Search

Vital signs are a group of biological indicators that show the status of the body’s life-sustaining functions. They provide an objective measurement of the essential physiological functions of a living organism, and their assessment is the critical first step for any clinical evaluation. Monitoring vital sign information provides valuable insight into the patient's condition, including how they are responding to medical treatment and, more importantly, whether the patient is deteriorating. However, conventional contact-based devices are inappropriate for long-term continuous monitoring. Besides mobility restrictions and stress, they can cause discomfort, and epidermal damage, and even lead to pressure necrosis. On the other hand, the contactless monitoring of vital signs using radar devices has several advantages. Radar signals can penetrate through different materials and are not affected by skin pigmentation or external light conditions. Additionally, these devices preserve privacy, can be low-cost, and transmit no more power than a mobile phone. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios. The challenge stems from the fact that when we breathe, or when the heart beats, the tiny induced motion of the chest wall surface can be smaller than one millimeter. This means that the vital sign information can be easily lost in the background noise, or even masked by additional body movements from the monitored subject. This thesis aims to propose innovative signal processing solutions to enable the contactless monitoring of vital signs in practical scenarios. Its main contributions are threefold: a new algorithm for recovering the chest wall movements from radar signals; a novel random body movement and interference mitigation technique; and a simple, yet robust and accurate, adaptive estimation framework. These contributions were tested under different operational conditions and scenarios, spanning ideal

We study scintillation and bit error rate performance of mirrored Bessel beams through turbulent atmosphere in this article. We beneft from numerical method to model the atmosphere. Since scintillation plays a vital role in optical wireless applications, reduction in this term provides better performance in these systems. Related with this, our results indicate that it is possible to decrease scintillation by increasing beam order to three when strong turbulent conditions are satisfed. In addition, we observe that argument of Bessel beam has more dominant role than beam order in moderate turbulence. Lastly, mirroring brings us a slight advantage in case of bit error rate. Results of this study can be benefcial for optical link designers

This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals of the LEO satellite and the passive beamforming of RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47% and 54.9% higher energy efficiency compared to the benchmark and conventional frameworks.

Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view.

This article presents a closed-loop differential phase compensation system for a precoding-enabled multibeam satellite forward link and its validation by live experiments on a GEO satellite scenario. The precoding operation avoids inter-beam interference and maximizes the spectrum efficiency by full frequency reuse as an alternative to the traditional two-color or four-color reuse methods proposed in the DVB-S2 standard. However, the satellite payload introduces differential phase and frequency impairments, which can degrade the precoding performance. This work describes the implementation of the differential phase and frequency tracking and compensation loop in an end-to-end testbed over a multibeam satellite system with independent local oscillators. The developed system performs end-to-end real-time communication over the satellite link, including channel measurements and precompensation. Results are validated by an over-the-air demonstration using two beams of the SES-14 multibeam satellite. Each beam is transmitted by independent transponders, which results in differential frequency and phase offsets due to the transponder undisciplined local oscillators. This phase offset makes it impossible to use precoding without the phase compensation loop. We prove that the implemented system can successfully track and compensate the differential phase and frequency to improve precoding performance.

Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.

Linear and symbol-level precoding in satellite communications have received increasing research attention thanks to their ability to tackle inter-beam interference, allowing the use of spectral resources more efficiently. However, there are still challenges and open questions regarding the implementation of practical precoding systems taking the phase uncertainties in estimating the channel state information into account. This work assesses the impact of phase variations and uncertainties inherent to the satellite communication system operating a precoded forward link. Specifically, we address the inability to measure at the user terminal, the absolute phase rotation introduced by the channel, and the transponder local oscillator phase noise effects on the precoding operations considering the use of frequency division multiplexing in the forward-uplink transmission. We formally demonstrate that the system performance for linear and non-linear precoding operations is not affected by the uncertainty in the phase measurements at the user terminal. Additionally, we show that using a single frequency reference for all the local oscillators at the transponder does not avoid the phase variations related to the frequency division multiplexing in the forward-uplink. This work demonstrates that these phase variations would not affect the system performance for an ideal zero-delay precoding loop. However, this is not feasible in practical scenarios, where the phase noise of the frequency reference at the transponder and the loop delay determine the impact on the system performance. We validate our results by simulations considering three frequency references with different stability levels in a typical GEO satellite system. Our results suggest that practical implementations of multiuser-MISO precoding systems must include a differential phase synchronization loop to compensate for this performance degradation.

Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMAbased systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcementlearning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios.

