Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

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.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

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.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring.

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.

[Multi-modal physiological time-frequency feature extraction network for accurate sleep stage classification].

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.

Messing up to clean up: Semi-randomized frequency selective space-filling curves to suppress physiological signal fluctuations in MRI.

PURPOSE: Rapid 3D steady-state sequences are widely used but are also known to be sensitive to semi-periodic physiological signal fluctuations due to, for example, cardiac pulsation, breathing, and eye/eyelids movement. This semi-periodicity results in repeating artifacts in the image whose intensity depends on the scan parameters. The purpose of this study is to design a reordering of the 2D phase encodes (within the 3D acquisition) that reduces these artifacts. METHODS: A randomized order of the phase encodes can suppress repeating artifact but may also introduce its own apparent noise, for example, in cases of slow subject movement or gradual changes in eddy currents. In a new design a semi-randomized space-filling curve is generated by scrambling the local order of the phase encodes to achieve a controlled frequency selective effect, that is, eliminating artifacts above a chosen (fluctuation) frequency threshold while leaving lower frequencies untouched, thus overcoming the limitations of a randomized order. The method was characterized in simulations and substantiated by human brain imaging at 7 T using two steady-state gradient echo variants that suffer from pulsation, either near blood vessels or near the ventricles. RESULTS: The simulations with a point source show that the maximum artifact intensity can be reduced by factors of 10-50 depending on the scan parameters. In human scanning, the new approach drastically reduced physiologically induced artifacts and was superior in this regard to both full randomization and a generalized Hilbert curve, another semi-randomized approach. CONCLUSION: The phase-encodes reordering presented here effectively removes artifacts arising from local fluctuations.

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

[Design of flexible wearable sensing systems].

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.

Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home.

Due to the recent COVID-19 pandemic, many people have faced in-home isolation, as every suspected patient must stay at home. The behavior of such isolated people needs to be monitored to ensure that they are staying at home. Using a camera is a very practical method. However, smart bracelets are more convenient when personal privacy is a concern or when the blood oxygen value or heart rate must be monitored. In this study, a low-cost indoor positioning system that uses a Bluetooth beacon, a smart bracelet, and an embedded system is proposed. In addition to monitoring whether a person living alone is active in a specific environment and tracking the heart rate or blood oxygen value under particular conditions, this system can also send early warning signals to specific observation units or relatives through instant messaging software.

[A study on the effect evaluation of virtual reality on workplace employees' emotional optimization].

Objective: To explore the effect of emotional optimization of workplace employees in immersive virtual natural environment. Methods: In July 2020, 15 subjects were selected to complete two groups of treadmill walking training experiments in virtual natural environment and daily environment respectively. At the same time, the subjects' skin electrical (EDA) , pulse frequency (Pf) , respiratory frequency (Rf) physiological data and Self-Assessment Manikin (SAM) data before and after walking were collected; the mean value of three dimensions of SAM and the emotion difference before and after the experiment were calculated. The differences of physiological indexes and subjective mood changes of subjects were tested by paired sample t-test. Results: Compared with the daily environment, the ΔEDA, ΔPf and ΔRf of the subjects in the virtual natural environment were all decreased , and the differences were statistically significant (P<0.05). There were statistically significant differences in pleasure and arousal between subjects before and after using the virtual natural environment (P <0.05). Compared with the daily environment, the Δpleasure degree of subjects using the virtual natural environment increased, and the Δarousal degree and Δdominance degree decreased, and the differences were statistically significant (P <0.05). Conclusion: Walking in virtual natural environment can help subjects improve their mood, relax and improve the regulation ability of autonomic nervous system.

Automatic Pulse Classification for Artefact Removal Using SAX Strings, a CENTER-TBI Study.

High-resolution, waveform-level data from bedside monitors carry important information about a patient's physiology but is also polluted with artefactual data. Manual mark-up is the standard practice for detecting and eliminating artefacts, but it is time-consuming, prone to errors, biased and not suitable for real-time processing.In this paper we present a novel automatic artefact detection technique based on a Symbolic Aggregate approXimation (SAX) technique which makes it possible to represent individual pulses as 'words'. It does that by coding each pulse with a specified number of letters (here six) from a predefined alphabet of characters (here six). The word is then fed to a support vector machine (SVM) and classified as artefactual or physiological.To define the universe of acceptable pulses, the arterial blood pressure from 50 patients was analysed, and acceptable pulses were manually chosen by looking at the average pulse that each 'word' generated. This was then used to train a SVM classifier. To test this algorithm, a dataset with a balanced ratio of clean and artefactual pulses was built, classified and independently evaluated by two observers achieving a sensitivity of 0.972 and 0.954 and a specificity of 0.837 and 0.837 respectively.

Python-Embedded Plugin Implementation in ICM+: Novel Tools for Neuromonitoring Time Series Analysis with Examples Using CENTER-TBI Datasets.

With the appearance of publicly available, high-resolution, physiological datasets in neurocritical care, like Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), there is a growing need for tools that could be used by clinical researchers to interrogate this information-rich data. The ICM+ software is widely used for processing data acquired from bedside monitors. Considering the growing popularity of scripting simple-syntax programming languages like Python, particularly among clinical researchers, we have developed an interface in ICM+ that provides a streamlined way of adding Python scripting functionality to the ICM+ calculation engine. The new interface imposes certain requirements on the scripts and needs an accompanying descriptor file that tells ICM+ about the functions implemented, so that they become available to the end user in the same way as native ICM+ functions. ICM+ also now includes a tool that eases the creation of Python functions to be imported. The Python extension works very efficiently, and any user with some degree of experience in scripting can use it to enrich capabilities of ICM+. Depending on the data analysed and calculations performed, Python functions are 15-60% slower than built-in ICM+ functions, which is a more-than-acceptable trade-off for empowering ICM+ with the unlimited analytical freedom offered by extensive Python libraries.

Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks.

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals.

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen's kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors.

Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics.

How Laboratory Experiments Can Be Exploited forMonitoring Stress in the Wild: A Bridge BetweenLaboratory and Daily Life.

Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.

Design Implementation and Evaluation of a Mobile Continuous Blood Oxygen Saturation Monitoring System.

OBJECTIVE: In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications. METHODS: We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles. RESULTS: The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system. CONCLUSION: These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles. SIGNIFICANCE: This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications.

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

A Portable and Wireless Multi-Channel Acquisition System for Physiological Signal Measurements.

We propose a portable and wireless acquisition system to help consumers or users register important physiological signals. The acquisition system mainly consists of a portable device, a graphic user interface (GUI), and an application program for displaying the signals on a notebook (NB) computer or a smart device. Essential characteristics of the portable device include eight measuring channels, a powerful microcontroller unit, a lithium battery, Bluetooth 3.0 data transmission, and a built-in 2 GB flash memory. In addition, the signals that are measured can be displayed on a tablet, a smart phone, or a notebook computer concurrently. Additionally, the proposed system provides extra power supply sources of ±3 V for the usage of external circuits. On the other hand, consumers or users can design their own sensing circuits and combine them with this system to carry out ubiquitous physiological studies. Four major advantages in the proposed system are the capability of combining it with a NB computer or a smart phone to display the signals being measured in real time, the superior mobility due to its own independent power system, flash memory, and good expandability. Briefly, this acquisition system offers consumers or users a convenient and portable studying tool to measure dynamic vital signals of interest in psychological and physiological research fields.

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.