precursor to more severe heart conditions. In this work, a multimodality-based deep learning model has been developed for diagnosing atrial fibrillation using an embedded system consisting of a Raspberry Pi 4B, an ESP8266 microcontroller, and an AD8232 single-lead ECG sensor to capture real-time ECG data. Our approach leverages a deep learning model that is capable of distinguishing atrial fibrillation from normal ECG signals. The proposed method involves real-time ECG signal acquisition and employs a multimodal model trained on the PTB-XL dataset. This model utilizes a multi-step approach combining a CNN–bidirectional LSTM for numerical ECG series tabular data and VGG16 for image-based ECG representations. A fusion layer is incorporated into the multimodal CNN-BiLSTM + VGG16 model to enhance atrial fibrillation detection, achieving state-of-the-art results with a precision of 94.07% and an F1 score of 0.94. This study demonstrates the efficacy of a multimodal approach in improving the real-time diagnosis of cardiovascular diseases. Furthermore, for edge devices, we have distilled knowledge to train a smaller student model, CNN-BiLSTM, using a larger CNN-BiLSTM model as a teacher, which achieves an accuracy of 83.21% with 0.85 s detection latency. Our work represents a significant advancement towards efficient and preventative cardiovascular health management. Full article

Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity. Full article

Bio-Radar (BR) systems have shown great promise for biometric applications. Conventional methods can be forged, or fooled. Even alternative methods intrinsic to the user, such as the Electrocardiogram (ECG), present drawbacks as they require contact with the sensor. Therefore, research has turned towards alternative methods, such as the BR. In this work, a BR dataset with 20 subjects exposed to different emotion-eliciting stimuli (happiness, fearfulness, and neutrality) in different dates was explored. The spectral distributions of the BR signal were studied as the biometric template. Furthermore, this study included the analysis of respiratory and cardiac signals separately, as well as their fusion. The main test devised was authentication, where a system seeks to validate an individual’s claimed identity. With this test, it was possible to infer the feasibility of these type of systems, obtaining an Equal Error Rate (EER) of $3.48\%$ if the training and testing data are from the same day and within the same emotional stimuli. In addition, the time and emotion results dependency is fully analysed. Complementary tests such as sensitivity to the number of users were also performed. Overall, it was possible to achieve an evaluation and consideration of the potential of BR systems for biometrics. Full article

Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications. Full article

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time. Full article

A variety of technologies that could enhance driving safety are being actively explored, with the aim of reducing traffic accidents by accurately recognizing the driver’s state. In this field, three mainstream detection methods have been widely applied, namely visual monitoring, physiological indicator monitoring and vehicle behavior analysis. In order to achieve more accurate driver state recognition, we adopted a multi-sensor fusion approach. We monitored driver physiological signals, electroencephalogram (EEG) signals and electrocardiogram (ECG) signals to determine fatigue state, while an in-vehicle camera observed driver behavior and provided more information for driver state assessment. In addition, an outside camera was used to monitor vehicle position to determine whether there were any driving deviations due to distraction or fatigue. After a series of experimental validations, our research results showed that our multi-sensor approach exhibited good performance for driver state recognition. This study could provide a solid foundation and development direction for future in-depth driver state recognition research, which is expected to further improve road safety. Full article

In this paper, we present a comprehensive assessment of individuals’ mental engagement states during manual and autonomous driving scenarios using a driving simulator. Our study employed two sensor fusion approaches, combining the data and features of multimodal signals. Participants in our experiment were equipped with Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) sensors, allowing us to collect their corresponding physiological signals. To facilitate the real-time recording and synchronization of these signals, we developed a custom-designed Graphical User Interface (GUI). The recorded signals were pre-processed to eliminate noise and artifacts. Subsequently, the cleaned data were segmented into 3 s windows and labeled according to the drivers’ high or low mental engagement states during manual and autonomous driving. To implement sensor fusion approaches, we utilized two different architectures based on deep Convolutional Neural Networks (ConvNets), specifically utilizing the Braindecode Deep4 ConvNet model. The first architecture consisted of four convolutional layers followed by a dense layer. This model processed the synchronized experimental data as a 2D array input. We also proposed a novel second architecture comprising three branches of the same ConvNet model, each with four convolutional layers, followed by a concatenation layer for integrating the ConvNet branches, and finally, two dense layers. This model received the experimental data from each sensor as a separate 2D array input for each ConvNet branch. Both architectures were evaluated using a Leave-One-Subject-Out (LOSO) cross-validation approach. For both cases, we compared the results obtained when using only EEG signals with the results obtained by adding SPR and ECG signals. In particular, the second fusion approach, using all sensor signals, achieved the highest accuracy score, reaching 82.0%. This outcome demonstrates that our proposed architecture, particularly when integrating EEG, SPR, and ECG signals at the feature level, can effectively discern the mental engagement of drivers. Full article