Technical advancements and experimental works for the integration of 5G and Non-Terrestrial Networks (NTN) have gained significant traction over the past few years. NTN components have been officially included in the 5G ecosystem by 3GPP in the latest Release-17. 5G-NTN research is ongoing and it is desirable to have a platform that facilitates quick prototyping of the proof-of-concept methods. OpenAirInterface(OAI) is an open-source experimental yet 3GPP standard-compliant Software Defined Radio (SDR) based protocol stack that has been widely known for implementing 4G/5G technologies. Due to its proven capabilities and flexibility, OAI is currently in the developmental process of integrating adaptations for the 5G-NTN. In this work, we discuss the peculiar features of OAI which are shaping it towards becoming a preferred tool for research and experimentation related to 5G-NTN. We provide details of completed/ongoing 5G-NTN projects leveraging OAI to achieve their objectives. In particular, we discuss 5G-GOA and 5G-LEO where critical adaptations in OAI are being done to support 5G-NTN usecases. Such adaptations enable direct-access between UE and gNB via transparent payload Geostationary (5G-GOA) and Nongeostationary satellites (5G-LEO). Both projects have closely followed 3GPP discussions over 5G-NTN and the adaptations are compliant with the currently frozen 3GPP Release-17. OAI adaptations from both projects will be merged into the main development branch of OAI. We also provide a future roadmap of OAI towards 5G-NTN development. We believe that the pioneering steps taken in the course of the aforementioned projects will establish OAI as a preferred tool for 5G-NTN research and experimentations.

Widely distributed radar systems are expected to enhance radar imaging performance due to their ability to capture diverse spatial scattering proprieties. Optimization-based sub-aperture imaging methods are used to adopt the isotropic scattering assumption within a narrow angular extent and reconstruct the scene image by fusing sub-aperture images. A previously proposed method based on consensus alternating direction method of multipliers (CADMM) provides a joint reconstruction of sub-aperture images along with a global image that represents the anisotropic scene. In this paper, we propose a modified version of CADMM which features lower complexity and faster convergence. By gradually learning the scene support during the iterative reconstruction, our proposed algorithm focuses on the image portion that contains the scattering targets and updates the sub-images accordingly. It also reduces the communication cost between the distributed sensors which need to exchange local image updates during CADMM iterations.

The integration of 5G with Non-Terrestrial Network (NTN) components is going through a series of technological advancements and soon satellites will be a part of the 5G ecosystem. Early demonstrators, especially based on open-source implementations, are essential to support further research. In this work, we discuss the ongoing activities and developments related to the project 5G-Enabled Ground Segment Technologies OverThe-Air Demonstrator (5G-GoA) which has been funded under the ESA-ARTES program. The vision of 5G-GoA is developing and implementing suitable modifications in the 5G New Radio (NR) standard for enabling direct radio access to 5G services using a transparent GEO satellite. For this purpose, we have used OpenAirInterface(OAI) which is a Software Defined Radio (SDR) based open-source implementation of the 5G-NR protocol stack. We adapted it to address the challenges caused by the excessive round-trip delay in GEO satellites. Our solutions encompass all the layers of the 5G protocol stack: The physical layer (e.g. synchronization) up to upper layer implementations (e.g. timers and random-access procedure) of the Radio Access Network. Our modifications comply with the specifications mentioned for 5GNTN in the recently frozen 3GPP Release-17. An end-to-end demonstrator has been developed for in-lab validation over a satellite channel emulator prior to over-the-satellite testing. Our initial experiments show promising results and the feasibility of direct access to 5G services through transparent GEO satellites.

Because of the near-field nature of radio propagation, spherical wave-front and multipath effect are prominent in indoor scenarios, making localization even more difficult. In this paper, we propose a three-dimensional (3D) indoor localization algorithm that takes these issues into account. Specifically, we first adopted a high-resolution channel parameter estimation method for path delays based on the Space-Alternating Generalized Expectation-maximization (SAGE), and then these path delays are adopted in the 3D localization principles based on the target-antenna geometry. The proposed algorithm is validated by numerical simulations, where the channel data is generated by the propagation graph (PG) to model the true wireless propagation closely in the testing scenarios. The results demonstrate that the proposed approach can deal with both point and non-point targets with 3D localization errors of less than 30 cm for 97% of the testing trails in a 10×20×3 m3 indoor space.

