Assessing the usability of aromatic mouthwashes in alleviating physiological stress responses.

Background: Mouthwashes play a pivotal role in oral care, and their efficacy has been explored extensively across various dimensions. As a contribution to the development of novel oral care products, this study aims to investigate the psychophysiological effects of aromatic mouthwashes during the resilience period from a short-term cognitive stressor utilizing biological signals and subjective evaluations. Methods: A within-participant experimental design with 22 healthy females was conducted with four mouthwashes; peppermint (Mint), peppermint + bergamot (MB), peppermint + sweet orange (MO), and peppermint + lavender (ML), and water as the control (Ctl), after a 20-min calculation task. Subjective evaluations and physiological responses including skin conductance level and electrocardiogram were recorded throughout the experiment. Results: Citrus mouthwashes (MO and MB) showed a greater decrease in heart rate and a significant increase in the high-frequency component of heart rate variability. The participants indicated a significant effect in terms of "flavor preference" and "refreshing sensation" for mouthwash use compared to the Ctl. Conclusion: The results suggest that rinsing with citrus-flavored mouthwashes has a positive impact in alleviating the physiological stress response (in terms of cardiac activity). These findings may have implications for the development of innovative, novel oral care products that promote stress reduction and improve oral health.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28 µA mm-1 cm-2 for glucose and 0.87 µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.

The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics-the Mantel test and the classifier two-sample test-were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21-5%.

Deep learning for predicting respiratory rate from biosignals.

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this paper, we evaluate the performance of deep learning models for respiratory rate prediction. We consider three datasets from bio-sensors which include electrocardiogram (ECG), photoplethysmogram (PPG) data, and surface electromyogram (sEMG) data. The deep learning models include Long short-term memory (LSTM) networks, Bidirectional LSTM (Bi-LSTM), attention-based variants of LSTM, CNN-LSTM and Convolutional-LSTM networks. The deep learning models are evaluated for two separate windows which are 32 s and 64 s window. The models' performance is evaluated using mean absolute error (MAE). The 64 s window has more accurate prediction compared to the 32 s window. Our results indicate Bi-LSTM with Bahdanu Attention has the best performance for the bio-signals. LSTM performs best with one of the datasets, yielding an MAE of 0.70 ± 0.02. Bi-LSTM with Bahdanau attention showed best results with two of the three datasets with MAE of 0.51 ± 0.03 for sEMG based data and MAE of 0.24 ± 0.03 with PPG and ECG based data.

Enabling Remote Responder Bio-Signal Monitoring in a Cooperative Human-Robot Architecture for Search and Rescue.

The roles of emergency responders are challenging and often physically demanding, so it is essential that their duties are performed safely and effectively. In this article, we address real-time bio-signal sensor monitoring for responders in disaster scenarios. In particular, we propose the integration of a set of health monitoring sensors suitable for detecting stress, anxiety and physical fatigue in an Internet of Cooperative Agents architecture for search and rescue (SAR) missions (SAR-IoCA), which allows remote control and communication between human and robotic agents and the mission control center. With this purpose, we performed proof-of-concept experiments with a bio-signal sensor suite worn by firefighters in two high-fidelity SAR exercises. Moreover, we conducted a survey, distributed to end-users through the Fire Brigade consortium of the Provincial Council of Málaga, in order to analyze the firefighters' opinion about biological signals monitoring while on duty. As a result of this methodology, we propose a wearable sensor suite design with the aim of providing some easy-to-wear integrated-sensor garments, which are suitable for emergency worker activity. The article offers discussion of user acceptance, performance results and learned lessons.

Speed controller-based fuzzy logic for a biosignal-feedbacked cycloergometer.

Nowadays, fuzzy-logic systems are implemented to control machinery or processes that previously required human manipulation. The main objective of this research is to propose a controller based on fuzzy-logic that uses bio-signals for decision making. The study presents the implementation of a fuzzy-speed controller for a therapeutic machine called cycloergometer. It is used in patients who require rehabilitation therapy to improve their mobility in the lower body or to increase their relaxation or flexibility. Basic controllers have been developed where the speed is decided through a user interface, and the therapist must constantly increase or decrease the speed according to the condition of the patient. In this paper, the speed of the therapy equipment is adjusted using the heart rate of the patient. In this way, a bio-signal is used to determine whether a person is tired or relaxed. Therefore, a mechanism is obtained that is not subject to the visual criteria of the therapist. A detailed review of the literature illustrates that one of the main limitations of electroencephalography and electromyography recordings is the low signal-to-noise ratio and the fact that the signals captured at the electrodes are a mixture of sources that cannot be observed directly with noninvasive methods. Therefore, it was decided to work with electrocardiogram-based signals for better robustness of the proposed system. The controller output is a voltage signal in PWM, which is determined by the membership and error functions. The behavior of the implemented controller is validated by different experimental tests based on the increase and decrease of the simulated and real heart rate of a patient. Finally, the results obtained and the possible areas of opportunity for the proposed design are discussed.

