Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.

Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins.

A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact accuracy and robustness of the measurements. A deep learning model based on the U-Net architecture is proposed to recover SCG signals contaminated by motion noise induced by walking. The model performance was evaluated through qualitative visualization, as well as quantitative analyses. Quantitative analyses included distance-based comparisons before and after applying our model. Analyses also included assessments of the model's efficacy in improving the performance of downstream tasks related to health parameter estimation during walking. Experimental findings revealed that the denoising model improved similarity to clean signals by approximately 90%. The performance of the model in enhancing heart rate estimation demonstrated a mean absolute error of 1.21 BPM and a root-mean-squared error (RMSE) of 1.97 BPM during walking after denoising with 9.16 BPM and 10.38 BPM improvements, respectively, compared to without denoising. Furthermore, the RMSEs of aortic opening and aortic closing time estimation after denoising for one dataset with catheter ground truth were 7.29 ms and 19.71 ms during walking, respectively, with 50.33 ms and 51.91 ms RMSE improvements compared to without denoising. And for another dataset with ICG-derived PEP ground truth, the RMSE of aortic opening time estimation after denoising was 10.21 ms during walking, with 38.74 ms RMSE improvement compared to without denoising. The proposed model attenuates motion noise from corrupted SCG signals while preserving cardiac information. This development paves the way for improved ambulatory cardiac health monitoring using wearable accelerometers during daily activities.

Improving Biological Joint Moment Estimation During Real-World Tasks with EMG and Instrumented Insoles.

OBJECTIVE: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. METHODS: We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. RESULTS: Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p<0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p<0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p<0.05) which was significantly higher than either modality individually (p<0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p<0.05). CONCLUSION: These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. SIGNIFICANCE: Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

A Physical Model-Based Approach to One-Point Calibration of Pulse Transit Time to Blood Pressure.

OBJECTIVE: To develop a novel physical model-based approach to enable 1-point calibration of pulse transit time (PTT) to blood pressure (BP). METHODS: The proposed PTT-BP calibration model is derived by combining the Bramwell-Hill equation and a phenomenological model of the arterial compliance (AC) curve. By imposing a physiologically plausible constraint on the skewness of AC at positive and negative transmural pressures, the number of tunable parameters in the PTT-BP calibration model reduces to 1. Hence, as opposed to most existing PTT-BP calibration models requiring multiple (≥2) PTT-BP measurements to personalize, the PTT-BP calibration model can be personalized to an individual subject using a single PTT-BP measurement pair. Equipped with the physically relevant PTT-AC and AC-BP relationships, the proposed approach may serve as a universal means to calibrate PTT to BP over a wide BP range. The validity and proof-of-concept of the proposed approach were evaluated using PTT and BP measurements collected from 22 healthy young volunteers undergoing large BP changes. RESULTS: The proposed approach modestly yet significantly outperformed an empiric linear PTT-BP calibration with a group-average slope and subject-specific intercept in terms of bias (5.5 mmHg vs 6.4 mmHg), precision (8.4 mmHg vs 9.4 mmHg), mean absolute error (7.8 mmHg vs 8.8 mmHg), and root-mean-squared error (8.7 mmHg vs 10.3 mmHg, all in the case of diastolic BP). CONCLUSION: We demonstrated the preliminary proof-of-concept of an innovative physical model-based approach to one-point PTT-BP calibration. SIGNIFICANCE: The proposed physical model-based approach has the potential to enable more accurate and convenient calibration of PTT to BP.

Noninvasive Vagal Nerve Stimulation for Opioid Use Disorder.

