Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking

Proposing an International Standard Accident Number for Interconnecting Information and Communication Technology Systems of the Rescue Chain

Background  The rapid dissemination of smart devices within the internet of things (IoT) is developing toward automatic emergency alerts which are transmitted from machine to machine without human interaction. However, apart from individual projects concentrating on single types of accidents, there is no general methodology of connecting the standalone information and communication technology (ICT) systems involved in an accident: systems for alerting (e.g., smart home/car/wearable), systems in the responding stage (e.g., ambulance), and in the curing stage (e.g., hospital). Objectives  We define the International Standard Accident Number (ISAN) as a unique token for interconnecting these ICT systems and to provide embedded data describing the circumstances of an accident (time, position, and identifier of the alerting system). Materials and methods  Based on the characteristics of processes and ICT systems in emergency care, we derive technological, syntactic, and semantic requirements for the ISAN, and we analyze existing standards to be incorporated in the ISAN specification. Results  We choose a set of formats for describing the embedded data and give rules for their combination to generate an ISAN. It is a compact alphanumeric representation that is generated easily by the alerting system. We demonstrate generation, conversion, analysis, and visualization via representational state transfer (REST) services. Although ISAN targets machine-to-machine communication, we give examples of graphical user interfaces. Conclusion  Created either locally by the alerting IoT system or remotely using our RESTful service, the ISAN is a simple and flexible token that enables technological, syntactic, and semantic interoperability between all ICT systems in emergency care

Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN)

Thus far, emergency calls are answered by human operators who interview the calling person in order to obtain all relevant information. In the near future-based on the Internet of (Medical) Things (IoT, IoMT)-accidents, emergencies, or adverse health events will be reported automatically by smart homes, smart vehicles, or smart wearables, without any human in the loop. Several parties are involved in this communication: the alerting system, the rescue service (responding system), and the emergency department in the hospital (curing system). In many countries, these parties use isolated information and communication technology (ICT) systems. Previously, the International Standard Accident Number (ISAN) has been proposed to securely link the data in these systems. In this work, we propose an ISAN-based communication platform that allows semantically interoperable information exchange. Our aims are threefold: (i) to enable data exchange between the isolated systems, (ii) to avoid data misinterpretation, and (iii) to integrate additional data sources. The suggested platform is composed of an alerting, responding, and curing system manager, a workflow manager, and a communication manager. First, the ICT systems of all parties in the early rescue chain register with their according system manager, which tracks the keep-alive. In case of emergency, the alerting system sends an ISAN to the platform. The responsible rescue services and hospitals are determined and interconnected for platform-based communication. Next to the conceptual design of the platform, we evaluate a proof-of-concept implementation according to (1) the registration, (2) channel establishment, (3) data encryption, (4) event alert, and (5) information exchange. Our concept meets the requirements for scalability, error handling, and information security. In the future, it will be used to implement a virtual accident registry

Time-Frequency Analysis of Optical and Electrical Cardiac Signals with Applications in Ultra-High-Field MRI

Electrocardiography (ECG) is the standard method for assessing the state of the cardiovascular system non-invasively. In the context of magnetic resonance imaging (MRI) the ECG signal is used for cardiac monitoring and triggering, i.e., the acquisition of images synchronized to the cardiac cycle. However, ECG acquisition is impeded by the static and dynamic magnetic fields which alter the measured voltages and may reduce signal-to-noise ratio (SNR), leading to false alarms during cardiac monitoring or to image artifacts during cardiac triggering. A major source of noise is the magnetohydrodynamic (MHD) effect as it is proportional to field strength and represents a key challenge in application of ultra-high-field (UHF) MRI >=7 T. In this work, two approaches for overcoming these limitations are proposed: i) Development of a hardware and software system based on the principal of photoplethysmography imaging (PPGi) as an optical method for acquiring a cardiac signal, and ii) development of algorithms for detecting fiducial points in ECG signals despite the low SNR. Due to the non-stationary dynamics of the cardiac activity, extraction of information from both types of signals is realized by time-frequency analysis. The feasibility of the PPGi system for heart rate measurement is demonstrated in an UHF MRI study where PPGi signals acquired from the forehead outperformed ECG in terms of accuracy and reliability. Application of the system at the sole of the foot for triggering allowed producing UHF MR angiography images with a quality similar to pulse oximetry triggering in a healthy volunteer. During the work on ECG algorithms, a general framework for multiscale parameter estimation (msPE) is developed. First, it is customized for delineation of (non-MR related) ECG signals from the reference QT database. Second, it is used for QRS detection in ECG signals acquired within UHF MRI. Processing the QT database shows that msPE is well-suited for processing ECG waves and outperforms state-of-the-art algorithms w.r.t. sensitivity in 4/9 fiducial points. QRS detection in ECG signals acquired within 7 T is shown to be robust with a sensitivity >=95% and an accuracy degraded by 1 ms compared to ECG signals without MHD noise. In summary, the proposed methods may provide useful steps towards unlocking the full potential of cardiac assessment in UHF MRI