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients’ health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level. Full article

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action. This paper proposes an IoT-based system for remote monitoring and early detection of health problems in home clinical settings. The system comprises three sensor types: MAX30100 for measuring blood oxygen level and heart rate; AD8232 ECG sensor module for ECG signal data; and MLX90614 non-contact infrared sensor for body temperature. The collected data is transmitted to a server using the MQTT protocol. A pre-trained deep learning model based on a convolutional neural network with an attention layer is used on the server to classify potential diseases. The system can detect five different categories of heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion of ventricular, and Unclassifiable beat from ECG sensor data and fever or non-fever from body temperature. Furthermore, the system provides a report on the patient’s heart rate and oxygen level, indicating whether they are within normal ranges or not. The system automatically connects the user to the nearest doctor for further diagnosis if any critical abnormalities are detected. Full article

Cardiovascular diseases require extensive diagnostic tests and frequent physician visits. With the advance in signal processing and sensor technology, now it is possible to acquire vital signs from the human body and process the signal to extract features necessary to primarily diagnose symptoms of cardiovascular disease early. This can help prevent deadly health incidents such as heart attack and or stroke, as well as reduce the number of visits to a health care facility. The proper detection of an elevated ST segment of ECG wave at an early stage may save the patient from having a heart attack or ST elevated myocardial infarction later. The use of a variety of complementary biomedical sensors can lead to a better diagnosis than what is possible when a single sensor is used. This paper proposes a MATLAB GUI which can detect elevated ST segments of ECG waves and use information from a variety of biomedical sensors to bring forth a technique to assess heart health to predict potential heart failure conditions. The proposed technique used fusion among multiple biomedical sensors to reduce the false alarm in diagnosis. Data from the online dataset were used to show the effectiveness and promise of the proposed detection of elevated ST segments and diagnosis techniques using the GUI. Full article

“Smart medical” applications refer to the fusion of technology and medicine that connects all linked sensor equipment with the patients, including those that measure physiological signals, such as blood pressure, pulse, and ECG. In addition, these physiological signal data are highly private and should be safely protected. It takes much longer to complete authentication processes in the traditional way, either based on public key infrastructure or attribute-based encryption, which is a burden for IoT devices. Hence, on the basis of attribute-based encryption, we propose lightweight authentication to shorten the time spent on authentication. Moreover, we use the patients’ data and timestamps as seeds to generate random numbers for authentication. The experiments show that the lightweight authentication using Xeon E3-1230 computer is about 4.45 times faster than complete authentication and 5.8 times faster than complete authentication when using Raspberry Pi. Our proposal significantly improves the disadvantages of IoT devices that lack computing power. Full article

The 12 leads of electrocardiogram (ECG) signals show the heart activities from different angles of coronal and axial planes; hence, the signals of these 12 leads have functional dependence on each other. This paper proposes a novel method for fusing the data of 12-lead ECG signals to diagnose heart problems. In the first phase of the proposed method, the time-frequency transform is employed to fuse the functional data of leads and extract the frequency data of ECG signals in 12 leads. After that, their dependence is evaluated through the correlation analysis. In the second phase, a structural learning method is adopted to extract the structural data from these 12 leads. Moreover, deep convolutional neural network (CNN) models are coded in this phase through genetic programming. These trees are responsible for learning deep structural features from functional data extracted from 12 leads. These trees are upgraded through the execution of the genetic programming (GP) algorithm to extract the optimal features. These two phases are used together to fuse the leads of ECG signals to diagnose various heart problems. According to the test results on ChapmanECG, including the signals of 10,646 patients, the proposed method enjoys the mean accuracy of 97.60% in the diagnosis of various types of arrhythmias in the Chapman dataset. It also outperformed the state-of-the-art methods. Full article

Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system. Full article