Background: Opioid Use Disorder (OUD) is an escalating public health problem with over 100,000 drug overdose-related deaths last year most of them related to opioid overdose, yet treatment options remain limited. Non-invasive Vagal Nerve Stimulation (nVNS) can be delivered via the ear or the neck and is a non-medication alternative to treatment of opioid withdrawal and OUD with potentially widespread applications. Methods: This paper reviews the neurobiology of opioid withdrawal and OUD and the emerging literature of nVNS for the application of OUD. Literature databases for Pubmed, Psychinfo, and Medline were queried for these topics for 1982-present. Results: Opioid withdrawal in the context of OUD is associated with activation of peripheral sympathetic and inflammatory systems as well as alterations in central brain regions including anterior cingulate, basal ganglia, and amygdala. NVNS has the potential to reduce sympathetic and inflammatory activation and counter the effects of opioid withdrawal in initial pilot studies. Preliminary studies show that it is potentially effective at acting through sympathetic pathways to reduce the effects of opioid withdrawal, in addition to reducing pain and distress. Conclusions: NVNS shows promise as a non-medication approach to OUD, both in terms of its known effect on neurobiology as well as pilot data showing a reduction in withdrawal symptoms as well as physiological manifestations of opioid withdrawal.

Leveraging Physiological Markers to Quantify the Transient Effects of Traumatic Stress and Non-Invasive Neuromodulation.

Millions around the world suffer from traumatic stress (stress caused by traumatic memories). Transcutaneous cervical vagus nerve stimulation (tcVNS) has been shown to counteract physiological changes associated with traumatic stress. However, little is known regarding the approximate timecourse of tcVNS effects. This knowledge of how quickly tcVNS takes effect is needed to optimize closed-loop tcVNS systems that can mitigate traumatic stress in a timely manner. To address this gap, we studied N=26 participants with history of prior trauma. Participants wore electrocardiogram, photoplethysmogram, seismocardiogram, and respiratory effort sensors throughout a double-blind protocol involving traumatic stress and active tcVNS (n=12) or sham stimulation (n=14). From the physiological signals, we extracted cardiovascular and respiratory markers and studied their dynamics during the traumatic stress and stimulation conditions. We decoupled the short-term transient responses from longer-term cumulative changes by centering each condition's response with respect to data immediately prior to the condition. We thereby elucidate a diverse set of transient physiological responses to tcVNS and traumatic stress. These responses demonstrate that tcVNS-induced changes occur within seconds and have the potential to reduce acute physiological manifestations of traumatic stress.Clinical relevance- Traumatic stress can overpower an individual within seconds and often occurs outside the clinic. This analysis focuses on transient physiological responses to traumatic memories and tcVNS captured using multimodal physiological sensing. We demonstrate that tcVNS-induced changes occur within seconds and have the potential to mitigate some of the short-term effects of traumatic stress.

Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features.

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance- This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Impedance cardiogram based exploration of cardiac mechanisms in post-traumatic stress disorder during trauma recall.

Post-traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real-time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non-PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory-based traumatic reminder stressor. To identify the parameters describing the cardiac mechanisms by which trauma reminders can create stress on the heart, we utilized a feature selection mechanism along with a random forest classifier distinguishing PTSD and non-PTSD. We extracted 99 parameters, including 76 biosignal-based and 23 sociodemographic, medical history, and psychiatric diagnosis features. A subject/twin-wise stratified nested cross-validation procedure was used for parameter tuning and model assessment to identify the important parameters. The identified parameters included biomarkers such as pre-ejection period, acceleration index, velocity index, Heather index, and several physiology-agnostic features. These identified parameters during trauma recall suggested a combination of increased sympathetic nervous system (SNS) activity and deteriorated cardiac contractility that may increase the heart failure risk for PTSD. This indicates that the PTSD symptom activation associates with real-time reductions in several cardiac contractility measures despite SNS activation. This finding may be useful in future cardiac prevention efforts.

Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection.

We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

Transcutaneous vagal nerve stimulation modulates stress-induced plasma ghrelin levels: A double-blind, randomized, sham-controlled trial.

