Electrophysiological patterns and structural substrates of Brugada syndrome: Critical appraisal and computational analyses.

Brugada syndrome (BrS) is a cardiac electrophysiological disease with unknown etiology, associated with sudden cardiac death. Symptomatic patients are treated with implanted cardiac defibrillator, but no risk stratification strategy is effective in patients that are at low to medium arrhythmic risk. Cardiac computational modeling is an emerging tool that can be used to verify the hypotheses of pathogenesis and inspire new risk stratification strategies. However, to obtain reliable results computational models must be validated with consistent experimental data. We reviewed the main electrophysiological and structural variables from BrS clinical studies to assess which data could be used to validate a computational approach. Activation delay in the epicardial right ventricular outflow tract is a consistent finding, as well as increased fibrosis and subclinical alterations of right ventricular functional and morphological parameters. The comparison between other electrophysiological variables is hindered by methodological differences between studies, which we commented. We conclude by presenting a recent theory unifying electrophysiological and structural substrate in BrS and illustrate how computational modeling could help translation to risk stratification.

Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings.

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement.

Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.

BACKGROUND: The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. OBJECTIVE: This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. METHODS: Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. RESULTS: We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. CONCLUSIONS: Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).

Exploiting Resistive Matrix Technology to Build a Stretchable Sensorised Sock for Gait Analysis in Daily Life.

We describe the development and preliminary evaluation of an innovative low-cost wearable device for gait analysis. We have developed a sensorized sock equipped with 32 piezoresistive textile-based sensors integrated in the heel and metatarsal areas for the detection of signals associated with the contact pressures generated during walking phases. To build the sock, we applied a sensing patch on a commercially available sock. The sensing patch is a stretchable circuit based on the resistive matrix method, in which conductive stripes, based on conductive inks, are coupled with piezoresistive fabrics to form sensing elements. In our sensorized sock, we introduced many relevant improvements to overcome the limitations of the classical resistive matrix method. We preliminary evaluated the sensorized sock on five healthy subjects by performing a total of 80 walking tasks at different speeds for a known distance. Comparison of step count and step-to-step frequency versus reference measurements showed a high correlation between the estimated measure and the real one.

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers.

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible.

Ambiguity-Free Optical-Inertial Tracking for Augmented Reality Headsets.

The increasing capability of computing power and mobile graphics has made possible the release of self-contained augmented reality (AR) headsets featuring efficient head-anchored tracking solutions. Ego motion estimation based on well-established infrared tracking of markers ensures sufficient accuracy and robustness. Unfortunately, wearable visible-light stereo cameras with short baseline and operating under uncontrolled lighting conditions suffer from tracking failures and ambiguities in pose estimation. To improve the accuracy of optical self-tracking and its resiliency to marker occlusions, degraded camera calibrations, and inconsistent lighting, in this work we propose a sensor fusion approach based on Kalman filtering that integrates optical tracking data with inertial tracking data when computing motion correlation. In order to measure improvements in AR overlay accuracy, experiments are performed with a custom-made AR headset designed for supporting complex manual tasks performed under direct vision. Experimental results show that the proposed solution improves the head-mounted display (HMD) tracking accuracy by one third and improves the robustness by also capturing the orientation of the target scene when some of the markers are occluded and when the optical tracking yields unstable and/or ambiguous results due to the limitations of using head-anchored stereo tracking cameras under uncontrollable lighting conditions.

ePhysio: A Wearables-Enabled Platform for the Remote Management of Musculoskeletal Diseases.

Technology advancements in wireless communication and embedded computing are fostering their evolution from standalone elements to smart objects seamlessly integrated in the broader context of the Internet of Things. In this context, wearable sensors represent the building block for new cyber-physical social systems, which aim at improving the well-being of people by monitoring and measuring their activities and provide an immediate feedback to the users. In this paper, we introduce ePhysio, a large-scale and flexible platform for sensor-assisted physiotherapy and remote management of musculoskeletal diseases. The system leverages networking and computing tools to provide real-time and ubiquitous monitoring of patients. We propose three use cases which differ in scale and context and are characterized by different human interactions: single-user therapy, indoor group therapy, and on-field therapy. For each use case, we identify the social interactions, e.g., between the patient and the physician and between different users and the performance requirements in terms of monitoring frequency, communication, and computation. We then propose three related deployments, highlighting the technologies that can be applied in a real system. Finally, we describe a proof-of-concept implementation, which demonstrates the feasibility of the proposed solution.

Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases.

Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms.

A Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors.

Electrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 4.7% and 18%, respectively.

A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors.

BACKGROUND: Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors. METHOD: To feed the reconstruction algorithm based on the bi-articular model, an ad-hoc sensing shirt was developed. The shirt was equipped with two inertial measurement units (IMUs) and an integrated textile strain sensor. We built the bi-articular model starting from the data obtained in two planar movements (arm abduction and flexion in the sagittal plane) and analysing the error between the reference data - measured through an optical reference system - and the socket-ball approximation of the shoulder. The 3D model was developed by extending the behaviour of the kinematic chain revealed in the planar trajectories through a parameter identification that takes into account the body structure of the subject. RESULT: The bi-articular model was evaluated in five subjects in comparison with the optical reference system. The errors were computed in terms of distance between the reference position of the trochlea (end-effector) and the correspondent model estimation. The introduced method remarkably improved the estimation of the position of the trochlea (and consequently the estimation of the hand position during reaching activities) reducing position errors from 11.5 cm to 1.8 cm. CONCLUSION: Thanks to the developed bi-articular model, we demonstrated a reliable estimation of the upper arm kinematics with a minimal sensing system suitable for daily life monitoring of recovery.

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition.

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.

Music, clicks, and their imaginations favor differently the event-based timing component for rhythmic movements.

The involvement or noninvolvement of a clock-like neural process, an effector-independent representation of the time intervals to produce, is described as the essential difference between event-based and emergent timing. In a previous work (Bravi et al. in Exp Brain Res 232:1663-1675, 2014a. doi: 10.1007/s00221-014-3845-9 ), we studied repetitive isochronous wrist's flexion-extensions (IWFEs), performed while minimizing visual and tactile information, to clarify whether non-temporal and temporal characteristics of paced auditory stimuli affect the precision and accuracy of the rhythmic motor performance. Here, with the inclusion of new recordings, we expand the examination of the dataset described in our previous study to investigate whether simple and complex paced auditory stimuli (clicks and music) and their imaginations influence in a different way the timing mechanisms for repetitive IWFEs. Sets of IWFEs were analyzed by the windowed (lag one) autocorrelation-wγ(1), a statistical method recently introduced for the distinction between event-based and emergent timing. Our findings provide evidence that paced auditory information and its imagination favor the engagement of a clock-like neural process, and specifically that music, unlike clicks, lacks the power to elicit event-based timing, not counteracting the natural shift of wγ(1) toward positive values as frequency of movements increase.

Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life.

Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints.

Modulation of isochronous movements in a flexible environment: links between motion and auditory experience.

The ability to perform isochronous movements while listening to a rhythmic auditory stimulus requires a flexible process that integrates timing information with movement. Here, we explored how non-temporal and temporal characteristics of an auditory stimulus (presence, interval occupancy, and tempo) affect motor performance. These characteristics were chosen on the basis of their ability to modulate the precision and accuracy of synchronized movements. Subjects have participated in sessions in which they performed sets of repeated isochronous wrist's flexion-extensions under various conditions. The conditions were chosen on the basis of the defined characteristics. Kinematic parameters were evaluated during each session, and temporal parameters were analyzed. In order to study the effects of the auditory stimulus, we have minimized all other sensory information that could interfere with its perception or affect the performance of repeated isochronous movements. The present study shows that the distinct characteristics of an auditory stimulus significantly influence isochronous movements by altering their duration. Results provide evidence for an adaptable control of timing in the audio-motor coupling for isochronous movements. This flexibility would make plausible the use of different encoding strategies to adapt audio-motor coupling for specific tasks.

New generation of wearable goniometers for motion capture systems.

BACKGROUND: Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient's home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented. METHODS: KPF single-and double-layer devices were designed and characterized under stretching and bending to work as strain sensors and goniometers. The theoretical working principle and the derived electromechanical model, previously proved for carbon elastomer sensors, were generalized to KPF. The devices were used to correlate angles and piezoresistive fabric behaviour, to highlight the differences in terms of performance between the single layer and the double layer sensors. A fast calibration procedure is also proposed. RESULTS: The proposed device was tested both in static and dynamic conditions in comparison with standard electrogoniometers and inertial measurement units respectively. KPF goniometer capabilities in angle detection were experimentally proved and a discussion of the device measurement errors of is provided. The paper concludes with an analysis of sensor accuracy and hysteresis reduction in particular configurations. CONCLUSIONS: Double layer KPF goniometers showed a promising performance in terms of angle measurements both in quasi-static and dynamic working mode for velocities typical of human movement. A further approach consisting of a combination of multiple sensors to increase accuracy via sensor fusion technique has been presented.