A Wearable Head Mounted Display Bio-Signals Pad System for Emotion Recognition.

Multimodal bio-signals acquisition based on wearable devices and using virtual reality (VR) as stimulus source are promising techniques in emotion recognition research field. Numerous studies have shown that emotional states can be better evoked through Immersive Virtual Environments (IVE). The main goal of this paper is to provide researchers with a system for emotion recognition in VR environments. In this paper, we present a wearable forehead bio-signals acquisition pad which is attached to Head-Mounted Displays (HMD), termed HMD Bio Pad. This system can simultaneously record emotion-related two-channel electroencephalography (EEG), one-channel electrodermal activity (EDA), photoplethysmograph (PPG) and skin temperature (SKT) signals. In addition, we develop a human-computer interaction (HCI) interface which researchers can carry out emotion recognition research using VR HMD as stimulus presentation device. To evaluate the performance of the proposed system, we conducted different experiments to validate the multimodal bio-signals quality, respectively. To validate EEG signal, we have assessed the performance in terms of EEG eyes-blink task and eyes-open and eyes-closed task. The EEG eyes-blink task indicates that the proposed system can achieve comparable EEG signal quality in comparison to the dedicated bio-signals measuring device. The eyes-open and eyes-closed task proves that the proposed system can efficiently record alpha rhythm. Then we used signal-to-noise ratio (SNR) and Skin Conductance Reaction (SCR) signal to validate the performance for EDA acquisition system. A filtered EDA signal, with a high mean SNR of 28.52 dB, is plotted on HCI interface. Moreover, the SCR signal related to stimulus response can be correctly extracted from EDA signal. The SKT acquisition system has been validated effectively by the temperature change experiment when subjects are in unpleasant emotion. The pulse rate (PR) estimated from PPG signal achieved the low mean average absolute error (AAE), which is 1.12 beats per minute (BPM) over 8 recordings. In summary, the proposed HMD Bio Pad offers a portable, comfortable and easy-to-wear device for recording bio-signals. The proposed system could contribute to emotion recognition research in VR environments.

A review on current trends in automatic sleep staging through bio-signal recordings and future challenges.

Sleep staging is a vital process conducted in order to analyze polysomnographic data. To facilitate prompt interpretation of these recordings, many automatic sleep staging methods have been proposed. These methods rely on bio-signal recordings, which include electroencephalography, electrocardiography, electromyography, electrooculography, respiratory, pulse oximetry and others. However, advanced, uncomplicated and swift sleep-staging-evaluation is still needed in order to improve the existing polysomnographic data interpretation. The present review focuses on automatic sleep staging methods through bio-signal recording including current and future challenges.

Aspectos éticos del uso de identificadores biométricos / Bioethical aspects of the use of biometric identifiers / Aspectos éticos do uso de identificadores biométricos

La biometría ha tenido una rápida expansión como tecnología, siendo cada vez más accesible para el público general, prestando utilidad a los gobiernos y a la sociedad en el manejo de la seguridad ciudadana. Se aplica para la verificación o la identificación de la identidad de un individuo y, desde un punto de vista puramente técnico, constituye un problema de reconocimiento de patrones. En Chile, el marco legal acerca del manejo de datos personales no incluye específicamente a la biometría. A la fecha, se encuentra en trámite un nuevo proyecto de ley que considera tanto los datos biométricos como los perfiles biológicos. En otros países, los informes de las últimas dos décadas, que analizan el uso de biometría, plantean como principal foco de preocupación, desde el punto de vista ético, el derecho a la privacidad de los individuos, entregándose orientaciones prácticas acerca de la aplicación apropiada de estas tecnologías. Finalmente, desde un breve análisis de la biopolítica, se contextualiza el papel que cumple la biometría y los requerimientos éticos básicos para su desarrollo en dicho marco.

Biometrics has had a rapid expansion as a technology, being increasingly accessible to the public, and lending utility to governments and society in the management of citizen security. It is applied either for verification or for identification of an individual's identity and technically constitutes a problem of pattern recognition. In Chile, the existing legal framework regarding the handling of personal data does not include specific biometrics in its regulation. A new bill that considers both biometric data and Biological profiles is in process. In addition, we revised the international reports of the last two decades that analyze the use of biometrics. The primary focus of concern of these documents, from the ethical point of view, is the right to privacy of individuals, giving practical guidance and discussion regarding the elements necessary for the application of these novel technologies in an appropriate ethically manner. Finally, we contextualize from a brief analysis of biopolitics the role of biometrics and the basic ethical requirements for its development in that framework.