5G services combined with the satellites, also termed 5G NonTerrestrial Networks (5G-NTN), have the capability of providing connectivity to the areas which were previously either unreachable or too costly to be reached by terrestrial communication networks. Proof-of-Concept (POC) demonstrators, preferably based on open-source implementation are desirable to expedite the ongoing research on 5G-NTN. In this work, we discuss the contributions made during the project 5G-GOA: 5G-Enabled Ground Segment Technologies Over-The-Air Demonstrator which aims to provide direct access to 5G services to a UE through a transparent payload Geostationary (GEO) satellite. 5G-GOA uses the open-source Software-Defined-Radio (SDR) platform OpenAirInterface (OAI) and does the necessary adaptations to achieve its objectives. Adaptations span physical layer techniques (e.g. synchronization) up to upper layer implementations (e.g., timers and random-access procedures) of the Radio Access Network (RAN). The adaptations are based on 3GPP 5G-NTN discussions and the solutions are compliant with the recently frozen 3GPP Release-17. An endto-end SDR-based 5G-NTN demonstrator has been developed for Over-The-Satellite (OTS) testing. We present results from several experiments that were conducted for in-lab validation of the demonstrator using a satellite channel emulator before going live with OTS tests. Experimental results indicate the readiness of the demonstrator for OTS testing which is scheduled during ICSSC 2022. The source code has been submitted to OAI public repository and is available for testing.

Future wireless networks pose several challenges such as high spectral efficiency, wide coverage massive connectivity, low receiver complexity, etc. To this end, this letter investigates an overlay based cognitive hybrid satellite-terrestrial network (CHSTN) combining non-orthogonal multiple access (NOMA) and conventional Alamouti space-time block coding (STBC) techniques. Herein, a decode-and-forward based secondary terrestrial network cooperates with a primary satellite network for dynamic spectrum access. Further, for reliable content delivery and low latency requirements, wireless caching is employed, whereby the secondary network can store the most popular contents of the primary network. Considering the relevant heterogeneous fading channel models and the NOMA-based imperfect successive interference cancellation, we examine the performance of CHSTN for the cache-free (CF) STBC-NOMA and the cache-aided (CA) STBC-NOMA schemes. We assess the outage probability expressions for primary and secondary networks and further, highlight the corresponding achievable diversity orders. Indicatively, the proposed CF/CA STBC-NOMA schemes for CHSTN perform significantly better than the benchmark standalone NOMA and OMA schemes.

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.

Background and Objective Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. Methods Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients’ status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes. Results Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care., Peer Reviewed, Postprint (published version)

Objective. Impaired trunk stability is frequent in spinal cord injury (SCI), but there is a lack of quantitative measures for assessing trunk function. Our objectives were to: (a) evaluate trunk muscle activity and movement patterns during a reaching task in SCI patients, (b) compare the impact of cervical (cSCI) and thoracic (tSCI) injuries in trunk function, and (c) investigate the effects of a startling acoustic stimulus (SAS) in these patients. Approach. Electromyographic (EMG) and smartphone accelerometer data were recorded from 15 cSCI patients, nine tSCI patients, and 24 healthy controls, during a reaching task requiring trunk tilting. We calculated the response time (RespT) until pressing a target button, EMG onset latencies and amplitudes, and trunk tilt, lateral deviation, and other movement features from accelerometry. Statistical analysis was applied to analyze the effects of group (cSCI, tSCI, control) and condition (SAS, non-SAS) in each outcome measure. Main results. SCI patients, especially those with cSCI, presented significantly longer RespT and EMG onset latencies than controls. Moreover, in SCI patients, forward trunk tilt was accompanied by significant lateral deviation. RespT and EMG latencies were remarkably shortened by the SAS (the so-called StartReact effect) in tSCI patients and controls, but not in cSCI patients, who also showed higher variability. Significance. The combination of EMG and smartphone accelerometer data can provide quantitative measures for the assessment of trunk function in SCI. Our results show deficits in postural control and compensatory strategies employed by SCI patients, including delayed responses and higher lateral deviations, possibly to improve sitting balance. This is the first study investigating the StartReact responses in trunk muscles in SCI patients and shows that the SAS significantly accelerates RespT in tSCI, but not in cSCI, suggesting an increased cortical control exerted by these patients., Peer Reviewed, Postprint (published version)

We present an extension of the widely used Hierarchical Alternating Least Squares (HALS) algorithm to solve Nonnegative Matrix Factorization (NMF) problems using rational functions, in order to unmix discretization of continuous signals. We observe that the use of rational functions in NMF can significantly improve the quality of the reconstruction of noisy data compared to the standard approach based on vectors, and to recent continuous signal factorization approaches using splines or polynomials. We also show that our algorithm obtains state-of-the-art results in the domain of multicomponent nanostructures spectrum image unmixing.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance— This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters., Peer Reviewed, Postprint (author's final draft)

