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Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.

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Flexible strain sensors have been widely used in wearable electronics. However, the fabrication of flexible strain sensors with a large strain detection range, high sensitivity, and negligible hysteresis remains a formidable challenge, even after enormous advancements in the field. Herein, a flexible microfluidic strain sensor was fabricated by filling poly(3,4-ethylenedioxythiophene):polystyrenesulfonate-MXene-gold (PEDOT:PSS-MXene-Au) nanocomposites into microchannels in an elastic matrix. Owing to the unique properties of the nanofiller and Ecoflex elastomer microchannel, the microfluidic strain sensor detected a strain of 0%-500% with low hysteresis (2.4%), high sensitivity (guage factor = 25.4), short response times (∼86 ms), and good durability. Moreover, the flexible microfluidic sensor was used to detect various physiological signals and human activities, control a mechanical hand, and capture hand motions in real time. As demonstrated by its good performance, the proposed flexible microfluidic sensor holds great potential in applications such as wearable electronics, physiological signal monitoring and human-machine interactions.

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INTRODUCTION: Fatigue is considered to have a life-threatening effect on human health and it has been an active field of research in different sectors. Deploying wearable physiological sensors helps to detect the level of fatigue objectively without any concern of bias in subjective assessment and interfering with work. METHODS: This paper provides an in-depth review of fatigue detection approaches using physiological signals to pinpoint their main achievements, identify research gaps, and recommend avenues for future research. The review results are presented under three headings, including: signal modality, experimental environments, and fatigue detection models. Fatigue detection studies are first divided based on signal modality into uni-modal and multi-modal approaches. Then, the experimental environments utilized for fatigue data collection are critically analyzed. At the end, the machine learning models used for the classification of fatigue state are reviewed. PRACTICAL APPLICATIONS: The directions for future research are provided based on critical analysis of past studies. Finally, the challenges of objective fatigue detection in the real-world scenario are discussed.

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BACKGROUND: Patients with respiratory or cardiovascular diseases often experience higher rates of hospital readmission due to compromised heart-lung function and significant clinical symptoms. Effective measures such as discharge planning, case management, home telemonitoring follow-up, and patient education can significantly mitigate hospital readmissions. OBJECTIVE: This study aimed to determine the efficacy of home telemonitoring follow-up in reducing hospital readmissions, emergency department (ED) visits, and total hospital days for high-risk postdischarge patients. METHODS: This prospective cohort study was conducted between July and October 2021. High-risk patients were screened for eligibility and enrolled in the study. The intervention involved implementing home digital monitoring to track patient health metrics after discharge, with the aim of reducing hospital readmissions and ED visits. High-risk patients or their primary caregivers received education on using communication measurement tools and recording and uploading data. Before discharge, patients were familiarized with these tools, which they continued to use for 4 weeks after discharge. A project manager monitored the daily uploaded health data, while a weekly video appointment with the program coordinator monitored the heart and breathing sounds of the patients, tracked health status changes, and gathered relevant data. Care guidance and medical advice were provided based on symptoms and physiological signals. The primary outcomes of this study were the number of hospital readmissions and ED visits within 3 and 6 months after intervention. The secondary outcomes included the total number of hospital days and patient adherence to the home monitoring protocol. RESULTS: Among 41 eligible patients, 93% (n=38) were male, and 46% (n=19) were aged 41-60 years, while 46% (n=19) were aged 60 years or older. The study revealed that home digital monitoring significantly reduced hospitalizations, ED visits, and total hospital stay days at 3 and 6 months after intervention. At 3 months after intervention, average hospitalizations decreased from 0.45 (SD 0.09) to 0.19 (SD 0.09; P=.03), and average ED visits decreased from 0.48 (SD 0.09) to 0.06 (SD 0.04; P<.001). Average hospital days decreased from 6.61 (SD 2.25) to 1.94 (SD 1.15; P=.08). At 6 months after intervention, average hospitalizations decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.01), and average ED visits decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.02). Average hospital days decreased from 7.48 (SD 2.32) to 6.03 (SD 3.12; P=.73). CONCLUSIONS: By integrating home telemonitoring with regular follow-up, our research demonstrates a viable approach to reducing hospital readmissions and ED visits, ultimately improving patient outcomes and reducing health care costs. The practical application of telemonitoring in a real-world setting showcases its potential as a scalable solution for chronic disease management.

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This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

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The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students' physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%.

