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This study aimed to develop and evaluate the reliability of a surface electromyography-based (sEMG) index to quantify knee muscle coactivation in healthy recreational athletes during the Lower Quarter Y-Balance Test (YBT-LQ). A prospective observational repeated measures design was used to assess both intra- and inter-rater reliability. Forty males completed three trials, with two raters independently conducting assessments. sEMG signals from the vastus medialis, vastus lateralis, biceps femoris, and semitendinosus muscles were collected to calculate four coactivation indices (CoAIs) for different muscle pairs. The reliability evaluation of these CoAIs was based on intraclass correlation coefficients. The results revealed that the reliability of the CoAIs varied depending on the muscle pair, direction, and limb examined. The highest intra- and inter-rater reliability was noted for the left lower limb in the posterolateral direction. The lowest reliability was found for the right lower limb in the anterior direction. Statistical analyses indicated significant differences in specific CoAIs across different YBT-LQ directions. These findings highlight the potential of sEMG-based CoAIs for assessing knee muscle activity during the YBT-LQ in healthy male recreational athletes. However, choosing CoAIs is critical for reliable clinical and research assessments of knee stability and neuromuscular control. [ABSTRACT FROM AUTHOR]

Breast cancers can appear and progress rapidly, necessitating more frequent monitoring outside of hospital settings to significantly reduce mortality rates. Recently, there has been considerable interest in developing techniques for portable, user-friendly, and low-cost breast tumor monitoring applications, enabling frequent and cost-efficient examinations. Microwave technique-based breast cancer detection, which is based on differential dielectric properties of malignant and healthy tissues, is regarded as a promising solution for cost-effective breast tumor monitoring. This paper presents the development process of the first proof-of-concept of a breast tumor monitoring vest which is based on the microwave technique. Two unique vests are designed and evaluated on realistic 3D human tissue phantoms having different breast density types. Additionally, the measured results are verified using simulations carried out on anatomically realistic voxel models of the electromagnetic simulations. The radio channel characteristics are evaluated and analyzed between the antennas embedded in the vest in tumor cases and reference cases. Both measurements and simulation results show that the proposed vest can detect tumors even if only 1 cm in diameter. Additionally, simulation results show detectability with 0.5 cm tumors. It is observed that the detectability of breast tumors depends on the frequency, antenna selection, size of the tumors, and breast types, causing differences of 0.5–30 dB in channel responses between the tumorous and reference cases. Due to simplicity and cost-efficiency, the proposed channel analysis-based breast monitoring vests can be used for breast health checks in smaller healthcare centers and for user-friendly home monitoring which can prove beneficial in rural areas and developing countries. [ABSTRACT FROM AUTHOR]

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using this camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation were evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and with only minor intervention. In this case, the mean difference between noncontact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor was proven effective under ideal animal experimental conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with noncontact sensor systems for use in the neonatal intensive care unit. [ABSTRACT FROM AUTHOR]

In recent years, the interest in medical monitoring for human health has been rapidly increasing due to widespread concern. Hydrogels are widely used in medical monitoring and other fields due to their excellent mechanical properties, electrical conductivity and adhesion. However, some of the non-degradable materials in hydrogels may cause some environmental damage and resource waste. Therefore, organic renewable natural polymers with excellent properties of biocompatibility, biodegradability, low cost and non-toxicity are expected to serve as an alternative to those non-degradable materials, and also provide a broad application prospect for the development of natural-polymer-based hydrogels as flexible electronic devices. This paper reviews the progress of research on many different types of natural-polymer-based hydrogels such as proteins and polysaccharides. The applications of natural-polymer-based hydrogels in body movement detection and biomedical monitoring are then discussed. Finally, the present challenges and future prospects of natural polymer-based hydrogels are summarized. [ABSTRACT FROM AUTHOR]