Intermittent hypoxia (IH) produces autonomic dysfunction that promotes the development of arrhythmia and hypertension in patients with obstructive sleep apnea (OSA). This paper investigated different heart rate variability (HRV) indices in the context of IH using a rat model for OSA. Linear and non-linear HRV parameters were assessed from ultra-short (15-s segments) and short-term (5 min) analyses of heartbeat time-series. Transient changes observed from pre-apnea segments to hypoxia episodes were evaluated, besides the relative and global impact of IH, as a function of its severity. Results showed an overall increase in ultra-short HRV markers as immediate response to hypoxia: standard deviation of normal RR intervals, SDNN = 1.2 ms (IQR: 1.1-2.1) vs 1.4 ms (IQR: 1.2-2.2), p = 0.015; root mean square of the successive differences, RMSSD = 1.7 ms (IQR: 1.5-2.2) vs 1.9 ms (IQR: 1.6-2.4), p = 0.031. The power in the very low frequency (VLF) band also showed a significant increase: 0.09 ms 2 (IQR: 0.05-0.20) vs 0.16 ms 2 (IQR: 0.12-0.23), p = 0.016, probably associated with the potentiation of the carotid body chemo-sensory response to hypoxia. Moreover, a clear link between severity of IH and short-term HRV measures was found in VLF and LF power, besides their progressive increase seen throughout the experiment after each apnea sequence. However, only those markers quantifying fragmentation levels in RR series were significantly affected when the experiment ended, as compared to baseline measures: percentage of inflection points, PIP = 49% (IQR: 45-51) vs 53% (IQR: 47-53), p = 0.031; percentage of short (geq 3 RR intervals) accelerated/decelerated segments, PSS = 75% (IQR: 51-81) vs 87% (IQR: 51-90), p = 0.046. These findings suggest a significant deterioration of cardiac rhythm with a more erratic behavior beyond the normal sinus arrhythmia, that may lead to a future cardiac condition., Peer Reviewed, Postprint (published version)

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI = 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients., Peer Reviewed, Postprint (published version)

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, left and right. An increase in sleep position resolution is necessary to better assess sleep position dynamics and to interpret more accurately intermediate sleep positions. This research aims to study the feasibility of smartphones as sleep position monitors by (1) developing algorithms to retrieve the sleep position angle from smartphone accelerometry; (2) monitoring the sleep position angle in patients with obstructive sleep apnea (OSA); (3) comparing the discretized sleep angle versus the four classic sleep positions obtained by the video-validated polysomnography (PSG); and (4) analyzing the presence of positional OSA (pOSA) related to its sleep angle of occurrence. Results from 19 OSA patients reveal that a higher resolution sleep position would help to better diagnose and treat patients with position-dependent diseases such as pOSA. They also show that smartphones are promising mHealth tools for enhanced position monitoring at hospitals and home, as they can provide sleep position with higher resolution than the gold-standard video-validated PSG., Peer Reviewed, Postprint (published version)

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application (‘SleepPos’ app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the ‘SleepPos’ app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The ‘SleepPos’ app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events., Peer Reviewed, Postprint (published version)

Patients suffering from obstructive sleep apnea (OSA) usually present an increased sympathetic activity caused by the intermittent hypoxia effect on autonomic control. This study evaluated the relationship between sleep stages and the apnea duration, frequency, and type, as well as their impact on HRV markers in different groups of disease severity. The hypnogram and R-R interval signals were extracted in 81 OSA patients from night polysomnographic (PSG) recordings. The apnea-hypopnea index (AHI) defined patient classification as mild-moderate (AHI<=30, n=44) or severe (AHI>30, n=37). The normalized power in VLH, LF, and HF bands of RR series were estimated by a time-frequency approach and averaged in 1-min epochs of normal and apnea segments. The autonomic response and the impact of sleep stages were assessed in both segments to compare patient groups. Deeper sleep stages (particularly S2) concentrated the shorter and mild apnea episodes (from 10 to 40 s) compared to light (SWS) and REM sleep. Longer episodes (>50 s) although less frequent, were of similar incidence in all stages. This pattern was more pronounced for the group of severe patients. Moreover, during apnea segments, LF nu was higher (p=0.044) for the severe group, since V LF nu and HF nu presented the greatest changes when compared to normal segments. The non-REM sleep seems to better differentiate OSA patients groups, particularly through VLF nu and HF nu (p<0.001). A significant difference in both sympathetic and vagal modulation between REM and non-REM sleep was only found within the severe group. These results confirm the importance of considering sleep stages for HRV analysis to further assess OSA disease severity, beyond the traditional and clinically limited AHI values.Clinical relevance—Accounting for sleep stages during HRV analysis could better assess disease severity in OSA patients., Peer Reviewed, Postprint (published version)