A smoothed boundary bidomain model for cardiac simulations in anatomically detailed geometries.

This manuscript presents a novel finite difference method to solve cardiac bidomain equations in anatomical models of the heart. The proposed method employs a smoothed boundary approach that represents the boundaries between the heart and the surrounding medium as a spatially diffuse interface of finite thickness. The bidomain boundary conditions are implicitly implemented in the smoothed boundary bidomain equations presented in the manuscript without the need of a structured mesh that explicitly tracks the heart-torso boundaries. We reported some significant examples assessing the method's accuracy using nontrivial test geometries and demonstrating the applicability of the method to complex anatomically detailed human cardiac geometries. In particular, we showed that our approach could be employed to simulate cardiac defibrillation in a human left ventricle comprising fiber architecture. The main advantage of the proposed method is the possibility of implementing bidomain boundary conditions directly on voxel structures, which makes it attractive for three dimensional, patient specific simulations based on medical images. Moreover, given the ease of implementation, we believe that the proposed method could provide an interesting and feasible alternative to finite element methods, and could find application in future cardiac research guiding electrotherapy with computational models.

Diffuse fibrosis and repolarization disorders explain ventricular arrhythmias in Brugada syndrome: a computational study.

In this work, we reported a computational study to quantitatively determine the individual contributions of three candidate arrhythmic factors associated with Brugada Syndrome. In particular, we focused our analysis on the role of structural abnormalities, dispersion of repolarization, and size of the diseased region. We developed a human phenomenological model capable of replicating the action potential characteristics both in Brugada Syndrome and in healthy conditions. Inspired by physiological observations, we employed the phenomenological model in a 2D geometry resembling the pathological RVOT coupled with healthy epicardial tissue. We assessed the insurgence of sustained reentry as a function of electrophysiological and structural abnormalities. Our computational study indicates that both structural and repolarization abnormalities are essential to induce sustained reentry. Furthermore, our results suggest that neither dispersion of repolarization nor structural abnormalities are sufficient on their own to induce sustained reentry. It should be noted how our study seems to explain an arrhythmic mechanism that unifies the classic repolarization and depolarization hypotheses of the pathophysiology of the Brugada Syndrome. Finally, we believe that this work may offer a new perspective on the computational and clinical investigation of Brugada Syndrome and its arrhythmic behaviour.

A transmurally heterogeneous model of the ventricular tissue and its application for simulation of Brugada Syndrome.

We present a transmurally heterogeneous phe-nomenological model of ventricular tissue that is designed to reproduce the most important features of action potential prop-agation of endocardial, midmyocardial, and epicardial tissue. Our model consists of only 3 variables and 20 parameters. Therefore, it is highly computational efficient and easy to fit to experimental data. We exploited our myocyte model to simulate action potential propagation in a 3D slab of cardiac tissue both in healthy conditions and in presence of Brugada syndrome. The results show that our model can accurately reproduce the transmural heterogeneity of the ventricular wall and the main characteristics of electrocardiographic pattern both in healthy and pathological conditions.

Low cardiac frequency associated with higher number of extrasistoles in a computational model of Brugada Syndrome.

Brugada Syndrome is a form of idiopathic ventricular fibrillation, to date there is no definitive theory about how ventricular fibrillation is initiated or its substrate. Starting from the clinical observation that cardiac episodes are more frequent at rest, we developed a model in order to study the effect of cardiac frequency on reentrant activity. Our results suggest that the combination of arrhythmic substrate and cardiac frequency has a role in the insurgence of cardiac arrhythmia.

An Innovative Sensorized Face Mask for Early Detection of Physiological Changes Associated with Viral Infection.

A sensorized face mask could be a useful tool in the case of a viral pandemic event, as well as the Covid-19 emergency. In the context of the proposed project "RESPIRE", we have developed a "Smart-Mask" able to collect the signal patterns of body temperature, respiration, and symptoms such as cough, through a set of textile sensors. The signals have been analyzed by Artificial Intelligence algorithms in order to compare them with gold standard measurements, and to recognize the physiological changes associated with a viral infection. This low-cost prototype of a smart face mask is a reliable tool for the estimation of the individual physiological parameters. Moreover, it enables both personal protection and the early and rapid identification and tracking of potentially infected individuals.