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Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

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Bioelectronics is an exciting field that bridges the gap between physiological activities and external electronic devices, striving for high resolution, high conformability, scalability, and ease of integration. One crucial component in bioelectronics is bioelectrodes, designed to convert neural activity into electronic signals or vice versa. Previously reported bioelectrodes have struggled to meet several essential requirements simultaneously: high-fidelity signal transduction, high charge injection capability, strain resistance, and multifunctionality. This work introduces a novel strategy for fabricating superior bioelectrodes by merging multiple charge-transfer processes. The resulting bioelectrodes offer accurate ion-to-electron transduction for capturing electrophysiological signals, dependable charge injection capability for neuromodulation, consistent electrode potential for artifact rejection and biomolecule sensing, and high transparency for seamless integration with optoelectronics. Furthermore, the bioelectrode can be designed to be strain-insensitive by isolating signal transduction from electron transportation. The innovative concept presented in this work holds great promise for extending to other electrode materials and paves the way for the advancement of multimodal bioelectronics.

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Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.

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Physiological Signals like Electromography (EMG) and Electroencephalography (EEG) can be analysed and decoded to provide vital information that can be used in a range of applications like rehabilitative robotics and remote device control. The process of acquiring and using these signals requires many compute-intensive tasks like signal acquisition, signal processing, feature extraction, and machine learning. Performing these activities on a PC-based system with well-established software tools like Python and Matlab is the first step in designing solutions based upon these signals. In the application domain of rehabilitative robotics, one of the main goals is to develop solutions that can be deployed for the use of individuals who need them in improving their Acitivities-for-Daily Living (ADL). To achieve this objective, the final solution must be deployed onto an embedded solution that allows high portability and ease-of-use. Porting a solution from a PC-based environment onto a resource-constraint one such as a microcontroller poses many challenges. In this research paper, we propose the use of an ARM-based Corex M-4 processor. We explore the various stages of the design from the initial testing and validation, to the deployment of the proposed algorithm on the controller, and further investigate the use of Cepstrum features to obtain a high classification accuracy with minimal input features. The proposed solution is able to achieve an average classification accuracy of 95.34% for all five classes in the EMG domain and 96.16% in the EEG domain on the embedded board.

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Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen's Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.

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This review summarizes recent progress in developing wireless, batteryless, fully implantable biomedical devices for real-time continuous physiological signal monitoring, focusing on advancing human health care. Design considerations, such as biological constraints, energy sourcing, and wireless communication, are discussed in achieving the desired performance of the devices and enhanced interface with human tissues. In addition, we review the recent achievements in materials used for developing implantable systems, emphasizing their importance in achieving multi-functionalities, biocompatibility, and hemocompatibility. The wireless, batteryless devices offer minimally invasive device insertion to the body, enabling portable health monitoring and advanced disease diagnosis. Lastly, we summarize the most recent practical applications of advanced implantable devices for human health care, highlighting their potential for immediate commercialization and clinical uses.

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The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.

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This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p<0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p>0.05). There was a significant difference between ovulating and non-ovulating cycles (p<0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (µS), respectively.

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BACKGROUND: Physiological signals such as heart rate and electrodermal activity can provide insight into an individual's mental state, which are invaluable information for mental health care. Using recordings of physiological signals from wearable devices in the wild can facilitate objective monitoring of symptom severity and evaluation of treatment progress. OBJECTIVE: We designed a study to evaluate the feasibility of predicting obsessive-compulsive disorder (OCD) events from physiological signals recorded using wrist-worn devices in the wild. Here, we present an analysis plan for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. METHODS: In total, 18 children and adolescents aged between 8 and 16 years were included in this study. Nine outpatients with an OCD diagnosis were recruited from a child and adolescent mental health center. Nine youths without a psychiatric diagnosis were recruited from the catchment area. Patients completed a clinical interview to assess OCD severity, types of OCD, and number of OCD symptoms in the clinic. Participants wore a biosensor on their wrist for up to 8 weeks in their everyday lives. Patients were asked to press an event tag button on the biosensor when they were stressed by OCD symptoms. Participants without a psychiatric diagnosis were asked to press this button whenever they felt really scared. Before and after the 8-week observation period, participants wore the biosensor under controlled conditions of rest and stress in the clinic. Features are extracted from 4 different physiological signals within sliding windows to predict the distress event logged by participants during data collection. We will test the prediction models within participants across time and multiple participants. Model selection and estimation using 2-layer cross-validation are outlined for both scenarios. RESULTS: Participants were included between December 2021 and December 2022. Participants included 10 female and 8 male participants with an even sex distribution between groups. Patients were aged between 10 and 16 years, and adolescents without a psychiatric diagnosis were between the ages of 8 and 16 years. Most patients had moderate to moderate to severe OCD, except for 1 patient with mild OCD. CONCLUSIONS: The strength of the planned study is the investigation of predictions of OCD events in the wild. Major challenges of the study are the inherent noise of in-the-wild data and the lack of contextual knowledge associated with the recorded signals. This preregistered analysis plan discusses in detail how we plan to address these challenges and may help reduce interpretation bias of the upcoming results. If the obtained results from this study are promising, we will be closer to automated detection of OCD events outside of clinical experiments. This is an important tool for the assessment and treatment of OCD in youth. TRIAL REGISTRATION: ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/study/NCT05064527. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48571.