This study aims to investigate noninvasive indices of neuromechanical coupling (NMC) and mechanical efficiency (MEff) of parasternal intercostal muscles. Gold standard assessment of diaphragm NMC requires using invasive techniques, limiting the utility of this procedure. Noninvasive NMC indices of parasternal intercostal muscles can be calculated using surface mechanomyography (sMMGpara) and electromyography (sEMGpara). However, the use of sMMGpara as an inspiratory muscle mechanical output measure, and the relationships between sMMGpara, sEMGpara, and simultaneous invasive and noninvasive pressure measurements have not previously been evaluated. sEMGpara, sMMGpara, and both invasive and noninvasive measurements of pressures were recorded in twelve healthy subjects during an inspiratory loading protocol. The ratios of sMMGpara to sEMGpara, which provided muscle-specific noninvasive NMC indices of parasternal intercostal muscles, showed nonsignificant changes with increasing load, since the relationships between sMMGpara and sEMGpara were linear (R2 = 0.85 (0.75–0.9)). The ratios of mouth pressure (Pmo) to sEMGpara and sMMGpara were also proposed as noninvasive indices of parasternal intercostal muscle NMC and MEff, respectively. These indices, similar to the analogous indices calculated using invasive transdiaphragmatic and esophageal pressures, showed nonsignificant changes during threshold loading, since the relationships between Pmo and both sEMGpara (R2 = 0.84 (0.77–0.93)) and sMMGpara (R2 = 0.89 (0.85–0.91)) were linear. The proposed noninvasive NMC and MEff indices of parasternal intercostal muscles may be of potential clinical value, particularly for the regular assessment of patients with disordered respiratory mechanics using noninvasive wearable and wireless devices., Peer Reviewed, Postprint (published version)

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Chronic obstructive pulmonary disease (COPD) is a disorder which induces a persistent inflammation of the lungs. This condition produces hypoventilation, affecting the blood oxygenation, and leads to an increased risk of developing lung cancer and heart disease. In this study, we evaluated how COPD affects the severity and characteristics of OSA in a multivariate demographic database including polysomnographic signals. Results showed SpO2 subtle variations, such as more non-recovered desaturations and increased time below a 90% SpO2 level, which, in the long term, could worsen the risk to suffer cardiovascular and cerebrovascular diseases., Peer Reviewed, Postprint (author's final draft)

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Fixed sample entropy (fSampEn) is a promising technique for the analysis of respiratory electromyographic (EMG) signals. Its use has shown outperformance of amplitude-based estimators such as the root mean square (RMS) in the evaluation of respiratory EMG signals with cardiac noise and a high correlation with respiratory signals, allowing changes in respiratory muscle activity to be tracked. However, the relationship between the fSampEn response to a given muscle activation has not been investigated. The aim of this study was to analyze the nature of the fSampEn measurements that are produced as the EMG activity increases linearly. Simulated EMG signals were generated and increased linearly. The effect of the parameters r and the size of the moving window N of the fSampEn were evaluated and compared with those obtained using the RMS. The RMS showed a linear trend throughout the study. A non-linear, sigmoidal-like behavior was found when analyzing the EMG signals using the fSampEn. The lower the values of r, the higher the non-linearity observed in the fSampEn results. Greater moving windows reduced the variation produced by too small values of r., Peer Reviewed, Postprint (author's final draft)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic conditions. The current assessment of COPD requires a maximal maneuver during a spirometry test to quantify airflow limitations of patients. Other less invasive measurements such as thoracic bioimpedance and myographic signals have been studied as an alternative to classical methods as they provide information about respiration. Particularly, strong correlations have been shown between thoracic bioimpedance and respiratory volume. The main objective of this study is to investigate bioimpedance and its combination with myographic parameters in COPD patients to assess the applicability in respiratory disease monitoring. We measured bioimpedance, surface electromyography and surface mechanomyography in forty-three COPD patients during an incremental inspiratory threshold loading protocol. We introduced two novel features that can be used to assess COPD condition derived from the variation of bioimpedance and the electrical and mechanical activity during each respiratory cycle. These features demonstrate significant differences between mild and severe patients, indicating a lower inspiratory contribution of the inspiratory muscles to global respiratory ventilation in the severest COPD patients. In conclusion, the combination of bioimpedance and myographic signals provides useful indices to noninvasively assess the breathing of COPD patients., Peer Reviewed, Postprint (updated version)