Recent research has focused on the effectiveness of Virtual Reality (VR) in games as a more immersive method of interaction. However, there is a lack of robust analysis of the physiological effects between VR and flatscreen (FS) gaming. This paper introduces the first systematic comparison and analysis of emotional and physiological responses to commercially available games in VR and FS environments. To elicit these responses, we first selected four games through a pilot study of 6 participants to cover all four quadrants of the valence-arousal space. Using these games, we recorded the physiological activity, including Blood Volume Pulse and Electrodermal Activity, and self-reported emotions of 33 participants in a user study. Our data analysis revealed that VR gaming elicited more pronounced emotions, higher arousal, increased cognitive load and stress, and lower dominance than FS gaming. The Virtual Reality and Flat Screen (VRFS) dataset, containing over 15 hours of multimodal data comparing FS and VR gaming across different games, is also made publicly available for research purposes. Our analysis provides valuable insights for further investigations into the physiological and emotional effects of VR and FS gaming. [ABSTRACT FROM AUTHOR]

This article extends and builds upon our previous research concerning Virtual Reality (VR) with bilateral stimulation as an automated stress-reduction therapy tool. The study coincided with Russia's invasion of Ukraine, thus the software was tailored to reduce the stress of war refugees. We created a 28 minutes relaxation training program in a virtual, relaxing environment in the form of a cozy apartment in the mountains. An integral part of this tool is a set of sensors, which collects and records objective physiological measures to evaluate the system's effectiveness. A pilot treatment programme, incorporating the VR system mentioned above was carried out on the experimental group of 55 Ukrainian refugees who participated in up to five relaxation training sessions. Before starting the session, baseline features such as subjectively perceived stress, mood, galvanic skin response, and EEG were registered. The monitoring of physiological indicators was continued during the training session. Before and after the session, volunteers were asked to fill in questionnaires regarding their current stress level and mood. The obtained results were analyzed in terms of variability over time: before, during, and after the session. [ABSTRACT FROM AUTHOR]

We present a dataset for the analysis of human affective states using functional near-infrared spectroscopy (fNIRS). Data were recorded from thirty-one participants who engaged in two tasks. In the emotional perception task the participants passively viewed images sampled from the standard international affective picture system database, which provided ground-truth valence and arousal annotation for the stimuli. In the affective imagery task the participants actively imagined emotional scenarios followed by rating these for subjective valence and arousal. Correlates between the fNIRS signal and the valence-arousal ratings were investigated to estimate the validity of the dataset. Source-code and summaries are provided for a processing pipeline, brain activity group analysis, and estimating baseline classification performance. For classification, prediction experiments are conducted for single-trial 4-class classification of arousal and valence as well as cross-participant classifications, and comparisons between high and low arousal variants of the valence prediction tasks. Finally, classification results are presented for subject-specific and cross-participant models. The dataset is made publicly available to encourage research on affective decoding and downstream applications using fNIRS data. [ABSTRACT FROM AUTHOR]

We report on a naturalistic study investigating the effects of routine driving on cardiovascular activation. We recruited 21 healthy young adults from a broad geographic area in the Southwestern United States. Using the participants’ own smartphones and smartwatches, we monitored for a week both their driving and non-driving activities. Monitoring included the continuous recording of 1) heart rate throughout the day, 2) hand motion during driving as a proxy of persistent texting, and 3) contextualized driving data, complete with traffic and weather information. These high temporal resolution variables were complemented with the drivers’ biographic and psychometric profiles. Our analysis suggests that anxiety predisposition and high speeds are associated with significant cardiovascular activation on drivers, likely linked to sympathetic arousal. Surprisingly, these associations hold true under good weather, normal traffic, and with experienced drivers behind the wheel. The said findings call for attention to insidious effects of apparently benign drives even for people in their prime. Accordingly, our research contributes to intriguing new discourses on driving affect and personal health informatics. [ABSTRACT FROM AUTHOR]

This manuscript explores the transformative potential of swellable microneedles (MNs) in drug delivery and diagnostics, addressing critical needs in medical treatment and monitoring. Innovations in hydrogel-integrated MN arrays facilitate controlled drug release, thereby expanding treatment options for chronic diseases and conditions that require precise dosage control. The review covers challenges, such as scalability, patient compliance, and manufacturing processes, as well as achievements in advanced manufacturing, biocompatibility, and versatile applications. Nonetheless, limitations in physiological responsiveness and long-term stability remain, necessitating further research in material innovation and integration with digital technologies. Future directions focus on expanding biomedical applications, material advancements, and regulatory considerations for widespread clinical adoption. [ABSTRACT FROM AUTHOR]

