Pupil Response in Visual Tracking Tasks: The Impacts of Task Load, Familiarity, and Gaze Position

Pupil size is a significant biosignal for human behavior monitoring and can reveal much underlying information. This study explored the effects of task load, task familiarity, and gaze position on pupil response during learning a visual tracking task. We hypothesized that pupil size would increase with task load, up to a certain level before decreasing, decrease with task familiarity, and increase more when focusing on areas preceding the target than other areas. Fifteen participants were recruited for an arrow tracking learning task with incremental task load. Pupil size data were collected using a Tobii Pro Nano eye tracker. A 2 × 3 × 5 three-way factorial repeated measures ANOVA was conducted using R (version 4.2.1) to evaluate the main and interactive effects of key variables on adjusted pupil size. The association between individuals' cognitive load, assessed by NASA-TLX, and pupil size was further analyzed using a linear mixed-effect model. We found that task repetition resulted in a reduction in pupil size; however, this effect was found to diminish as the task load increased. The main effect of task load approached statistical significance, but different trends were observed in trial 1 and trial 2. No significant difference in pupil size was detected among the three gaze positions. The relationship between pupil size and cognitive load overall followed an inverted U curve. Our study showed how pupil size changes as a function of task load, task familiarity, and gaze scanning. This finding provides sensory evidence that could improve educational outcomes.

Accelerometer-based detection of African swine fever infection in wild boar

Infectious wildlife diseases that circulate at the interface with domestic animals pose significant threats worldwide and require early detection and warning. Although animal tracking technologies are used to discern behavioural changes, they are rarely used to monitor wildlife diseases. Common disease-induced behavioural changes include reduced activity and lethargy ('sickness behaviour'). Here, we investigated whether accelerometer sensors could detect the onset of African swine fever (ASF), a viral infection that induces high mortality in suids for which no vaccine is currently available. Taking advantage of an experiment designed to test an oral ASF vaccine, we equipped 12 wild boars with an accelerometer tag and quantified how ASF affects their activity pattern and behavioural fingerprint, using overall dynamic body acceleration. Wild boars showed a daily reduction in activity of 10-20% from the healthy to the viremia phase. Using change point statistics and comparing healthy individuals living in semi-free and free-ranging conditions, we show how the onset of disease-induced sickness can be detected and how such early detection could work in natural settings. Timely detection of infection in animals is crucial for disease surveillance and control, and accelerometer technology on sentinel animals provides a viable complementary tool to existing disease management approaches.

Aiding gastrointestinal diagnostic laboratory by designing a device for the non invasive detection of peptic ulcer

Peptic ulcer (PU) has been recognized as an utmost gastrointestinal problem that affects the lining of the stomach and duodenum, specifically triggering soreness. It is a life-threatening condition, while roots of the infection are not identified yet. There are various risk factors for the cause of peptic ulcer disease, but the most significant is "Helicobacter pylori" (H. pylori). The detection of this disease involves different invasive procedures which are painful and not feasible for everyone. The aim of this device is to identify the peptic ulcer non-invasively by unmasking the presence of H. Pylori bacterium by monitoring crucial parameters of the disease which include respiration rate, heart rate, ECG, pH of Saliva, and temperature. Multiple investigations related to PU authenticate the alteration in these physicochemical aspects of the body. The increase in the level of stomach acid in PU is responsible for belching and bloating. Heart rate, temperature, and respiratory rate are also elevated during peptic ulcers while the pH of Saliva is decreased toward the acidic side. The disturbance in the QRS complex of the ECG wave is also observed. These biosignals are examined as analog input from the body, sent into MCP3008, and converted into digital input signals. Then these digital inputs are directed toward Raspberry pi 3 which processes, received inputs, and shows output on the LCD. The values of parameters obtained are then compared with standard values and a conclusion is made that whether a patient has a peptic ulcer or not.

Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification

Background

Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification.

Methods

To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation.

Results

The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study.

Conclusion

We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies.

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance

The deficiency and excess of vitamin D cause various diseases, necessitating continuous management; but it is not easy to accurately measure the serum vitamin D level in the body using a non-invasive method. The aim of this study is to investigate the correlation between vitamin D levels, body information obtained by an InBody scan, and blood parameters obtained during health checkups, to determine the optimum frequency of vitamin D quantification in the skin and to propose a vitamin D measurement method based on impedance. We assessed body composition, arm impedance, and blood vitamin D concentrations to determine the correlation between each element using multiple machine learning analyses and an algorithm which predicted the concentration of vitamin D in the body using the impedance value developed. Body fat percentage obtained from the InBody device and blood parameters albumin and lactate dehydrogenase correlated with vitamin D level. An impedance measurement frequency of 21.1 Hz was reflected in the blood vitamin D concentration at optimum levels, and a confidence level of about 75% for vitamin D in the body was confirmed. These data demonstrate that the concentration of vitamin D in the body can be predicted using impedance measurement values. This method can be used for predicting and monitoring vitamin D-related diseases and may be incorporated in wearable health measurement devices.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Voice is one of the essential mechanisms for communicating and expressing one's intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components.

Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area

Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.

Biosignal Compression Toolbox for Digital Biomarker Discovery

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare "data deluge," leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the "Biosignal Data Compression Toolbox," an open-source, accessible software platform for compressing biosignal data.

In the Flow of Life: Capturing Affective Socializing Dynamics Using a Wearable Sensor and Intensive Daily Diaries

Interpersonal socializing is important to many sociological outcomes, but assessing the affective dynamics within interactional contexts is extremely challenging methodologically. As a first step toward capturing socializing and affective outcomes concurrently, this pilot study (n = 118) combines intensive daily surveys with a wearable sensor that tracked affective arousal. This approach allowed the operationalization of affect along its two primary dimensions, valence and arousal, which were then linked to periods socializing with a romantic partner, a best friend, and/or a group of friends. Although socializing predicted positive and negative affective valence concurrently in time, only socializing with groups of friends consistently predicted increased affective arousal. Findings for romantic partners and/or socializing with a close friend suggest that low arousal "downtime" with close intimates may also provide important social functions. This work demonstrates a new biosignaling approach to affective dynamics broadly relevant to emotion-related sociological research.

[Metrological assessment of the averaged phase cardiocycle in solving problems of rehabilitation and sports medicine]

Electrocardiography is one of the leading and most common methods of instrumental research of heart biorhythms in primary medical and social care. The development of information and digital technologies, robotics for the purpose of preclinical assessment and ranking of myocardial status in different categories of the population for preventive purposes has allowed us to create highly sensitive methods for recording and processing biosignals. Converting and displaying a cardiosignal on the phase plane to obtain a averaged phase cardiocycle (APC) is a demostrative instrumental method of research.

AIM OF STUDY

To determine the possibility of measuring the myocardial biosignal using the averaged cardiocycle of a single-channel ECG in the phase space.

MATERIALS AND METHODS

Registration and analysis of the averaged biosignal obtained by converting a single-channel ECG in phase space, taking into account age and gender characteristics, was carried out using the FAZAGRAF software and hardware complex, which implements the original information technology for processing an electrocardiogram in phase space using computer graphics ideas and automatic pattern recognition methods. The parameters of the phase averaged cycle were subject to an analytical assessment: duration and amplitude characteristics of the P, Q, R, S, T waves (ms). The construction of a nonlinear mathematical model is carried out using an algebraic model of constructive logic based on predicate logic, followed by a multivariate assessment of the influence of each factor. The massive of verified data is represented by 58 016 values of indicators of the cardiovascular system in 1568 study participants aged 20 to 65 years. All subjects were divided into 2 groups: group 1 - 1087 conditionally healthy patients, group 2 - 471 patients with a verified diagnosis of pathology of the cardiovascular system. The information content of the QTc indicator was determined using ROC analysis. The age-related features of some parameters of the averaged phase cycle were examined in a cohort of 218 conditionally healthy schoolchildren aged 6-17 in Simferopol. The study of the adaptive features of the circulatory system was carried out in 40 sportsmen aged 14-15 with different profile orientation of training activity, which was divided into 2 cohorts. The first cohort included 20 wrestlers, the second - amounted to 20 football players. The gender characteristics of the reaction to natural aroma effects were studied in 30 conditionally healthy young people aged 17-18 (15 boys and 15 girls).

RESULTS AND CONCLUSION

The most significant parameters of the averaged phase cardiocycle for assessing the states and characteristics of their values were revealed. The obtained parameters of the averaged phase cardiocycle with a quantitative assessment of ranges with a significant number of subjects were used to solve preventive (predictive) problems of rehabilitation and sports medicine.

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification

The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.

Wireless, Skin-Mountable EMG Sensor for Human-Machine Interface Application

The development of advanced technologies for wireless data collection and the analysis of quantitative data, with application to a human–machine interface (HMI), is of growing interest. In particular, various wearable devices related to HMIs are being developed. These devices require a customization process that considers the physical characteristics of each individual, such as mounting positions of electrodes, muscle masses, and so forth. Here, the authors report device and calculation concepts for flexible platforms that can measure electrical signals changed through electromyography (EMG). This soft, flexible, and lightweight EMG sensor can be attached to curved surfaces such as the forearm, biceps, back, legs, etc., and optimized biosignals can be obtained continuously through post-processing. In addition to the measurement of EMG signals, the application of the HMI has stable performance and high accuracy of more than 95%, as confirmed by 50 trials per case. The result of this study shows the possibility of application to various fields such as entertainment, the military, robotics, and healthcare in the future.

Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems.

A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

Expectations of the Physiological Responses Can Change the Somatosensory Experience for Acupuncture Stimulation

Objective: Humans interpret sensory inputs based on actual stimuli and expectations of the stimuli. We investigated whether manipulating information related to the physiological response could change the somatosensory experience of acupuncture.

Methods: Twenty-four participants received tactile stimulations with a von Frey filament on the left arm. Participants were informed that they would receive acupuncture stimulations at different angles while they were presented with changes in their peripheral blood flow (PBF) measured with Laser Doppler perfusion imaging. However, in reality, they were observing premade pseudo-biosignal images (six sessions: one circular, two rectangular elongated, two diagonally elongated, and one cross-fixation [control] shape). After each session, the participants reported the intensity and location of the de qi sensations perceived on their arm using a bodily sensation mapping tool. The spatial patterns of the somatic sensations were visualized using statistical parametric mapping. The F1 score was calculated to measure the similarity between the presented pseudo-biosignals and reported de qi response images.

Results: The spatial configurations of the presented pseudo-biosignal images and de qi response images were similar. The rectangular elongated pseudo-biosignal shape had a significantly higher F1 score compared to the control. All tactile stimulations produced similar levels of enhanced PBF regardless of the pseudo-biosignal shape.

Conclusion: The spatial configurations of somatic sensations changed according to the presented pseudo-biosignal shape, suggesting that expectations of the physiological response to acupuncture stimulation can influence the perceived somatic sensation.

An Insulated Flexible Sensor for Stable Electromyography Detection: Applicationto Prosthesis Control

Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin and are therefore sensitive to sweat and require preparation of the skin. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Due to their insulating layer between skin and sensor area, capacitive sensors are insensitive to the skin condition, they require neither conductive connection to the skin nor electrolytic paste or skin preparation. Here, we describe a highly stable, low-power capacitive EMG measurement set-up that is suitable for real-world application. Various flexible multi-layer sensor set-ups made of copper and insulating foils, flex print and textiles were compared. These flexible sensor set-ups adapt to the anatomy of the human forearm, therefore they provide high wearing comfort and ensure stability against motion artifacts. The influence of the materials used in the sensor set-up on the magnitude of the coupled signal was demonstrated based on both theoretical analysis and measurement.The amplifier circuit was optimized for high signal quality, low power consumption and mobile application. Different shielding and guarding concepts were compared, leading to high SNR.

Portable System for Real-Time Detection of Stress Level

Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.

Biosignals, Standards and FHIR - The Way to Go?

BACKGROUND

Standards have become available to share semantically encoded vital parameters from medical devices, as required for example by personal healthcare records. Standardised sharing of biosignal data largely remains open.

OBJECTIVES

The goal of this work is to explore available biosignal file format and data exchange standards and profiles, and to conceptualise end-to-end solutions.

METHODS

The authors reviewed and discussed available biosignal file format standards with other members of international standards development organisations (SDOs).

RESULTS

A raw concept for standards based acquisition, storage, archiving and sharing of biosignals was developed. The GDF format may serve for storing biosignals. Signals can then be shared using FHIR resources and may be stored on FHIR servers or in DICOM archives, with DICOM waveforms as one possible format.

CONCLUSION

Currently a group of international SDOs (e.g. HL7, IHE, DICOM, IEEE) is engaged in intensive discussions. This discussion extends existing work that already was adopted by large implementer communities. The concept presented here only reports the current status of the discussion in Austria. The discussion will continue internationally, with results to be expected over the coming years.

Stretchable Multichannel Electromyography Sensor Array Covering Large Area for Controlling Home Electronics with Distinguishable Signals from Multiple Muscles

Physiological signals provide important information for biomedical applications and, more recently, in the form of wearable electronics for active interactions between bodies and external environments. Multiple physiological sensors are often required to map distinct signals from multiple points over large areas for more diverse applications. In this paper, we present a reusable, multichannel, surface electromyography (EMG) sensor array that covers multiple muscles over relatively large areas, with compliant designs that provide different levels of stiffness for repetitive uses, without backing layers. Mechanical and electrical characteristics along with distinct measurements from different muscles demonstrate the feasibility of the concept. The results should be useful to actively control devices in the environment with one array of wearable sensors, as demonstrated with home electronics.