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This study aimed to systematically review the application and research progress of flexible microfluidic wearable devices in the field of sports. The research team thoroughly investigated the use of life signal-monitoring technology for flexible wearable devices in the domain of sports. In addition, the classification of applications, the current status, and the developmental trends of similar products and equipment were evaluated. Scholars expect the provision of valuable references and guidance for related research and the development of the sports industry. The use of microfluidic detection for collecting biomarkers can mitigate the impact of sweat on movements that are common in sports and can also address the issue of discomfort after prolonged use. Flexible wearable gadgets are normally utilized to monitor athletic performance, rehabilitation, and training. Nevertheless, the research and development of such devices is limited, mostly catering to professional athletes. Devices for those who are inexperienced in sports and disabled populations are lacking. Conclusions: Upgrading microfluidic chip technology can lead to accurate and safe sports monitoring. Moreover, the development of multi-functional and multi-site devices can provide technical support to athletes during their training and competitions while also fostering technological innovation in the field of sports science.

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To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi-omics signal-level perspective. A physiological signal fusion matrix and fusion map, which can estimate the blood pressure of blood loss(BPBL), are constructed. The results demonstrate the efficacy of the fusion map model, with correlation values of 0.988 and 0.991 between the estimated BPBL and the true systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The root mean square errors for SBP and DBP were 3.21 mmHg and 3.00 mmHg, respectively. The model validation showed that the fusion map method is capable of simultaneous highlighting of the respective characteristics of ECG and PPG and their correlation, improving the utilization of the information and the accuracy of BPBL. This article validates that physiological signal fusion map can effectively improve the accuracy of BPBL estimation and provides a new perspective for the field of physiological information fusion.

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Epidermal electronics, an emerging interdisciplinary field, is advancing the development of flexible devices that can seamlessly integrate with the skin. These devices, especially Electric Double Layer (EDL)-based sensors, overcome the limitations of conventional electronic devices, offering high sensitivity, rapid response, and excellent stability. Especially, Electric Double Layer (EDL)-based epidermal sensors show great potential in the application of wearable electronics to detect biological signals due to their high sensitivity, fast response, and excellent stability. The advantages can be attributed to the biocompatibility of the materials, the flexibility of the devices, and the large capacitance due to the EDL effect. Furthermore, we discuss the potential of EDL epidermal electronics as wearable sensors for health monitoring and wound healing. These devices can analyze various biofluids, offering real-time feedback on parameters like pH, temperature, glucose, lactate, and oxygen levels, which aids in accurate diagnosis and effective treatment. Beyond healthcare, we explore the role of EDL epidermal electronics in human-machine interaction, particularly their application in prosthetics and pressure-sensing robots. By mimicking the flexibility and sensitivity of human skin, these devices enhance the functionality and user experience of these systems. This review summarizes the latest advancements in EDL-based epidermal electronic devices, offering a perspective for future research in this rapidly evolving field.

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Introduction: In the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO2) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO2) reflects changes in arterial CO2 and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO2 recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO2 led to suggested possible analytical models, they have not been widely applied. Methods: In this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state. Results: We demonstrate that the one-to-one mapping between respiration and CO2 recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO2. Moreover, dynamic PETCO2 can be successfully derived from the predicted CO2, achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO2 prediction. Discussion: Our results demonstrate that dynamic CO2 can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing.

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Emotions play a pivotal role in human cognition, exerting influence across diverse domains of individuals' lives. The widespread adoption of artificial intelligence and machine learning has spurred interest in systems capable of automatically recognizing and classifying emotions and affective states. However, the accurate identification of human emotions remains a formidable challenge, as they are influenced by various factors and accompanied by physiological changes. Numerous solutions have emerged to enable emotion recognition, leveraging the characterization of biological signals, including the utilization of cardiac signals acquired from low-cost and wearable sensors. The objective of this work was to comprehensively investigate the current trends in the field by conducting a Systematic Literature Review (SLR) that focuses specifically on the detection, recognition, and classification of emotions based on cardiac signals, to gain insights into the prevailing techniques employed for signal acquisition, the extracted features, the elicitation process, and the classification methods employed in these studies. A SLR was conducted using four research databases, and articles were assessed concerning the proposed research questions. Twenty seven articles met the selection criteria and were assessed for the feasibility of using cardiac signals, acquired from low-cost and wearable devices, for emotion recognition. Several emotional elicitation methods were found in the literature, including the algorithms applied for automatic classification, as well as the key challenges associated with emotion recognition relying solely on cardiac signals. This study extends the current body of knowledge and enables future research by providing insights into suitable techniques for designing automatic emotion recognition applications. It emphasizes the importance of utilizing low-cost, wearable, and unobtrusive devices to acquire cardiac signals for accurate and accessible emotion recognition.