Measurement of Chest Physiological Signals using Wirelessly Coupled Bio-Impedance Patches

iEat: automatic wearable dietary monitoring with bio-impedance sensing

Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people's dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

Arterial Pulse Localization with Varying Electrode Sizes and Spacings in Wrist-Worn Bioimpedance Sensing

Bioimpedance has emerged as a promising modality to continuously monitor hemodynamic and respiratory physiological parameters through a non-invasive skin-contact approach. Bioimpedance sensors placed at the radial zone of the volar wrist provide sensitive operation to the blood flow of the underlying radial artery. The translation of bioimpedance systems into medical-grade settings for continuous hemodynamic monitoring, however, presents challenges when constraining the necessary sensing components to a minimal form factor while maintaining sufficient accuracy and precision of measurements. Thus, it is important to understand the effects of electrode configuration on bioimpedance signals when reducing them to a wearable form factor. Previous work regarding electrode configurations in bioimpedance does not address wearable constraints, nor do they focus on electrodes viable for wearable applications. In this study, we present empirical evidence of the effects of dry silver electrode sizes and spacings on the specificity and sensitivity of a wrist-worn bioimpedance sensor array. We found that wrist-worn bioimpedance systems for hemodynamic monitoring would benefit from reduced injection electrode spacings (up to a 392% increase in signal amplitude with a 50% decrease in spacing), increased sensing electrode spacings, and decreased electrode surface areas. Clinical Relevance - The work directly contributes towards the development of cuffless continuous blood pressure monitors with applications in clinical and ambulatory settings.

A Flexible and Miniaturized Chest Patch for Real-time PPG/ECG/Bio-Z Monitoring

We proposed a lightweight and wearable chest patch for real-time monitoring of three vital signals, photoplethysmography (PPG), electrocardiography (ECG), and bioimpedance (Bio-Z). It comprises a flexible electrode patch and a miniaturized wireless signal acquisition module. Heart rate (HR), heart rate variability (HRV), and blood pressure (BP) can be extracted from the raw signals. The flexible electrode patch is comfortable for the user while maintaining stable contact with human skin, guaranteeing the wearability. Size of the signal acquisition module is only 17.3mm×14.5mm×9mm, and it weighs only 3.2g, including an 80mAh lithium polymer battery, which keeps the entire patch working for more than 4 hours. A host controller, involving a graphic user interface (GUI) is developed to receive and visualize the data from the chest patch. The proposed device successfully collected three vital signals with high signal quality and showed its potential in healthcare applications.

Non-Invasive Cardiac and Respiratory Activity Assessment From Various Human Body Locations Using Bioimpedance

Objective: Bioimpedance sensing is a powerful technique that measures the tissue impedance and captures important physiological parameters including blood flow, lung movements, muscle contractions, body fluid shifts, and other cardiovascular parameters. This paper presents a comprehensive analysis of the modality at different arterial (ulnar, radial, tibial, and carotid arteries) and thoracic (side-rib cage and top thoracolumbar fascia) body regions and offers insights into the effectiveness of capturing various cardiac and respiratory activities. Methods: We assess the bioimpedance performance in estimating inter-beat (IBI) and inter -breath intervals (IBrI) on six-hours of data acquired in a pilot-study from five healthy participants at rest. Results: Overall, we achieve mean errors as low as 0.003 ± 0.002 and 0.67 ± 0.28 seconds for IBI and IBrI estimations, respectively. Conclusions: The results show that bioimpedance can be effectively used to monitor cardiac and respiratory activities both at limbs and upper body and demonstrate a strong potential to be adopted by wearables that aim to provide high-fidelity physiological sensing to address precision medicine needs.

