Association of physiological factors with grip and leg extension strength: tohoku medical megabank community-based cohort study.

BACKGROUND: Upper and lower extremity muscle strength can be used to predict health outcomes. However, the difference between the relation of upper extremity muscle and of lower extremity muscle with physiological factors is unclear. This study aimed to evaluate the association between physiological data and muscle strength, measured using grip and leg extension strength, among Japanese adults. METHODS: We conducted a cross-sectional study of 2,861 men and 6,717 women aged ≥ 20 years living in Miyagi Prefecture, Japan. Grip strength was measured using a dynamometer. Leg extension strength was measured using a hydraulic isokinetic leg press machine. Anthropometry and physiological data, including blood pressure, calcaneal ultrasound bone status, pulmonary function, carotid echography, and blood information, were assessed. We used a general linear model adjusted for age, body composition, and smoking status to evaluate the association between muscle strength and physiological factors. RESULTS: Grip and leg extension strength were positively associated with bone area ratio, vital capacity, forced vital capacity, forced expiratory volume in one second, and estimated glomerular filtration rate, and negatively associated with waist circumference and percentage body fat mass in both the sexes. Diastolic blood pressure was positively associated with grip strength in both the sexes and leg extension strength in men, but not women. High-density lipoprotein cholesterol and red blood cell counts were positively associated with grip and leg extension strength in women, but not men. In both the sexes, pulse rate, total cholesterol, and uric acid were consistently associated with only leg extension strength, but not grip strength. In women, glycated hemoglobin demonstrated negative and positive associations with grip and leg extension strength, respectively. CONCLUSIONS: Grip and leg extension strength demonstrated similar associations with anthropometry, pulmonary function, and estimated glomerular filtration rate, but the associations with the other factors were not always consistent.

A tree-based explainable AI model for early detection of Covid-19 using physiological data.

With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .

EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment.

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.

The Impact of Various Cockpit Display Interfaces on Novice Pilots' Mental Workload and Situational Awareness: A Comparative Study.

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Research on identification of flight cadets' cognitive load based on multi-source physiological data and CGAN-DBN model.

Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets' cognitive load. The research results show that the flight cadets' cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.

In our study, a highly accurate cognitive load identification model for flight cadets was established by using multi-source physiological data. Moreover, it provides a theoretical basis for identifying the cognitive load of pilots through wearable physiological devices. Our intent is to catalyse further research and technological development.

Emotional experiences of service robots' anthropomorphic appearance: a multimodal measurement method.

Anthropomorphic appearance is a key factor to affect users' attitudes and emotions. This research aimed to measure emotional experience caused by robots' anthropomorphic appearance with three levels - high, moderate, and low - using multimodal measurement. Fifty participants' physiological and eye-tracker data were recorded synchronously while they observed robot images that were displayed in random order. Afterward, the participants reported subjective emotional experiences and attitudes towards those robots. The results showed that the images of the moderately anthropomorphic service robots induced higher pleasure and arousal ratings, and yielded significantly larger pupil diameter and faster saccade velocity, than did the low or high robots. Moreover, participants' facial electromyography, skin conductance, and heart-rate responses were higher when observing moderately anthropomorphic service robots. An implication of the research is that service robots' appearance should be designed to be moderately anthropomorphic; too many human-like features or machine-like features may disturb users' positive emotions and attitudes.Practitioner Summary: This research aimed to measure emotional experience caused by three types of anthropomorphic service robots using a multimodal measurement experiment. The results showed that moderately anthropomorphic service robots evoked more positive emotion than high and low anthropomorphic robots. Too many human-like features or machine-like features may disturb users' positive emotions.

A hardware and software system for MRI applications requiring external device data.

PURPOSE: Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline. METHODS: The hardware includes a set of external devices - electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. - using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications. RESULTS: The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction. CONCLUSION: The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

A Music Playback Algorithm Based on Residual-Inception Blocks for Music Emotion Classification and Physiological Information.