In this article, we successfully developed and simulated a wearable pressure sensor for monitoring blood pressure. This sensor utilizes MEMS technology and is based on LC wireless, eliminating the need for a battery system. The sensor is constructed with a metal-insulator-metal capacitive design, featuring a diaphragm measuring 1.1 mm × 1.1 mm and with a thickness of 22 µm. Furthermore, we devised a unique C-shaped circular slot diaphragm to enhance sensitivity by reducing the mechanical rigidity of the membrane. Our design and simulation were conducted using COMSOL Multiphysics. From our findings, we observed that the frequency response to varying pressure ranged from 285 to 445 MHz. Additionally, our results yielded a mechanical sensitivity of 125.7 nm/mmHg, along with a sensor sensitivity of 1.49 fF/mmHg. Notably, the sensor with the novel C-shaped circular slot diaphragm exhibited high-pressure sensitivity, surpassing that of a clamped diaphragm by 15.5%. [ABSTRACT FROM AUTHOR]

Stress has been identified as one of major causes of health issues. To detect the stress levels with higher accuracy, fusion of multimodal physiological signals is a promising technique. However, there is an asynchrony between physiological signals observed from different perspectives. Exploring the temporal alignment relationship between modalities is helpful to improve the quality of multimodal fusion. This paper proposes an end-to-end multimodal stress detection model based on Bidirectional Cross- and Self-modal Attention (BCSA) mechanism. Specifically, we first construct different feature extractors based on the characteristics of Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) to complete automated temporal feature extraction. Second, cross-modal attention is used to seek the alignment relationship between the two modalities and fully fuse cross-modal information. The self-modal attention is used to attenuate noise and redundant information, highlight important information and obtain salient stress representations. Finally, the stress representations of the two modalities are processed separately, and the mean square error (MSE) is used to narrow the gap between them. Experimental results on the UBFC-Phys dataset and WESAD dataset show that the proposed model can effectively improve the accuracy of stress recognition, and outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We present the design, implementation, and results of an algorithm for respiratory rate (RR) estimation using respiratory-induced frequency, intensity, and amplitude variation calculated from the infrared (IR) channel of the SEN-15219 board for photoplethysmography (PPG) acquisition. First, the algorithm was developed in Python (on a PC) using synthetic signals and publicly available respiration and PPG data. We also include a graphical user interface to process data from sensors and display vital signs. Later, we ported the algorithm to an MSP432P401R microcontroller to complete our wearable prototype. Results are promissory and show that RR estimation can be performed on the selected platform with our proposed Fourier Product (FP) method, which results in a Mean Absolute Error of 4.1 using 16-s windows of IR-PPG signals. [ABSTRACT FROM AUTHOR]

In biomedical monitoring, non-intrusive and continuous tracking of vital signs is a crucial yet challenging objective. Although accurate, traditional methods, such as electrocardiography (ECG) and photoplethysmography (PPG), necessitate direct contact with the patient, posing limitations for long-term and unobtrusive monitoring. To address this challenge, we introduce the EmRad system, an innovative solution harnessing the capabilities of continuous-wave (CW) radar technology for the contactless detection of vital signs, including heart rate and respiratory rate. EmRad discerns itself by emphasizing miniaturization, performance, scalability, and its ability to generate large-scale datasets in various environments. This article explains the system's design, focusing on signal processing strategies and motion artifact reduction to ensure precise vital sign extraction. The EmRad system's versatility is showcased through various case studies, highlighting its potential to transform vital sign monitoring in research and clinical contexts.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

This study aimed to develop and evaluate the reliability of a surface electromyography-based (sEMG) index to quantify knee muscle coactivation in healthy recreational athletes during the Lower Quarter Y-Balance Test (YBT-LQ). A prospective observational repeated measures design was used to assess both intra- and inter-rater reliability. Forty males completed three trials, with two raters independently conducting assessments. sEMG signals from the vastus medialis, vastus lateralis, biceps femoris, and semitendinosus muscles were collected to calculate four coactivation indices (CoAIs) for different muscle pairs. The reliability evaluation of these CoAIs was based on intraclass correlation coefficients. The results revealed that the reliability of the CoAIs varied depending on the muscle pair, direction, and limb examined. The highest intra- and inter-rater reliability was noted for the left lower limb in the posterolateral direction. The lowest reliability was found for the right lower limb in the anterior direction. Statistical analyses indicated significant differences in specific CoAIs across different YBT-LQ directions. These findings highlight the potential of sEMG-based CoAIs for assessing knee muscle activity during the YBT-LQ in healthy male recreational athletes. However, choosing CoAIs is critical for reliable clinical and research assessments of knee stability and neuromuscular control.

