Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation.

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset.

In today's neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images' effect on the adversarial network's generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets.

A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver's Vital Signs.

With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography.

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 Ω with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20-150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.

Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary 868MHz communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the 868MHz is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: 99.6±0.8%, Root-Mean-Square Deviation (RMSE) of mean stride time: 5ms, RMSE of percentage stance time: 2.3%). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life.

Sensor Fusion of Cardiorespiratory Signals Using an Adaptive Kalman Filter.

For unobtrusive monitoring of vital signs, redundant sensors are beneficial to fuse several sensor measurements which can improve the estimation of, e.g. heart rate and respiratory rate. In this paper, an adaptive unscented Kalman filter is used to estimate respiratory rate and heart rate on a new simplified model for cardiorespiratory coupling. Additionally, the Kalman filter is tuned to incorporate the non-white system noise of the model. The Kalman filter is tested on synthesised data with variations regarding SNR, model mismatch and amount of sensors. For respiratory rate, a median squared error of as low as 0.02BPM2 and, for heart rate, a median squared error of as low as 0.2BPM2 for ideal assumptions is achieved.

A Physical Phantom for the Simulation of Neonatal Thermoregulation.

Preterm infants are at an increased health risk due to their low maturity. To monitor their health, vital signs are measured using contact-based methods. The adhesive sensors used to detect body temperature can damage the sensitive skin of neonates. Thus, a subject of current research is non-invasive measurement methods based on infrared thermography. In this context, thermal phantoms can be used to develop contactless temperature measurement systems and, furthermore, investigate the thermal behavior of preterm infants. In this work, an improved thermal phantom is introduced to simulate the thermoregulation of a premature infant. The shape and size are adapted to the body of a premature infant in the 29th week of pregnancy. The phantom consists of a 3D-printed frame to which carbon fiber heating elements and Pt1000 temperature sensors are attached. The frame is enclosed by a thermally conductive skin layer made of a silicone boron nitride mixture. Ball joints allow the body parts to tilt and rotate, enabling the phantom to model different body postures. Using PI controllers, the thermal phantom can achieve desired temperatures in 13 different areas of the body while maintaining a homogeneous temperature distribution on the skin surface. In addition, pathological temperature scenarios such as a central-peripheral temperature difference or a change in body temperature can be simulated with a maximum deviation of ± 0.4 °C.

Using Synthesized IMU Data to Train a Long-Short Term Memory-based Neural Network for Unobtrusive Gait Analysis with a Sparse Sensor Setup.

In this work, we evaluated the possibility to use synthesized IMU data for training a deep neural network to generate a more complex, full-body description of the human gait in terms of joint angle trajectories from a sparse sensor setup. In this context, a sparse sensor setup consists of a few sensors attached to human body segments in an unobtrusive manner to possibly provide a monitoring system in an everyday life scenario. Since the relation between the input IMU data and the output joint angle trajectories is highly non-linear, neural networks appear to provide an optimal framework to formulate a mapping description. Especially with respect to periodic signals, recurrent neural networks (RNNs) have gained importance in the recent years. In this work, we have used a special type of RNNs that can be implemented by using long-short term memory (LSTM) cells, which have shown promising results when being applied to sequential data. The artificial training data was generated by a simulative human gait model and virtually attached sensor devices. The trained network was subsequently validated by a dataset that was recorded from a treadmill walking trial using a motion capturing system and an IMU sensor system. The qualitative comparison already shows promising results, however, this study can only be considered to provide preliminary results in this area. Clinical Relevance- This approach has the potential to be applied in the remote assessment of gait behavior during everyday life environments using an unobtrusive sensor net-work. In particular for monitoring older people suffering from an increased fall risk or any significant gait impairments this work is of possible interest.

Identification of Individually Altered Gait Behavior Using an Unobtrusive IMU Sensor Setup.