BACKGROUND: Transcutaneous cervical vagus nerve stimulation (tcVNS) has emerged as a potential treatment strategy for patients with stress-related psychiatric disorders. Ghrelin is a hormone that has been postulated to be a biomarker of stress. While the mechanisms of action of tcVNS are unclear, we hypothesized that tcVNS reduces the levels of ghrelin in response to stress. METHODS: Using a randomized double-blind approach, we studied the effects of tcVNS on ghrelin levels in individuals with a history of exposure to traumatic stress. Participants received either sham (n = 29) or active tcVNS (n = 26) after exposure to acute personalized traumatic script stress and mental stress challenges (public speech, mental arithmetic) over a three day period. RESULTS: There were no significant differences in the levels of ghrelin between the tcVNS and sham stimulation groups at either baseline or in the absence of trauma scripts. However, tcVNS in conjunction with personalized traumatic scripts resulted in lower ghrelin levels compared to the sham stimulation group (265.2 ± 143.6 pg/ml vs 478.7 ± 349.2 pg/ml, P = 0.01). Additionally, after completing the public speaking and mental arithmetic tests, ghrelin levels were found to be lower in the group receiving tcVNS compared to the sham group (293.3 ± 102.4 pg/ml vs 540.3 ± 203.9 pg/ml, P = 0.009). LIMITATIONS: Timing of ghrelin measurements, and stimulation of only left vagus nerve. CONCLUSION: tcVNS decreases ghrelin levels in response to various stressful stimuli. These findings are consistent with a growing literature that tcVNS modulates hormonal and autonomic responses to stress.

Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study.

Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life. This study aimed to evaluate the effectiveness of three well-established transcutaneous PNS modalities in reducing physiological manifestations of stress compared to a sham: auricular and cervical Vagus Nerve Stimulation (taVNS and tcVNS), and Median Nerve Stimulation (tMNS). Using a single-blind sham-controlled crossover study with four visits, we compared the stress mitigation effectiveness of taVNS, tcVNS, and tMNS, quantified through physiological markers derived from five physiological signals peripherally measured on 19 young healthy volunteers. Participants underwent three acute mental and physiological stressors while receiving stimulation. Blinding effectiveness was assessed via subjective survey. taVNS and tMNS relative to sham resulted in significant changes that suggest a reduction in sympathetic outflow following the acute stressors: Left Ventricular Ejection Time Index (LVETI) shortening (tMNS: p = 0.007, taVNS: p = 0.015) and Pre-Ejection Period (PEP)-to-LVET ratio (PEP/LVET) increase (tMNS: p = 0.044, taVNS: p = 0.029). tMNS relative to sham also reduced Pulse Pressure (PP; p = 0.032) and tonic EDA activity (tonicMean; p = 0.025). The nonsignificant blinding survey results suggest these effects were not influenced by placebo. taVNS and tMNS effectively reduced stress-induced sympathetic arousal in wearable-compatible physiological signals, motivating their future use in novel personalized stress therapies to improve quality of life.

Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study.

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis. In this work, LS and Impedance Pneumography (IP) measurements were obtained from 10 healthy volunteers while performing normal and forced-expiratory (FE) breathing maneuvers using our wearable IP and respiratory sounds (WIRS) system. Simultaneous auscultation was performed with the Eko CORE stethoscope (EKO). The breathing-phase context was extracted from the IP signals and used to compute phase-by-phase (Inspiratory (I), expiratory (E), and their ratio (I:E)) and breath-by-breath acoustic features. Their individual and added value was then elucidated through machine learning analysis. We found that the phase-contextualized features effectively captured the underlying acoustic differences between deep and FE breaths, yielding a maximum F1 Score of 84.1 ±11.4% with the phase-by-phase features as the strongest contributors to this performance. Further, the individual phase-contextualized models outperformed the traditional breath-by-breath models in all cases. The validity of the results was demonstrated for the LS obtained with WIRS, EKO, and their combination. These results suggest that incorporating breathing-phase context may enhance computerized LS analysis. Hence, multimodal sensing systems that enable this, such as WIRS, have the potential to advance LS clinical utility beyond traditional manual auscultation and improve patient care.

Effect of transcutaneous cervical vagus nerve stimulation on declarative and working memory in patients with Posttraumatic Stress Disorder (PTSD): A pilot study.

BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with changes in multiple neurophysiological systems, including verbal declarative memory deficits. Vagus Nerve Stimulation (VNS) has been shown in preliminary studies to enhance function when paired with cognitive and motor tasks. The purpose of this study was to analyze the effect of transcutaneous cervical VNS (tcVNS) on attention, declarative and working memory in PTSD patients. METHODS: Fifteen PTSD patients were randomly assigned to active tcVNS (N = 8) or sham (N = 7) stimulation in a double-blinded fashion. Memory assessment tests including paragraph recall and N-back tests were performed to assess declarative and working memory function when paired with active/sham tcVNS once per month in a longitudinal study during which patients self-administered tcVNS/sham twice daily. RESULTS: Active tcVNS stimulation resulted in a significant improvement in paragraph recall performance following pairing with paragraph encoding for PTSD patients at two months (p < 0.05). It resulted in a 91 % increase in paragraph recall performance within group (p = 0.03), while sham tcVNS exhibited no such trend in performance improvement. In the N-back study, positive deviations in accuracy, precision and recall measures on different day visits (7,34,64,94) of patients with respect to day 1 revealed a pattern of better performance of the active tcVNS population compared to sham VNS which did not reach statistical significance. LIMITATIONS: Our sample size was small. CONCLUSIONS: These preliminary results suggest that tcVNS improves attention, declarative and working memory, which may improve quality of life and productivity for patients with PTSD. Future studies are required to confirm these results.

Mid-Activity and At-Home Wearable Bioimpedance Elucidates an Interpretable Digital Biomarker of Muscle Fatigue.

OBJECTIVE: Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue. Unfortunately, the current state-of-the-art wearable systems for muscle fatigue tracking suffer from either low specificity or poor usability. METHODS: We propose using dual-frequency bioimpedance analysis (DFBIA) to non-invasively assess intramuscular fluid dynamics and thereby muscle fatigue. A wearable DFBIA system was developed to measure leg muscle fatigue of 11 individuals during a 13-day protocol consisting of exercise and unsupervised at-home portions. RESULTS: We derived a digital biomarker of muscle fatigue, fatigue score, from the DFBIA signals that was able to estimate the percent reduction in muscle force during exercise with repeated-measures Pearson's r = 0.90 and mean absolute error (MAE) of 3.6%. This fatigue score also estimated delayed onset muscle soreness with repeated-measures Pearson's r = 0.83 and MAE = 0.83. Using at-home data, DFBIA was strongly associated with absolute muscle force of participants (n = 198, p < 0.001). CONCLUSION: These results demonstrate the utility of wearable DFBIA for non-invasively estimating muscle force and pain through the changes in intramuscular fluid dynamics. SIGNIFICANCE: The presented approach may inform development of future wearable systems for quantifying muscle health and provide a novel framework for athletic performance optimization and injury prevention.

Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: a clinical validation in an extended study population.

BACKGROUND: Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. FINDINGS: A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. CONCLUSION: Joint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy.

MagTrack: A Wearable Tongue Motion Tracking System for Silent Speech Interfaces.

PURPOSE: Current electromagnetic tongue tracking devices are not amenable for daily use and thus not suitable for silent speech interface and other applications. We have recently developed MagTrack, a novel wearable electromagnetic articulograph tongue tracking device. This study aimed to validate MagTrack for potential silent speech interface applications. METHOD: We conducted two experiments: (a) classification of eight isolated vowels in consonant-vowel-consonant form and (b) continuous silent speech recognition. In these experiments, we used data from healthy adult speakers collected with MagTrack. The performance of vowel classification was measured by accuracies. The continuous silent speech recognition was measured by phoneme error rates. The performance was then compared with results using data collected with commercial electromagnetic articulograph in a prior study. RESULTS: The isolated vowel classification using MagTrack achieved an average accuracy of 89.74% when leveraging all MagTrack signals (x, y, z coordinates; orientation; and magnetic signals), which outperformed the accuracy using commercial electromagnetic articulograph data (only y, z coordinates) in our previous study. The continuous speech recognition from two subjects using MagTrack achieved phoneme error rates of 73.92% and 66.73%, respectively. The commercial electromagnetic articulograph achieved 64.53% from the same subject (66.73% using MagTrack data). CONCLUSIONS: MagTrack showed comparable results with the commercial electromagnetic articulograph when using the same localized information. Adding raw magnetic signals would improve the performance of MagTrack. Our preliminary testing demonstrated the potential for silent speech interface as a lightweight wearable device. This work also lays the foundation to support MagTrack's potential for other applications including visual feedback-based speech therapy and second language learning.

Real-Time Seismocardiogram Feature Extraction Using Adaptive Gaussian Mixture Models.