Surface electromyography (sEMG) can be used for the evaluation of respiratory muscle activity. Recording sEMG involves the use of surface electrodes in a bipolar configuration. However, electrocardiographic (ECG) interference and electrode orientation represent considerable drawbacks to bipolar acquisition. As an alternative, concentric ring electrodes (CREs) can be used for sEMG acquisition and offer great potential for the evaluation of respiratory muscle activity due to their enhanced spatial resolution and simple placement protocol, which does not depend on muscle fiber orientation. The aim of this work was to analyze the performance of CREs during respiratory sEMG acquisitions. Respiratory muscle sEMG was applied to the diaphragm and sternocleidomastoid muscles using a bipolar and a CRE configuration. Thirty-two subjects underwent four inspiratory load spontaneous breathing tests which was repeated after interchanging the electrode positions. We calculated parameters such as (1) spectral power and (2) median frequency during inspiration, and power ratios of inspiratory sEMG without ECG in relation to (3) basal sEMG without ECG (Rins/noise), (4) basal sEMG with ECG (Rins/cardio) and (5) expiratory sEMG without ECG (Rins/exp). Spectral power, Rins/noise and Rins/cardio increased with the inspiratory load. Significantly higher values (p <; 0.05) of Rins/cardio and significantly higher median frequencies were obtained for CREs. Rins/noise and Rins/exp were higher for the bipolar configuration only in diaphragm sEMG recordings, whereas no significant differences were found in the sternocleidomastoid recordings. Our results suggest that the evaluation of respiratory muscle activity by means of sEMG can benefit from the remarkably reduced influence of cardiac activity, the enhanced detection of the shift in frequency content and the axial isotropy of CREs which facilitates its placement., Peer Reviewed, Postprint (author's final draft)

Trunk stability is essential to maintain upright posture and support functional movements. In this study, we aimed to characterize the muscle activity and movement patterns of trunk flexion during an arm reaching task in sitting healthy subjects and investigate whether trunk stability is affected by a startling acoustic stimulus (SAS). For these purposes, we calculated the electromyographic (EMG) onset latencies and amplitude parameters in 8 trunk, neck, and shoulder muscles, and the tilt angle and movement features from smartphone accelerometer signals recorded during trunk bending in 33 healthy volunteers. Two-way repeated measures ANOVAs were applied to examine the effects of SAS and target distance (15 cm vs 30 cm). We found that SAS markedly reduced the response time and EMG onset latencies of all muscles, without changing neither movement duration nor muscle recruitment pattern. Longer durations, higher tilt angles, and higher EMG amplitudes were observed at 30 cm compared to 15 cm. The accelerometer signals had a higher frequency content in SAS trials, suggesting reduced movement control. The proposed measures have helped to establish the trunk flexion pattern in arm reaching in healthy subjects, which could be useful for future objective assessment of trunk stability in patients with neurological affections., Peer Reviewed, Postprint (published version)

The control of a synthetic human body, or Avatar, is an essential step before the animation process. These two important stages (control and animation) are applied in diverse areas, including video games and 3D animation films. Avatars deserve particular consideration in virtual reality (VR) applications as they represent a fundamental mechanism for interacting with a virtual environment. Avatar’s animation require a precise tracking of the user’s movements, that must be faithfully replicated, in the 3D scenes, by avatars. The control of the ability to animate the upper or lower limbs articulation of the human body, in 3D scenes, is a rather complex task, due to the movements to be reproduced with exact closure, by the avatar, while tracking them by using a camera sensor. These problems are mainly associated with the 3D design of the human body joints and the exact reproduction of anthropomorphic properties in virtual scenes. This article presents an original framework capable of capturing and reproducing, in real time, the same movements provided by humans, using a Microsoft Kinect camera in virtual landscapes. The proposal concentrates on human’s posture failures monitoring by avatars, to improve the posture of people, in rehabilitation stages (victims of stroke or mobility impairment). These scenes contain real motion capture data, which helps to solve postural problems of persons with physical disabilities. Experiments prove the flexibility of the proposed model. During the evaluation phase, we set different scenarios, for an extended analysis of patients’ movements through those of avatars.

Chronic obstructive pulmonary disease (COPD) patients exhibit impaired autonomic control which can be assessed by heart rate variability analysis. The study aims to evaluate the cardiac autonomic responses of COPD patients after completing a conventional six-minute walk test (6MWT). Fifty COPD patients were included in the study, for which an ECG signal (lead II) was acquired by a wearable device, before, during, and after the test. We used the heart rate (HR) time-series to assess the heart rate dynamic during recovery. The heart rate recovery (HRR) marker was evaluated every 5 s after the 6MWT and showed different dynamic trends among severity groups. We compared the HRR among patient groups classified according to the GOLD standard. Significantly larger normalized HRR values (nHRR) were found in mild COPD patients (n=23, GOLD={1,2}; nHRR1=14.8±7.5 %, nHRR2=18.6±8.1 %) compared to those with more disease severity (n=23, GOLD={3,4}; nHRR1=9.3±5.8 %, p=0.002; and nHRR2= 13.7±6.7 %, p=0.041). The largest differences were observed around the first 30 s of the recovery phase (nHRR=10.8±6.6 % vs. nHRR=5.6±4 % p=0.001). Our results showed a slower recovery for the severest patients, suggesting that cardiac parameters like the ones we propose here, may provide valuable information for a better characterization of COPD severity., Peer Reviewed, Postprint (published version)