Parameter estimation based on scale-dependent algebraic expressions and scale-space fitting

We present our results of applying wavelet theory to the classic problem of estimating the unknown parameters of a model function subject to noise. The model function studied in this context is a generalization of the second-order Gaussian derivative of which the Gaussian function is a special case. For all five model parameters (amplitude, width, location, baseline, undershoot-size), scale-dependent algebraic expressions are derived. Based on this analytical framework, our first method estimates all parameters by substituting into a given expression numerically obtained values, such as the zero-crossings of the multiscale decompositions of the noisy input signal, using Gaussian derivative wavelets. Our second method takes these estimates as starting values for iterative least-squares optimization to fit our algebraic zero-crossing model to observed numeric zero-crossings in scale-space. For evaluation, we apply our method together with three reference methods to the three-parameter Gaussian model function. The results show that our method is on average 3.7 times more accurate than the respective best reference method for signal-to-noise ratios (SNR) from −10 to 70 dB, using a synthetic test scenario proposed by a competitor. For our full five-parameter model, we investigate overall estimation error as well as per-parameter error and per-parameter uncertainty as a function of SNR and various noise models, including correlated noise. To demonstrate practical effectiveness and relevance, we apply our method to the well-studied problem of QRS complex delineation in electrocardiography signals. Out-of-the-box results show a performance comparable to the best algorithms known to date, without relying on problem-specific heuristic decision rules

Completing the Cabrera Circle: Deriving adaptable leads from ECG limb leads by combining constraints with a correction factor

Abstract Objective. We present a concept for processing 6-lead electrocardiography (ECG) signals which can be applied to various use cases in quantitative electrocardiography. Approach. Our work builds upon the mathematics of the well- known Cabrera sequence which is a re-sorting of the six limb leads (I, II, III, aVR, aVL, aVF) into a clockwise and physiologically-interpretable order. By deriving correction factors for harmonizing lead strengths and choosing an appropriate basis for the leads, we extend this concept towards what we call the “Cabrera Circle” based on a mathematically sound foundation. Main results. To demonstrate the practical effectiveness and relevance of this concept, we analyze its suitability for deriving interpolated leads between the six limb leads and a “radial” lead which both can be useful for specific use cases. We focus on the use cases of i) determination of the electrical heart axis by proposing a novel interactive tool for reconstructing the heart’s vector loop and ii) improving accuracy in time of automatic R-wave detection and T-wave delineation in 6-lead ECG. For the first use case, we derive an equation which allows projections of the 2-dimensional vector loops to arbitrary angles of the Cabrera Circle. For the second use case, we apply several state-of-the-art algorithms to a freely- available 12-lead dataset (Lobachevsky University Database). Out-of-the-box results show that the derived radial lead outperforms the other limb leads (I, II, III, aVR, aVL, aVF) by improving F1 scores of R-peak and T-peak detection by 0.61 and 2.12, respectively. Results of on- and offset computations are also improved but on a smaller scale. Significance. In summary, the Cabrera Circle offers a methodology that might be useful for quantitative electrocardiography of the 6-lead subsystem—especially in the digital age