Wearable systems can provide accurate cardiovascular evaluations by estimating hemodynamic indices in real-time. Key hemodynamic parameters can be non-invasively estimated using the seismocardiogram (SCG), a cardiomechanical signal whose features link to cardiac events like aortic valve opening (AO) and closing (AC). However, tracking a single SCG feature is unreliable due to physiological changes, motion artifacts, and external vibrations. This work proposes an adaptable Gaussian Mixture Model (GMM) to track multiple AO/AC correlated features in quasi-real-time from the SCG. The GMM calculates the likelihood of an extremum being an AO/AC feature for each SCG beat. The Dijkstra algorithm selects heartbeat-related extrema, and a Kalman filter updates the GMM parameters while filtering features. Tracking accuracy is tested on a porcine hypovolemia dataset with varying noise levels. Blood volume loss estimation accuracy is also evaluated using the tracked features on a previously developed model. Experimental results show a 4.5 ms tracking latency and average root mean square errors (RMSE) of 1.47 ms for AO and 7.67 ms for AC at 10 dB noise, and 6.18 ms for AO and 15.3 ms for AC at -10 dB noise. When considering all AO/AC correlated features, the combined RMSE remains in similar ranges, specifically 2.70 ms for AO and 11.91 ms for AC at 10 dB noise, and 7.50 ms for AO and 16.35 ms for AC at -10 dB noise. The proposed algorithm offers low latency and RMSE for all tracked features, making it suitable for real-time processing. These systems enable accurate, timely extraction of hemodynamic indices for many cardiovascular monitoring applications, including trauma care in field settings.

Wearable Biosensors in Congenital Heart Disease: Needs to Advance the Field.

Traditional measures of clinical status and physiology have generally been based in health care settings, episodic, short in duration, and performed at rest. Wearable biosensors provide an opportunity to obtain continuous non-invasive physiologic data from patients with congenital heart disease (CHD) in the real-world setting, over longer durations, and across varying levels of activity. However, there are significant technical limitations to the use of wearable biosensors in CHD. Here, we review current applications of wearable biosensors in CHD; how clinical and research uses of wearable biosensors must consider various CHD physiologies; the technical challenges in developing wearable biosensors for CHD; and special considerations for digital biomarkers in CHD.

Validating Adhesive-Free Bioimpedance of the Leg in Mid-Activity and Uncontrolled Settings.

OBJECTIVE: Musculoskeletal health monitoring is limited in everyday settings where patient symptoms can substantially change - delaying treatment and worsening patient outcomes. Wearable technologies aim to quantify musculoskeletal health outside clinical settings but sensor constraints limit usability. Wearable localized multi-frequency bioimpedance assessment (MFBIA) shows promise for tracking musculoskeletal health but relies on gel electrodes, hindering extended at-home use. Here, we address this need for usable technologies for at-home musculoskeletal health assessment by designing a wearable adhesive-free MFBIA system using textile electrodes in extended uncontrolled mid-activity settings. METHODS: An adhesive-free multimodal wearable leg MFBIA system was developed in-lab under realistic conditions (5 participants, 45 measurements). Mid-activity textile and gel electrode MFBIA was compared across multiple compound movements (10 participants). Accuracy in tracking long-term changes in leg MFBIA was assessed by correlating gel and textile MFBIA simultaneously recorded in uncontrolled settings (10 participants, 80+ measurement hours). RESULTS: Mid-activity MFBIA measurements with textile electrodes agreed highly with (ground truth) gel electrode measurements (average [Formula: see text], featuring <1-Ohm differences (0.618 ± 0.340 Ω) across all movements. Longitudinal MFBIA changes were successfully measured in extended at-home settings (repeated measures r = 0.84). Participant responses found the system to be comfortable and intuitive (8.3/10), and all participants were able to don and operate the system independently. CONCLUSION: This work demonstrates wearable textile electrodes can be a viable substitute for gel electrodes when monitoring leg MFBIA in dynamic, uncontrolled settings. SIGNIFICANCE: Adhesive-free MFBIA can improve healthcare by enabling robust wearable musculoskeletal health monitoring in at-home and everyday settings.