Developing SysteMatic: Prevention, precision and equity by design for people living with multiple long-term conditions.

Background: The number of individuals living with multiple (≥2) long term conditions (MLTCs) is a growing global challenge. People with MLTCs experience reduced life expectancy, complex healthcare needs, higher healthcare utilisation, increased burden of treatment, poorer quality of life and higher mortality. Evolving technologies including artificial intelligence (AI) could address some of these challenges by enabling more preventive and better integrated care, however, they may also exacerbate inequities. Objective: We aim to deliver an equity focused, action-ready plan for transforming MLTC prevention and care, co-designed by people with lived experience of MLTCs and delivered through an Innovation Hub: SysteMatic. Design: Our Hub is being co-designed by people with lived experience of MLTCs, practitioners, academics and industry partners in Liverpool and Glasgow, UK. This work builds on research into mental-physical health interdependence across the life-course, and on mobilisation of large-scale quantitative data and technology validation in health and care systems serving deprived populations in Glasgow and Liverpool. We work with 3 population segments: 1) Children & Families: facing psychosocial and environmental challenges with lifetime impacts; 2). Working Life: people with poorly integrated mental, physical and social care; and 3) Pre-Frailty: older people with MLTCs. We aim to understand their experiences and in parallel look at routinely collected health data on people with MLTCs to help us identify targets for intervention. We are co-identifying opportunities for systems transformation with our patient partners, healthcare professionals and through discussion with companies and public-sector organisations. We are co-defining 3/5/7-year MLTC innovation/transition targets and sustainable learning approaches. Discussion: SysteMatic will deliver an actionable MLTC Innovation Hub strategic plan, with investment from the UK National Health Service, civic health and care partners, universities, and industry, enabling feedback of well-translated, patient and public prioritised problems into the engineering, physical, health and social sciences to underpin future equitable innovation delivery.

Multi-band ultrathin reflective metasurface for linear and circular polarization conversion in Ku, K, and Ka bands.

Linear polarization (LP) and circular polarization (CP) holds paramount importance in Ku, K, and Ka bands for satellite based communication, and remote sensing applications. Satellite based remote sensing applications face challenges like atmospheric attenuation, noise & interference, and signal degradation. Moreover, satellite based communication application demands CP in two distinct, non-adjacent frequency bands with orthogonal polarizations at greater oblique angles, considering the unpredictable incidence angles of electromagnetic (EM) waves. Addressing these challenges, an innovative metasurface polarization converter is proposed to operate efficiently across the Ku-band (13.5-18.0 GHz), K-band (18.0-26.5 GHz), and Ka-band (26.5-38.5 GHz) frequency ranges. The converter achieves left-handed circular polarization (LHCP) in the Ku- and Ka-bands within the frequency ranges of 14.57-15.65 GHz and 27.47-33.85 GHz for y-polarized incident EM waves. Additionally, it provides right-handed circular polarization (RHCP) in the K- and Ka-bands at 17.27-23.92 GHz and 35.87-38.32 GHz for y-polarized incident EM waves. The LP conversion ratio exceeds 95% in the frequency bands of 15.97-16.85 GHz, 24.70-26.65 GHz, and 34.37-35.45 GHz for y-polarized, LHCP, and RHCP incident EM waves, respectively. The metasurface exhibits robust performance up to incidence angles of 45 degrees under oblique conditions. Experimental validation using traditional board-circuit manufacturing demonstrates close agreement between measured co- and cross-polarized reflection coefficients and simulations in the 13.5-18 GHz, and 24-38.5 GHz frequency range. Thin metasurface with a thickness of only 0.64 = 0.013λo mm, the proposed design outperforms existing studies in the literature, establishing its competitive edge in terms of structure and performance.

Transparent RFID tag wall enabled by artificial intelligence for assisted living.

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the Transparent RFID Tag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of 95.6 % under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.

Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios.

Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

A high-performance sub-THz planar antenna array for THz sensing and imaging applications.

Terahertz (THz) spectral region from 0.1 to 3 THz is envisaged to hold immense potential in the next generation of wireless technologies. Recently, research has focused on this terahertz gap, because of its unprecedented channel capacities. At the physical layer, the design complexities and fabrication of THz devices, especially antennas are the prime bottlenecks to realize its full potential. This article introduces a cost-effective, easy-to-fabricate, and reproducible sub-THz antenna design based on a single-layer planar printed circuit board technology. The antenna incorporates carefully designed quasi-cross slots and applied machine learning-assisted global optimization techniques to achieve the desired performance metrics. The antenna performance is elucidated through numerical simulations and verified through a rigorous in-house THz experimental framework around 100-110 GHz. The proposed antenna offers a peak gain of 13.90 dBi with less than 1 dB variation within the entire band of 100-110 GHz. The antenna holds the potential to achieve terabits per second data rates and futuristic high-resolution short-range THz imaging applications.

Tag-free indoor fall detection using transformer network encoder and data fusion.