Music can generate a positive effect in runners' performance and motivation. However, the practical implementation of music intervention during exercise is mostly absent from the literature. Therefore, this paper designs a playback sequence system for joggers by considering music emotion and physiological signals. This playback sequence is implemented by a music selection module that combines artificial intelligence techniques with physiological data and emotional music. In order to make the system operate for a long time, this paper improves the model and selection music module to achieve lower energy consumption. The proposed model obtains fewer FLOPs and parameters by using logarithm scaled Mel-spectrogram as input features. The accuracy, computational complexity, trainable parameters, and inference time are evaluated on the Bi-modal, 4Q emotion, and Soundtrack datasets. The experimental results show that the proposed model is better than that of Sarkar et al. and achieves competitive performance on Bi-modal (84.91%), 4Q emotion (92.04%), and Soundtrack (87.24%) datasets. More specifically, the proposed model reduces the computational complexity and inference time while maintaining the classification accuracy, compared to other models. Moreover, the size of the proposed model for network training is small, which can be applied to mobiles and other devices with limited computing resources. This study designed the overall playback sequence system by considering the relationship between music emotion and physiological situation during exercise. The playback sequence system can be adopted directly during exercise to improve users' exercise efficiency.

Automated Assessment of Cardiovascular Sufficiency Using Non-Invasive Physiological Data.

For fluid resuscitation of critically ill individuals to be effective, it must be well calibrated in terms of timing and dosages of treatments. In current practice, the cardiovascular sufficiency of patients during fluid resuscitation is determined using primarily invasively measured vital signs, including Arterial Pressure and Mixed Venous Oxygen Saturation (SvO2), which may not be available in outside-of-hospital settings, particularly in the field when treating subjects injured in traffic accidents or wounded in combat where only non-invasive monitoring is available to drive care. In this paper, we propose (1) a Machine Learning (ML) approach to estimate the sufficiency utilizing features extracted from non-invasive vital signs and (2) a novel framework to address the detrimental impact of inter-patient diversity on the ability of ML models to generalize well to unseen subjects. Through comprehensive evaluation on the physiological data collected in laboratory animal experiments, we demonstrate that the proposed approaches can achieve competitive performance on new patients using only non-invasive measurements. These characteristics enable effective monitoring of fluid resuscitation in real-world acute settings with limited monitoring resources and can help facilitate broader adoption of ML in this important subfield of healthcare.

A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals.

Teaching is an activity that requires understanding the class's reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel's model, we grouped the most important Ekman's facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students' level of attention for in-presence lectures.

A Systematic Review for Cognitive State-Based QoE/UX Evaluation.

Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user's cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014-2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures.

Classification of Critical Levels of CO Exposure of Firefigthers through Monitored Heart Rate.

Smoke inhalation poses a serious health threat to firefighters (FFs), with potential effects including respiratory and cardiac disorders. In this work, environmental and physiological data were collected from FFs, during experimental fires performed in 2015 and 2019. Extending a previous work, which allowed us to conclude that changes in heart rate (HR) were associated with alterations in the inhalation of carbon monoxide (CO), we performed a HR analysis according to different levels of CO exposure during firefighting based on data collected from three FFs. Based on HR collected and on CO occupational exposure standards (OES), we propose a classifier to identify CO exposure levels through the HR measured values. An ensemble of 100 bagged classification trees was used and the classification of CO levels obtained an overall accuracy of 91.9%. The classification can be performed in real-time and can be embedded in a decision fire-fighting support system. This classification of FF' exposure to critical CO levels, through minimally-invasive monitored HR, opens the possibility to identify hazardous situations, preventing and avoiding possible severe problems in FF' health due to inhaled pollutants. The obtained results also show the importance of future studies on the relevance and influence of the exposure and inhalation of pollutants on the FF' health, especially in what refers to hazardous levels of toxic air pollutants.

Concluding Notes and Future Directions.