Breast cancers can appear and progress rapidly, necessitating more frequent monitoring outside of hospital settings to significantly reduce mortality rates. Recently, there has been considerable interest in developing techniques for portable, user-friendly, and low-cost breast tumor monitoring applications, enabling frequent and cost-efficient examinations. Microwave technique-based breast cancer detection, which is based on differential dielectric properties of malignant and healthy tissues, is regarded as a promising solution for cost-effective breast tumor monitoring. This paper presents the development process of the first proof-of-concept of a breast tumor monitoring vest which is based on the microwave technique. Two unique vests are designed and evaluated on realistic 3D human tissue phantoms having different breast density types. Additionally, the measured results are verified using simulations carried out on anatomically realistic voxel models of the electromagnetic simulations. The radio channel characteristics are evaluated and analyzed between the antennas embedded in the vest in tumor cases and reference cases. Both measurements and simulation results show that the proposed vest can detect tumors even if only 1 cm in diameter. Additionally, simulation results show detectability with 0.5 cm tumors. It is observed that the detectability of breast tumors depends on the frequency, antenna selection, size of the tumors, and breast types, causing differences of 0.5–30 dB in channel responses between the tumorous and reference cases. Due to simplicity and cost-efficiency, the proposed channel analysis-based breast monitoring vests can be used for breast health checks in smaller healthcare centers and for user-friendly home monitoring which can prove beneficial in rural areas and developing countries.

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using this camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation were evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and with only minor intervention. In this case, the mean difference between noncontact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor was proven effective under ideal animal experimental conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with noncontact sensor systems for use in the neonatal intensive care unit.

Long-term daily-life body signal monitoring offers numerous advantages, such as timely response to health alerts, diseases monitoring, and reducing time and expenses related to clinical trials. Access to physiological data can be achieved with low-cost and comfortable wireless wearable sensors. In our previous publication, we reported a low-cost, easy to implement, and unobtrusive wireless resistive analog passive (WRAP) sensor to provide a feasible bio-signal monitoring technique by using a pair of printed spiral coils (PSC) in a near field connection. Sensitivity, defined as the response to the transducer, is a critical feature in the establishment of a reliable system. In the previous publication, we presented the utilization of a Genetic Algorithm to design a pair of coils and related components to maximize sensitivity. Although the coils' misalignment can significantly affect the optimized sensitivity, it was not incorporated into the optimization process. This paper focuses on optimizing the coils and components in order to maximize both their sensitivity and their resilience against movements of the PSC pair. In a square-shaped pair comprising a primary coil of 60 mm and a secondary coil of 20 mm dimensions, we found that the sensitivity is maximized at 1.3 mƱ for a 16 mm axial distance. Additionally, it remains above 0.65 mƱ within ±11.25 mm lateral and +14 mm axial displacements. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an auto-segmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM.

In recent years, the interest in medical monitoring for human health has been rapidly increasing due to widespread concern. Hydrogels are widely used in medical monitoring and other fields due to their excellent mechanical properties, electrical conductivity and adhesion. However, some of the non-degradable materials in hydrogels may cause some environmental damage and resource waste. Therefore, organic renewable natural polymers with excellent properties of biocompatibility, biodegradability, low cost and non-toxicity are expected to serve as an alternative to those non-degradable materials, and also provide a broad application prospect for the development of natural-polymer-based hydrogels as flexible electronic devices. This paper reviews the progress of research on many different types of natural-polymer-based hydrogels such as proteins and polysaccharides. The applications of natural-polymer-based hydrogels in body movement detection and biomedical monitoring are then discussed. Finally, the present challenges and future prospects of natural polymer-based hydrogels are summarized.

This manuscript explores the transformative potential of swellable microneedles (MNs) in drug delivery and diagnostics, addressing critical needs in medical treatment and monitoring. Innovations in hydrogel-integrated MN arrays facilitate controlled drug release, thereby expanding treatment options for chronic diseases and conditions that require precise dosage control. The review covers challenges, such as scalability, patient compliance, and manufacturing processes, as well as achievements in advanced manufacturing, biocompatibility, and versatile applications. Nonetheless, limitations in physiological responsiveness and long-term stability remain, necessitating further research in material innovation and integration with digital technologies. Future directions focus on expanding biomedical applications, material advancements, and regulatory considerations for widespread clinical adoption.