A biometria teve uma rápida expansão como tecnologia, sendo cada vez mais acessível para o público geral, sendo de utilidade para os governos e à sociedade no manejo da segurança cidadã. Ela pode ser aplicada para a verificação ou estabelecimento da identidade de um indivíduo e, desde um ponto de vista puramente técnico, constitui um problema de reconhecimento de padrões. No Chile, o contexto legal acerca do manejo de dados pessoais não inclui especificamente a biometria. Atualmente, encontra-se tramitando um novo projeto de lei que considera tanto os dados biométricos como os perfis biológicos. Em outros países, os informes das últimas duas décadas que analisam o uso da biometria, propõem como principal foco de preocupação, do ponto de vista ético, o direito à privacidade dos indivíduos, entregando-se orientações práticas acerca da aplicação apropriada destas tecnologias. Finalmente, desde uma breve análise da bio-política, contextualiza-se o papel que cumpre a biometria e os requerimentos éticos básicos para seu desenvolvimento em tal contexto legal.

High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall.

Falls are a significant cause of loss of independence, disability and reduced quality of life in people with Parkinson's disease (PD). Intervening quickly and accurately on the postural instability could strongly reduce the consequences of falls. In this context, the paper proposes and validates a novel architecture for the reliable recognition of losses of balance situations. The proposed system addresses some challenges related to the daily life applicability of near-fall recognition systems: the high specificity and system robustness against the Activities of Daily Life (ADL). In this respect, the proposed algorithm has been tested on five different tasks: walking steps, sudden curves, chair transfers via the timed up and go (TUG) test, balance-challenging obstacle avoidance and slip-induced loss of balance. The system analyzes data from wireless acquisition devices that capture electroencephalography (EEG) and electromyography (EMG) signals. The collected data are sent to two main units: the muscular unit and the cortical one. The first realizes a binary ON/OFF pattern from muscular activity (10 EMGs) and triggers the cortical unit. This latter unit evaluates the rate of variation in the EEG power spectrum density (PSD), considering five bands of interest. The neuromuscular features are then sent to a logical network for the final classification, which distinguishes among falls and ADL. In this preliminary study, we tested the proposed model on 9 healthy subjects (aged 26.3 ± 2.4 years), even if the study on PD patients is under investigation. Experimental validation on healthy subjects showed that the system reacts in 370.62 ± 60.85 ms with a sensitivity of 93.33 ± 5.16%. During the ADL tests the system showed a specificity of 98.91 ± 0.44% in steady walking steps recognition, 99.61 ± 0.66% in sudden curves detection, 98.95 ± 1.27% in contractions related to TUG tests and 98.42 ± 0.90% in the obstacle avoidance protocol.

A Review on Methods for Random Motion Detection and Compensation in Bio-Radar Systems.

The bio-radar system can measure vital signals accurately, by using the Doppler effect principle, which relates the received signal properties to the distance change between the radar antennas and the subject chest-wall. These systems have countless applications, from short range detection to assist in rescue missions, to long-term applications as for the continuous sleeping monitoring. Once the main applications of these systems intend to monitor subjects during long periods of time and under noisy environments, it is impossible to guarantee the patient immobilization, hence its random motion, as well as other clutter sources, will interfere in the acquired signals. Therefore, the signal processing algorithms developed for these applications have been facing several challenges regarding the random motion detection and mitigation. In this paper, an extended review on the already implemented methods is done, considering continuous wave radars. Several sources of random motion are considered, along with different approaches to compensate the distortions caused by them.

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload.

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

Integration of Wearable Solutions in AAL Environments with Mobility Support.

The overall demographic profile of current societies point to a significant growth of the elderly people. Associated with the increase of the average hope of life and consequent increase in chronic diseases, there is the need for protection and daily care. Increasing investments in technology, such as Ambient Assisted Living (AAL) solutions, promote the quality of live extending the time people can live in their desired environment. This paper proposes the design, deployment, and real testbed of an e-health wearable monitoring system based on the integration of several AAL tools and platforms for elderly's bio-signals monitoring. This solution includes electrocardiography (ECG), respiration rate, beats per minute, body temperature, and falls detention and notification. The paper also describes, in detail, the real pilot and analyzes some early results concerning the users quality of experience, and the found results are very promising.

The Design of Wavelet-based Biological Signal CODEC for Telemedicine Application / 대한의료정보학회지

In this paper, waveform preserving coding technique is proposed for the bio-signals based on wavelet transform local maxima representation. In order to efficiently encode the bio-signal, it utilize the inherent property of bio-signals that sharp waves corresponding to the local maxima in wavelet domain contain the most of the clinically significant information in general. The testing sets of ECG and EEG are experimented to select the appropriate wavelet basis and to demonstrate the usefulness regarding to coding efficiency. The spline basis is more suitable in designing the coder than the Harr basis since the spline basis produces less number of local maxima than the Harr basis. The proposed wavelet method based on spline basis outperforms the conventional subband coding technique in terms of wave form degradation and coding efficiency. The actual transmission experiment using external communication network and the general personal computer demonstrates the applicability to telemedicine system handling the bio-signals.