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing

Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable counterparts that are more familiar to healthcare providers. For instance, bio-impedance (Bio-Z) is a conveniently collected modality for measuring physiological parameters but is not highly interpretable. Thus, translating it to a well-known modality such as electrocardiogram (ECG) improves the usability of Bio-Z in wearables. Deep learning solutions are well-suited for this task given complex relationships between modalities generated by distinct processes. However, current algorithms usually train a single model for all users that results in ignoring cross-user variations. Retraining for new users usually requires collecting abundant labeled data, which is challenging in healthcare applications. In this paper, we build a modality translation framework to translate Bio-Z to ECG by learning personalized user information without training several independent architectures. Furthermore, our framework is able to adapt to new users in testing using very few samples. We design a meta-learning framework that contains shared and user-specific parameters to account for user differences while learning from the similarity amongst user signals. In this model, a meta-learner approximated by a neural network learns how to learn user-specific parameters and can efficiently update them in testing. Our experiments show that the proposed model reduces the normalized root mean square error (NRMSE) by 41% compared to training a single model for all users and by 36% compared to training independent models for each user. When adapting the model to new users, our model outperforms fine-tuning a pre-trained model through back-propagation by 40% using as few as two new samples in testing.

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.

A Wrist-worn Respiration Monitoring Device using Bio-Impedance

In the US alone, 22 million individuals suffer from obstructive sleep apnea (OSA), with 80% of the cases symptoms undiagnosed. Hence, there is an unmet need to continuously and unobtrusively monitor respiration and detect possible occurrences of apnea. Recent advancements in wearable biomedical technology can enable the capture of the periodicity of the heart pressure pulse from a wrist-worn device. In this paper, we propose a bio-impedance (Bio-Z)-based respiration monitoring system. We establish close contact with the skin using gold e-tattoos with a 35 mm by 5 mm active sensing area. We extracted the respiration from the wrist Bio-Z signal leveraging three different techniques and showed that we can detect the start of each respiration beat with an average root mean square error (RMSE) less than 13% and mean error of 0.3% over five subjects.

ImpediBands: Body Coupled Bio-Impedance Patches for Physiological Sensing Proof of Concept

Continuous and robust monitoring of physiological signals is essential in improving the diagnosis and management of cardiovascular and respiratory diseases. The state-of-the-art systems for monitoring vital signs such as heart rate, heart rate variability, respiration rate, and other hemodynamic and respiratory parameters use often bulky and obtrusive systems or depend on wearables with limited sensing methods based on repetitive properties of the signals rather than the morphology. Moreover, multiple devices and modalities are typically needed for capturing various vital signs simultaneously. In this paper, we introduce ImpediBands: small-sized distributed smart bio-impedance (Bio-Z) patches, where the communication between the patches is established through the human body, eliminating the need for electrical wires that would create a common potential point between sensors. We use ImpediBands to collect instantaneous measurements from multiple locations over the chest at the same time. We propose a blind source separation (BSS) technique based on the second-order blind identification (SOBI) followed by signal reconstruction to extract heart and lung activities from the Bio-Z signals. Using the separated source signals, we demonstrate the performance of our system via providing strong confidence in the estimation of heart and respiration rates with low RMSE (HR_RMSE = 0.579 beats per minute, RR_RMSE = 0.285 breaths per minute), and high correlation coefficients (r_HR = 0.948, r_RR = 0.921).

Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors

We introduce a novel approach for robust estimation of physiological parameters such as interbeat interval (IBI) and heart rate variability (HRV) from cardiac signals captured with wearable sensors in the presence of motion artifacts. Motion artifact due to physical exercise is known as a major source of noise that contributes to a significant decline in the performance of IBI and HRV estimation techniques for cardiac monitoring in free-living environments. Therefore, developing robust estimation algorithms is essential for utilization of wearable sensors in daily life situations. The proposed approach includes two algorithmic components. First, we propose a combinatorial technique to select characteristic points that define heartbeats in noisy signals in time domain. The heartbeat detection problem is defined as a shortest path search problem on a direct acyclic graph that leverages morphological features of the cardiac signals by taking advantage of the time-continuity of heartbeats - each heartbeat ends with the starting point of the next heartbeat. The graph is constructed with vertices and edges representing candidate morphological features and IBIs, respectively. Second, we propose a fusion technique to combine physiological parameters estimated from different morphological features using the shortest path algorithm to obtain more accurate IBI/HRV estimations. We evaluate our techniques on motion-corrupted photoplethysmogram and electrocardiogram signals. Our results indicate that the estimated IBIs are highly correlated with the ground truth (r = 0.89) and detected HRV parameters indicate high correlation with the true HRV parameters. Furthermore, our findings demonstrate that the developed fusion technique, which utilizes different morphological features, achieves a correlation coefficient that is at least 3% higher than that obtained using single physiological characteristic.