The rapid development and ubiquity of mobile and wearable devices promises to enable researchers to monitor users’ granular emotional data in a less intrusive manner. Researchers have used a wide variety of mobile and wearable devices for this purpose, and have proposed various approaches to sense users’ emotional states. In this survey, we utilise three established digital libraries (ACM Digital Library, IEEE Xplore Digital Library, and Springer Nature). We analysed and critically assessed the different approaches used in the three stages (perception, learning, inference) of a typical mobile emotion sensing framework, following a structured paper selection process. The contribution of this survey is three-fold; first, we document all the latest relevant literature on mobile emotion sensing research; second, we describe how mobile and wearable devices use their sensing and computing capabilities to monitor human emotions; third, we discuss challenges and opportunities of mobile emotion sensing to demonstrate the potential of this thriving field of research. [ABSTRACT FROM AUTHOR]

Affective computing strives to recognize a person's affective state (e.g., emotion, mood) based on what can be observed. However, electroencephalogram (EEG) and video technologies have not been widely adopted for daily life affect monitoring due to obtrusiveness and privacy concerns. Although the connection between affective states and biophysical data collected with unobtrusive wrist-worn wearables in lab settings has been established successfully, the number of studies for affect recognition in the wild is still limited, and current methods have not yet provided the accuracy necessary for robust applications. In this study, we propose a smart mood-aware emotion detection method. The proposed emotion recognition method extracts the most distinctive features from the physiological data and adds the output of the automated mood detection system as an input to improve performance. The effect of the division of self-report scales into emotion classes is also investigated. The proposed system obtained higher emotion recognition accuracies than most in-the-wild studies when we tested it with the daily life data collected from 14 participants for one week. [ABSTRACT FROM AUTHOR]

Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an autosegmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM. [ABSTRACT FROM AUTHOR]

Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system's ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disease characterized by both motor and non-motor features. The disease has a significant clinical impact on patients, families, and caregivers through its progressive degenerative effects on mobility and muscle control. Therefore, it is important to discuss the early diagnosis of PD to control the disease of PD patients and prolong their life. The primary objective of our research work was the early diagnosis of patients using Artificial Intelligence (AI). To accomplish that, we developed an Artificial Intelligence Model (model) that diagnoses PD using data collected from patient’s walks. This model is represented by a CNN (Convolutional Neural Network) that finds patterns of Parkinson’s disease in the collected data. The data is represented by multiple images (correlation plots) that are generated from raw data collected using a physiograph created by our team. For the model training, we used data from twelve patients and three healthy people that proved the accuracy of our model. To achieve better results from our AI model, we decided to augment our data by using a Generative Adversarial Network (GAN) that we modeled to generate images containing biomechanical information that represents healthy and patient patterns of Parkinson’s disease. After many hours of training and a series of changes applied to the model architecture, the GAN model demonstrated remarkable advancements in its ability to generate high-quality images that are very similar to the real data collected by us from our study group and generates real patterns of Parkinson’s disease represented in the images. The faithful replication of the correlations matrices by our GAN model helps our classification model to learn the Parkinson’s disease symptoms and patterns represented in the correlation matrices we generated. In the future, we want to increase the complexity of the GAN model and train it to generate specific patterns of Parkinson’s disease based on different inputs, to have a more complex generated dataset that will help us to develop future models.

Due to the high heterogeneity in outcome measures after total hip arthroplasty (THA), the prospective observational study investigated the relationships between symmetry-based (SBMs), performance-based (PBMs), and functional outcome measures in THA patients to determine necessary or redundant categories of tests. The study material consisted of 24 patients with end-stage hip osteoarthritis scheduled for THA. The patients were examined one day before surgery and consecutively on the 4th day, 9th day, and 10th week postoperatively using the SBMs (weight-bearing chair-rising test, measurements of the maximal isometric torque of the quadriceps muscle); the PBMs (10-m walk, timed up-and-go, and stair-climbing tests); and the functional outcome measure (Harris Hip Score). The results obtained in a given category of tests at different time points were compared, and the correlations between the tests were determined. The reliability of the outcome measures was determined. The results of tests in the studied categories statistically significantly (p < 0.05) improved at the 10th week postoperatively compared to preoperative results. No strong correlations were revealed between the three studied types of outcome measures in THA patients. Therefore, none of them can be considered redundant. It also means that the relevance of symmetry for a core measurement set to describe the domain function in THA patients must be further clarified. [ABSTRACT FROM AUTHOR]