Gait behavior is considered an important indicator for the assessment of the general health status and provides a diagnostic observation for neuro-degenerative and musculo-skeletal diseases. Individual changes in gait behavior often reflect a deterioration of the current health status in a general sense and therefore provide significant information for clinicians and care-givers. In this work, we have used an unobtrusive sensor setup comprising three inertial measurement units (IMUs) located at the wrist, the chest and the thigh to obtain an objective measure of the human locomotion. We conducted a clinical trial in a movement laboratory environment to obtain a database of gait data at different walking speeds and conditions. The aging-simulation suit GERT was used to deteriorate the individual gait behavior during the experiments. Treadmill walking trials were used to train different classifiers to discriminate normal walking from GERT-affected walking patterns. Level-ground walking trials were used to validate the previously generated classifiers. A five-fold cross validation during the training process yielded overall F1-scores between 0.965 and 0.986. The validation tests showed promising results with prediction accuracies of more than 80%. Clinical relevance- The clinical relevance of this contri-bution can be considered two-fold. First we demonstrate the possibility of an unobtrusive monitoring system to iden-tify individual deterioration of gait behavior. Second we also validate the use of aging-simulation suits to introduce individual changes of gait patterns in healthy subjects to create a database of simulated yet realistic gait impairments associated with aging.

Step Length Estimation with Wearable Wrist Sensor using ANN.

Step Length is an important metric that can be used for the analysis and assessment of the gait. Proper dynamical models are not available in current literature associated with the wrist that can adequately determine the step length using recursive estimation techniques. This study presents a method to estimate the step length using angular velocity data from the wrist sensor. The technique maps the dynamical region corresponding to periods of activity of the gait manifested in angular velocity from the inertial measurement unit located at the wrist to that of the thigh using an artificial neural network, upon which an unscented Kalman filter is used to determine the horizontal position of the foot relative to the hip, and consequently, determine step length. The results for Step Length indicate an average accuracy of 81.8% and 91.1% for the young and elderly, respectively, when compared to a reference system, which, in our study, is data from a treadmill.

Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor.

The step length is an important parameter in gait analysis. Long-term monitoring applications for gait analysis are often based on inertial measurement units (IMUs) due to their low-cost and unobtrusive nature. Spatial gait parameters, such as step or stride length, are therefore not directly accessible. In this contribution, we focus on model-based algorithms for step length estimation based on a pendant-integrated IMU during slow walking speeds. We present a model-based approach to estimate the step length, which is divided into two successive steps. As the first part of our approach, we present an algorithm for estimation of the vertical displacement of the center of mass (CoM) during gait. Based on this estimate, we present a novel approach to estimate the step length, which we have deduced from a previously published, simplified gait model. The algorithm is compared to a commonly known approach for accelometry-based step length prediction and validated against reference data obtained from a force plate-integrated treadmill for gait analysis during a clinical study with ten healthy subjects. Due to the applicability to gait stability assessment in elderly or gait impaired patients, we focus on slow walking speeds (1-4 km h-1). The presented algorithms outperform the existing approach and the proposed model calculations provide a more accurate prediction. For the vertical displacement, we achieved a precision of 9.3% (CoV) with an RMSE of 1.5 mm in terms of the trajectory amplitude during normal gait patterns. The step length estimation yields satisfying results with a relative prediction error of lower than 10% for walking speeds of 2-4kmh-1.

Estimation of Stride Time Variability in Unobtrusive Long-Term Monitoring Using Inertial Measurement Sensors.

Stride time variability is an important indicator for the assessment of gait stability. An accurate extraction of the stride intervals is essential for determining stride time variability. Peak detection is a commonly used method for gait segmentation and stride time estimation. Standard peak detection algorithms often fail due to additional movement components and measurement noise. A novel algorithm for robust peak detection in inertial sensor signals was proposed in a previous contribution. In this work, we present a novel approach for estimation of stride time variability based on the formerly proposed peak detection algorithm applied to an unobtrusive sensor setup for motion monitoring. The unobtrusive sensor setup includes a wrist sensor, a pocket or belt sensor, and a necklace sensor, all equipped with both accelerometer and gyroscope. The goal of this work is to implement a generalized approach for accurate and robust stride interval determining algorithm for different sensor locations. Therefore, treadmill and level ground walking experiments were conducted with ten healthy subjects at increasing walking speeds and an age-simulating suit. With the proposed algorithm, we achieved a RMSE of 0.07 s for the stride interval estimation during treadmill walking experiments. The results give promising indications that detection of variation of stride time variability is possible using the proposed unobtrusive sensor setup.