Chronic obstructive pulmonary disease (COPD) patients exhibit depressed heart rate variability (HRV), while comorbidities may worsen the patients’ prognosis. We investigated whether HRV analyis, clinical markers of disease severity and respiratory function, may explain the presence of cardiac-related comorbidities. Several HRV indices were evaluated in 46 COPD patients before a 6-minute walk test (6MWT). Maximum heart rate (HRmax) and walked distance (Dist) were measured during the test, while heart rate recovery (HRR) was estimated immediately afterwards. All these features and the patient characteristics were used to identify cardiac-related comorbidities (COPDco, n=11). A logistic regression lassifier with regularization was used for modeling and feature selection, while model assessment was performed by leave-oneout cross-validation. Only 4 features were needed to accurately identify comorbidities with overall performance metrics AUC=84%, sensitivity=73% specificity=83%. The feature subset included the ratio given by the forced expiratory volume and the forced vital capacity ((FEV1/FVC), the normalized HRR at minute 3, the Borg-scale of exertional dyspnea and the normalized LF power. These features could provide relevant nformation for early identification of cardiac comorbidities in COPD patients., Peer Reviewed, Postprint (published version)

Background: Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making. Objective: The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge. Methods: Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients’ functional features, and population health risk assessment, were considered. Results: We studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively. Conclusions: The results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge., Peer Reviewed, Postprint (published version)

Respiratory sounds yield pertinent information about respiratory function in both health and disease. Normal lung sound intensity is a characteristic that correlates well with airflow and it can therefore be used to quantify the airflow changes and limitations imposed by respiratory diseases. The dual aims of this study are firstly to establish whether previously reported asymmetries in normal lung sound intensity are affected by varying the inspiratory threshold load or the airflow of respiration, and secondly to investigate whether fixed sample entropy can be used as a valid measure of lung sound intensity. Respiratory sounds were acquired from twelve healthy individuals using four contact microphones on the posterior skin surface during an inspiratory threshold loading protocol and a varying airflow protocol. The spatial distribution of the normal lung sounds intensity was examined. During the protocols explored here the normal lung sound intensity in the left and right lungs in healthy populations was found to be similar, with asymmetries of less than 3 dB. This agrees with values reported in other studies. The fixed sample entropy of the respiratory sound signal was also calculated and compared with the gold standard root mean square representation of lung sound intensity showing good agreement., Postprint (author's final draft)

Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. OSA is also known to be position-dependent in some patients, which is referred to as positional OSA (pOSA). Screening for pOSA is necessary in order to design more personalized and effective treatment strategies. In this article, we propose analyzing accelerometry signals, recorded with a smartphone, to detect and monitor OSA at home. Our objectives were to: (1) develop an algorithm for detecting thoracic movement associated with disordered breathing events; (2) compare the performance of smartphones as OSA monitoring tools with a type 3 portable sleep monitor; and (3) explore the feasibility of using smartphone accelerometry to retrieve reliable patient sleep position data and assess pOSA. Accelerometry signals were collected through simultaneous overnight acquisition using both devices with 13 subjects. The smartphone tool showed a high degree of concordance compared to the portable device and succeeded in estimating the apnea-hypopnea index (AHI) and classifying the severity level in most subjects. To assess the agreement between the two systems, an event-by-event comparison was performed, which found a sensitivity of 90% and a positive predictive value of 80%. It was also possible to identify pOSA by determining the ratio of events occurring in a specific position versus the time spent in that position during the night. These novel results suggest that smartphones are promising mHealth tools for OSA and pOSA monitoring at home., Peer Reviewed, Postprint (published version)