Smart wearables, equipped with sensors monitoring physiological parameters, are becoming an integral part of our life. In this work, we investigate the possibility of utilizing such wearables to recognize emotions in the wild. In most reviewed papers, the authors apply a similar procedure consisting of participant recruitment, stimuli preparation and annotation, signal collection and processing, self-assessment, and machine learning model learning and validation. Besides, we identified seven emotion recognition scenarios and analyzed the transition from psychological models to machine learning tasks. Even though the majority of the research was performed in the laboratory environment, we conclude that studies in the field are feasible. They require especially: (1) new self-assessment and triggering procedures adjusted to a real-life scenario, (2) more attention to the machine learning process, including suitable deep learning architectures, revision of the data imbalance problem, and subject-specific data processing, (3) adequate validation procedures, (4) consideration of the model generalizability versus personalizability, (5) comfortable devices able to provide reliable measurements in motion. Additionally, more large-scale studies are necessary to increase result credibility. We also postulate actions toward replicability and comparability of the research. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring. [ABSTRACT FROM AUTHOR]

During the current crisis caused by the COVID-19 pandemic, Wearable IoT (WIoT) health devices have become essential resources for remote monitoring of the main physiological signs affected by this disease. As well as sensors, microprocessor, and wireless communication elements are widely studied, the power supply unit has the same importance for the WIoT technology, since the autonomy of the system between recharges is of great importance. This letter presents the design of the power supply system of a WIoT device capable of monitoring oxygen saturation and body temperature, sending the collected data to an IoT platform. The supply system is based on a three-stage block consisting of a rechargeable battery, battery charge controller, and dc voltage converter. The power supply system is designed and implemented as a prototype in order to test performance and efficiency. The results show that the designed block provides a stable supply voltage avoiding energy losses, which makes it an efficient and rapidly developing system. [ABSTRACT FROM AUTHOR]

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

ObjectiveTo improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient.Methods1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF.ResultsThe best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method.ConclusionThis work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients.SignificanceIn future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many studies are racing to develop improved algorithms towards precision patient monitoring, while little has been done to investigate the aspect of algorithm generalizability across different health institutions. Our group has been developing an evolving framework termed SuperAlarm that extracts multivariate patterns in data streams (monitor alarms, electronic health records and physiologic waveforms) of modern health enterprise to predict patient deterioration and has demonstrated great potential in mitigating alarm fatigue. In this study, we further investigate the generalizability of SuperAlarm by designing a comprehensive approach to achieve performance comparison in predicting in-hospital code blue (CB) events across two health institutions. SuperAlarm model trained with alarm data in one institution is tested on both internal and external test sets. Results show comparable performance with sensitivity up to 80% within one-hour window of events and over 90% in reduction of false alarms in both institutions. Cross-institutional performance agreement can be further improved by predicting a more stringent CB subtype (cardiopulmonary arrest), with internal sensitivity lying within 95% confident interval of external one up to 8-hour before event onset. The cross-institutional performance comparison offers first-hand knowledge on both advantages and challenges in generalizing a prediction algorithm across different institutions, which hold key information to guide the design of model training and deployment strategy.

A large number of health and wellness applications wish to monitor electrocardiogram signal (ECG) in the least intrusive way possible. The use of dry textile electrodes with high input impedance bio-amplifiers is a common technique. This paper presents a novel ultra-high input impedance buffer to obtain a reduced input capacitance and enhance the acquisition of results for dry or non-contact capacitive ECG electrodes. Based on different bootstrapping techniques, input stage biasing and unipolar power supply, an operational amplifier buffer is implemented on Printed Circuit Board (PCB) to provide a low cost solution. The solution considers the protections for electrostatic discharges (ESD) and filters for interferences, maintaining high input impedance design. A method to measure input impedances in the range of femtofarads is also proposed. This active electrode prototype presents a 191 fF input capacitance at 50 Hz. Different skin-electrode impedance cases are studied to obtain the Common Mode Rejection Ratio (CMRR). As a result, a value of 81.4 dB is obtained with zero mismatch and 76.1 dB at 50 Hz is obtained with a resistive mismatch of 1–2.2 $\text{M}\Omega $ . Moreover, ECG test is evaluated using a discrete back-end circuit, digital filtering and software display. Finally, the full ECG circuit has been simulated under an ESD transient of ±8 kV IEC 61000–4-2 waveform. This paper demonstrated that the proposed solution with standard discrete components fulfills the industry requirements for robust ultra-high impedance ECG sensor.

Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’ volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict” approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch” RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases.

Contact lenses are ideal conduits for continuous health monitoring. They have a long safety record, and they sit on the eye, where they have access to a range of biological signals. Making the transition from vision correction to biological monitoring, however, requires advances in technological development so the lenses not only detect and report signals accurately, but retain the high level of comfort that users have come to expect. [ABSTRACT FROM AUTHOR]

Extended reality has reached a critical point in biomedicine. The technology is accelerating and excitement about potential health care benefits is mounting but bridging the gap between envisioning what could be and actually making it happen is still a work in progress. Part of the challenge is coordinating all the major players to ease the development pipeline so virtual, augmented, and mixed reality can reach their potential in everything from diagnostics and surgeries to clinician training and telemedicine. [ABSTRACT FROM AUTHOR]

Continuous high perceived workload has a negative impact on the individual's well-being. Prior works focused on detecting the workload with medical-grade wearable systems in restricted settings, and the effect of applying deep learning techniques for perceived workload detection in the wild settings is not investigated. We present an unobtrusive, comfortable, pervasive, and affordable Long Short-Term Memory Network based continuous workload monitoring system based on a smartwatch application that monitors the perceived workload of individuals in the wild. We have recorded physiological data from daily life with perceived workload questionnaires from subjects in their real-life environments over a month. The model was trained and evaluated with the daily-life physiological data coming from different days, which makes it robust to daily changes in the heart rate variability that we use with accelerometer features to assess low and high workload. Our system has the capability of detecting perceived workload by using traditional and deep classifiers. We discussed the problems related to ’in the wild’ applications with the consumer-grade smartwatches. We showed that Long Short-Term Memory Network with feature extraction outperforms traditional classifiers and Convolutional Neural Networks on discrimination of low and high perceived workload with smartwatches in the wild. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile and wearable devices, it is increasingly possible to access users’ affective data in a more unobtrusive manner. On this basis, researchers have proposed various systems to recognize user’s emotional states. However, most of these studies rely on traditional machine learning techniques and a limited number of signals, leading to systems that either do not generalize well or would frequently lack sufficient information for emotion detection in realistic scenarios. In this paper, we propose a novel attention-based LSTM system that uses a combination of sensors from a smartphone (front camera, microphone, touch panel) and a wristband (photoplethysmography, electrodermal activity, and infrared thermopile sensor) to accurately determine user’s emotional states. We evaluated the proposed system by conducting a user study with 45 participants. Using collected behavioral (facial expression, speech, keystroke) and physiological (blood volume, electrodermal activity, skin temperature) affective responses induced by visual stimuli, our system was able to achieve an average accuracy of 89.2 percent for binary positive and negative emotion classification under leave-one-participant-out cross-validation. Furthermore, we investigated the effectiveness of different combinations of data signals to cover different scenarios of signal availability. [ABSTRACT FROM AUTHOR]

Physiological automatic personality recognition has been largely developed to model an individual’s personality trait from a variety of signals. However, few studies have tackled the problems of integration methodology from multiple observations into a single personality prediction. In this study, we focus on finding a novel learning architecture to model the personality trait under a Many-to-One scenario. We propose to integrate not only the information on the user but also consider the effect of the affective multimedia stimulus. Specifically, we present a novel Acoustic-Visual Guided Attentive Graph Convolutional Network for enhanced personality recognition. The emotional multimedia content guides the formation of the physiological responses into a graph-like structure to integrate latent inter-correlation among all responses toward affective multimedia. Then these graphs would be further processed by the Graph Convolutional Network (GCN) to jointly model instances and inter-correlation levels of the subject’s responses. We show that our model outperforms the current state of the art on two large public corpora for personality recognition. Further analysis reveals that there indeed exists a multimedia preference for inferring personality from physiology, and several frequency-domain descriptors in ECG and the tonic component in EDA are shown to be robust for automatic personality recognition. [ABSTRACT FROM AUTHOR]