The articles in this issue include experimental research and clinical studies on the bidirectional process of connecting experience and words, which we term the Referential Process (RP). These concluding notes focus on new questions and new directions for research. Studies now under way include characterization and measurement of the Arousal function of the referential process, which involves how people talk when the connection to specific ideas is not yet fully developed, and new research on paralinguistic features of interpersonal communication. Further work in these areas will involve automatic transcription technology to incorporate pitch, rate of speech and loudness, as well as development of a Time-DAAP program to enable such assessment. Research is also needed to investigate the relationship between language and underlying physiological and neurological mechanisms. These relationships can be examined using physiological measures such as galvanic skin response (GSR) and changes in heart rate and respiration. While fMRI scans are not compatible with tasks requiring speech production, fMRI compatible tablet systems are available for writing tasks. Participants may also be scanned while reading literary passages that show differences in RP functions. A major goal of our research program is the application of RP measures in large scale treatment efficacy and effectiveness studies evaluating particular treatment forms. The computerized referential process procedures have the potential to study whole trajectories of large numbers of treatments; and also to identify important turning points within treatments and within sessions. The interpretation of these measures in the context of a systematic theory of treatment process has the value of enabling results that are not only statistically powerful but clinically significant as well. Other potential areas of study include application of the language measures in large scale studies of political, religious and literary discourse.

Development of an IoT-Based Construction Worker Physiological Data Monitoring Platform at High Temperatures.

This study presents an IoT-based construction worker physiological data monitoring platform using an off-the-shelf wearable smart band. The developed platform is designed for construction workers performing under high temperatures, and the platform is composed of two parts: an overall heat assessment (OHS) and a personal management system (PMS). OHS manages the breaktimes for groups of workers based using a thermal comfort index (TCI), as provided by the Korea Meteorological Administration (KMA), while PMS assesses the individual health risk level based on fuzzy theory using data acquired from a commercially available smart band. The device contains three sensors (PPG, Acc, and skin temperature), two modules (LoRa and GPS), and a power supply, which are embedded into a microcontroller (MCU). Thus, approved personnel can monitor the status as well as the current position of a construction worker via a PC or smartphone, and can make necessary decisions remotely. The platform was tested in both indoor and outdoor environment for reliability, achieved less than 1% of error, and received satisfactory feedback from on-site users.

Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals.

A key access point to the functioning of the autonomic nervous system is the investigation of peripheral signals. Wearable devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. However, the achievable data quality can be lower, and data are subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study, we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and electrodermal activity signals is validated with a standard set of signal quality indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.

On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach.

In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011) Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model's predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico-BG-cortical loops.SIGNIFICANCE STATEMENT Inspired by the idea that basal ganglia (BG) teach the prefrontal cortex (PFC) to acquire category representations, we developed a novel neurocomputational model and tested it on a task that was recently applied in monkey experiments. As an advantage over previous models of category learning, our model allows to compare simulation data with single-cell recordings in PFC and BG. We not only derived model predictions, but already verified a prediction to explain the observed drop in striatal category selectivity. When testing our model with a simple, real-world face categorization task, we observed that the fast striatal learning with a performance of 85% correct responses can teach the slower PFC learning to push the model performance up to almost 100%.

Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.

An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals.

Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.

An optimal two-bout strategy with phase change material cooling vests to improve comfort in hot environment.

Cooling vests incorporating phase change material (PCM) packets are used to improve comfort of workers in hot environments. This work aims to investigate by modeling and experimentation the effect of dividing the working duration into two bouts, where different PCM melting temperatures are used in each bout. An integrated bio-heat and fabric-PCM model predictions of physiological and subjective votes are validated via active human subject testing at hot conditions. A parametric study is performed to select, at two conditions (40°C and 45°C), the optimal PCM melting temperatures of the two bouts that would result with similar thermal comfort and sensation to the optimal single-bout case. The optimal case achieves most reductions in energy use for PCM regeneration, PCM carried weight and material cost. The results of the parametric study showed that heat storage is reduced in the second bout due to wearing the second vest with lower PCM melting temperature, thus thermal comfort and sensation are significantly improved. The optimal case at the 40°C environment uses a vest with 21°C PCMs in the first bout and a vest with 21°C PCMs in the second bout (V21→V21). At 45°C, the optimal case is V18→V10 with significant PCM weight reductions from the reference single bout case by a minimum of 47%. Thus, the issue of extra carried weight that affect metabolism and ease of movement when applying continuous cooling during work have been mitigated by using the two-bout strategy.