Reaching and grasping (R&G) is a skilled voluntary movement which is critical for animals. In this work, we aim to identify muscle synergy patterns from R&G movements in rats and show how these patterns can be used to characterize such movements and investigate their consistency and repeatability. For that purpose, we analyzed the electromyographic (EMG) activity of five forelimb muscles recorded while the animals were engaged in R&G tasks. Our dataset included 200 R&G attempts from three different rats. Non-negative matrix factorization was used to decompose EMG signals and extract muscle synergies. We compared all pairs of attempts and created cross-validated models to study intra- and inter-subject variability. We found that three synergies were enough to accurately reconstruct the EMG envelopes. These muscle synergies and their corresponding activation coefficients were very similar for all the attempts in the database, providing a general pattern to describe the movement. Results suggested that the movement strategy adopted by an individual in its different attempts was highly repetitive, but also resembled the strategies adopted by the other animals. Inter-subject variability was not much higher than intra-subject variability. This study is a proof-of-concept, but the proposed approaches can help to establish whether there is a stereotyped pattern of neuromuscular activity in R&G movement in healthy rats, and the changes that occur in animal models of acute neurological injuries. Research on muscle synergies could elucidate motor control mechanisms, and lead to quantitative tools for evaluating upper limb motor impairment after an injury, Peer Reviewed, Postprint (published version)

ung sound (LS) signals are often contaminated by impulsive artifacts that complicate the estimation of lung sound intensity (LSI) using conventional amplitude estimators. Fixed sample entropy (fSampEn) has proven to be robust to cardiac artifacts in myographic respiratory signals. Similarly, fSampEn is expected to be robust to artifacts in LS signals, thus providing accurate LSI estimates. However, the choice of fSampEn parameters depends on the application and fSampEn has not previously been applied to LS signals. This study aimed to perform an evaluation of the performance of the most relevant fSampEn parameters on LS signals, and to propose optimal fSampEn parameters for LSI estimation. Different combinations of fSampEn parameters were analyzed in LS signals recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing. The performance of fSampEn was assessed by means of its cross-covariance with flow signals, and optimal fSampEn parameters for LSI estimation were proposed., Postprint (author's final draft)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Neural respiratory drive as measured by the electromyography allows the study of the imbalance between the load on respiratory muscles and its capacity. Surface respiratory electromyography (sEMG) is a non-invasive tool used for indirectly assessment of NRD. It also provides a way to evaluate the level and pattern of respiratory muscle activation. The prevalence of electrocardiographic activity (ECG) in respiratory sEMG signals hinders its proper evaluation. Moreover, the occurrence of abnormal heartbeats or cardiac arrhythmias in respiratory sEMG measures can make even more challenging the NRD estimation. Respiratory sEMG can be evaluated using the fixed sample entropy (fSampEn), a technique which is less affected by cardiac artefacts. The aim of this work was to investigate the performance of the fSampEn, the root mean square (RMS) and the average rectified value (ARV) on respiratory sEMG signals with supraventricular arrhythmias (SVA) for NRD estimation. fSampEn, ARV and RMS parameters increased as the inspiratory load increased during the test. fSampEn was less influenced by ECG with SVAs for the NRD estimation showing a greater response to respiratory sEMG, reflected with a higher percentage increase with increasing load (228 % total increase, compared to 142 % and 135 % for ARV and RMS, respectively)., Peer Reviewed, Postprint (author's final draft)

this work, we evaluated a non-linear approach to estimate morphological variations in ECG depolarization, in the context of intermittent hypoxia (IH). Obstructive apnea sequences were provoked for 15 minutes in anesthetized Sprague-Dawley rats, alternating with equal periods of normal breathing, in a recurrent obstructive sleep apnea (OSA) model. Each apnea episode lasted 15 s, while the frequency used for each sequence was randomly selected. Average heartbeats obtained before the start and at the end of each episode, were delineated to extract only the QRS wave. Then, the segmented QRS waves were non-linearly aligned using the dynamic time warping (DWT) algorithm. Morphological QRS changes in both the amplitude and temporal domains were estimated from this alignment procedure. The hypoxic and basal segments were analyzed using ECG (lead I) recordings acquired during the experiment. To assess the effects of IH over time, the changes relative to the basal QRS wave were determined, in the intervals prior to each successive events until the end of the experiment. The results showed a progressive increase in the amplitude and time-domain morphological markers of the QRS wave along the experiment, which were strongly correlated with the changes in traditional QRS markers (r ˜ 0.9). Significant changes were found between pre-apnea and hypoxic measures only for the time-domain analysis (p<; 0.001), probably due to the short duration of the simulated apnea episodes. Clinical relevance Increased variability in ECG depolarization morphology during recurrent hypoxic episodes would be closely related to the expression of cardiovascular dysfunction in OSA patients., Peer Reviewed, Postprint (author's final draft)

Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow. Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful over- and under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners. Keywords: Data mining and knowledge discovery; Machine learning; Emerging applications and technology; Intelligent vehicles; Ambient intelligence