Biophysical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG) are key to the sensing of vital parameters for wellbeing. Coincidentally, both ECG and PPG provide a different window into the same phenomena, namely, the cardiac cycle. However, they are used separately for various health monitoring tasks, which often have safety implications. As these sensors are subject to errors and failures, there is a need to develop methods that consider both sensors so as to enhance the safety of the health monitoring application. Considering this, we present the first approach to establish the key relationships between ECG and PPG signals formally. We combine formal runtime monitoring with statistical analysis and regression analysis for our results. [ABSTRACT FROM AUTHOR]

Long-term daily-life body signal monitoring offers numerous advantages, such as timely response to health alerts, diseases monitoring, and reducing time and expenses related to clinical trials. Access to physiological data can be achieved with low-cost and comfortable wireless wearable sensors. In our previous publication, we reported a low-cost, easy to implement, and unobtrusive wireless resistive analog passive (WRAP) sensor to provide a feasible bio-signal monitoring technique by using a pair of printed spiral coils (PSC) in a near field connection. Sensitivity, defined as the response to the transducer, is a critical feature in the establishment of a reliable system. In the previous publication, we presented the utilization of a Genetic Algorithm to design a pair of coils and related components to maximize sensitivity. Although the coils’ misalignment can significantly affect the optimized sensitivity, it was not incorporated into the optimization process. This paper focuses on optimizing the coils and components in order to maximize both their sensitivity and their resilience against movements of the PSC pair. In a square-shaped pair comprising a primary coil of 60 mm and a secondary coil of 20 mm dimensions, we found that the sensitivity is maximized at 1.3 mƱ for a 16 mm axial distance. Additionally, it remains above 0.65 mƱ within ±11.25 mm lateral and +14 mm axial displacements.

Type 1 Diabetes Mellitus (T1DM) is one of the most adverse diseases in the modern era; its treatment is mainly based on exogenous insulin injections. The scientific community has formulated strategies to improve insulin supply using state-of-the-art technology. Therefore, this article develops a multi-age glycemic control scheme, which can be implemented in an Artificial Pancreas (AP) device to enhance diabetics treatment. The procedure is based on the implementation of a neuro-fuzzy inverse optimal control (NF-IOC) algorithm on the Texas Instrument LAUNCHXL-F28069M development board; this controller communicates with the Uva/Padova simulator for diabetics' patients of different ages under predefined meal protocols running on a Personal Computer (PC). The novelty lies in the proposed NF-IOC capability to regulate glucose within safe levels for virtual populations of 10 adults, 10 adolescents and, 10 children.

LinderCare is a triaging medical device that combines a wristband for continuous patient vital monitoring and geolocation within the hospital for real-time patient triaging. This solution is designed to speed up the triaging process, easing the workload on medical personnel and enabling them to respond to emergency cases quickly. LinderCare can enhance service delivery by streamlining the triage process, benefiting patients and healthcare providers. By implementing this innovative system, we expect significant improvements in healthcare services, ultimately leading to better well-being for patients and healthcare providers in Kenya. [ABSTRACT FROM AUTHOR]

CardioCare helps people with health setbacks like strokes by providing real-time heart monitoring through a wearable device and mobile app. An algorithm analyses data to determine emergencies. The wearable device sounds an alarm and sends notifications for ambulance dispatch or ideal travel routes to medical facilities. It also keeps a QR-coded medical history for proactive care and faster medical response. [ABSTRACT FROM AUTHOR]

ChromaSense, an advanced wearable oximetry sensor, provides superior accuracy compared to traditional vital sign monitors by mitigating noise sources due to user variations (e.g., skin tone, skin thickness, body mass index (BMI), perfusion index), environmental perturbations (e.g., motion artifact, sensor placement), and environmental factors. With advanced sensors and intelligent algorithms, it not only offers precise oxygen saturation readings but also measures important vital signs, such as heart rate, respiration rate, heart rate variability, blood pressure and arterial stiffness based on signal quality of the of recorded photoplethysmogram (PPG) signals. [ABSTRACT FROM AUTHOR]