This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5 % . This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.

Bridging digital and electromagnetic realms for enhanced wireless communication: current space.

Recently, Tie Jun Cui and team members introduced innovative macroscopic and statistical models for digital coding metasurfaces, bridging the digital and electromagnetic realms and quantifying information loss for enhanced wireless communication system design. This is a highlight of it.

Highly Sensitive Terahertz Metasurface Based on Electromagnetically Induced Transparency-Like Resonance in Detection of Skin Cancer Cells.

Terahertz (THz) metasurfaces based on high Q-factor electromagnetically induced transparency-like (EIT-like) resonances are promising for biological sensing. Despite this potential, they have not often been investigated for practical differentiation between cancerous and healthy cells. The present methodology relies mainly on refractive index sensing, while factors of transmission magnitude and Q-factor offer significant information about the tumors. To address this limitation and improve sensitivity, we fabricated a THz EIT-like metasurface based on asymmetric resonators on an ultra-thin and flexible dielectric substrate. Bright-dark modes coupling at 1.96 THz was experimentally verified, and numerical results and theoretical analysis were presented. An enhanced theoretical sensitivity of 550 GHz/RIU was achieved for a sample with a thickness of 13 µm due to the ultra-thin substrate and novel design. A two-layer skin model was generated whereby keratinocyte cell lines were cultured on a base of collagen. When NEB1-shPTCH (basal cell carcinoma (BCC)) were switched out for NEB1-shCON cell lines (healthy) and when BCC's density was raised from 1 × 105 to 2.5 × 105, a frequency shift of 40 and 20 GHz were observed, respectively. A combined sensing analysis characterizes different cell lines. The findings may open new opportunities for early cancer detection with a fast, less-complicated, and inexpensive method.

RIS-assisted near-field localization using practical phase shift model.

Our research focuses on examining the problem of localizing user equipment (UE) in the uplink scenario using reconfigurable intelligent surfaces (RIS) based lens. We carry out a thorough analysis of the Fisher information matrix (FIM) and assess the influence of various RIS-based lens configurations using an actual RIS phase-dependent amplitude variations model. Furthermore, to reduce the complexity of the maximum likelihood (ML) estimator, a simple localization algorithm-based angular expansion is presented. Simulation results show superior localization performance when prior location information is available for directional and positional channel configurations. The position error bound (PEB) and the root mean square error (RMSE) are studied to evaluate the localization accuracy of the user utilizing the realistic RIS phase-dependent amplitude model in the near-field region. Furthermore, the achievable data rate is obtained in the same region using the realistic RIS phase-dependent amplitude model. It is noticed that adopting the actual RIS phase-dependent amplitude model under the near-field channel increases the localization error and degrades the data rate performance for amplitude value less than one so, the unity assumption of the RIS phase shift model used widely in the literature is inaccurate.

A comprehensive multimodal dataset for contactless lip reading and acoustic analysis.

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject's lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition.

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.

Reconfigurable absorptive and polarization conversion metasurface consistent for wide angles of incidence.

In this paper, a single-layer reconfigurable reflective metasurface is presented. The proposed metasurface operates at 5.4 GHz and can achieve either absorption or cross-polarization conversion corresponding at two different diode biasing states. The reflective metasurface acts as an absorber for an incident wave when the diodes are forward-biased. Similarly, it changes the polarization state of the reflected wave for a linearly polarized incident wave when the diodes are reverse-biased. The proposed structure maintains the aforementioned performance characteristics for oblique incidence, up to 60° compared to the perpendicular incidence. The proposed metasurface can achieve linear to linear polarization conversion with polarization conversion ratio (PCR) > 95% and absorption, with absorption ratio (AR) > 80% in the same frequency band just by reconfiguring the state of the PIN diodes.

Dual-sense slot-based CP MIMO antenna with polarization bandwidth reconfigurability.

In this letter, a compact, planar circularly polarized (CP) sub-GHz slot-based multiple-input-multiple-output (MIMO) antenna with dual sense CP along with polarization bandwidth reconfigurability is presented. The pentagonal reactively loaded slot is fed by two folded tapered feedlines to achieve CP. The antenna offers left-hand-circular polarization (RHCP) with the as well as right hand circular polarization (LHCP). The antenna exhibit linearly polarization (LP) by exciting two ports simultaneously. Moreover, the antenna CP resonance can be reconfigured by varying the capacitance of the varactor diode. The antenna has a wide -10 dB operating frequency band from 578-929 MHz. while the axial ratio (AR) bandwidth ranges from 490-810 MHz. Moreover, the two elements MIMO are optimized and placed on compact dimensions 100 × 100 × 0.76 mm3 to realize pattern diversity. The antenna's key characteristics are compact size, wide-band sub-GHz operation, dual sense CP, polarization bandwidth reconfigurability and good MIMO performance. Thus, it is a suitable candidate to be utilized in CubeSats applications in sub-GHz bands.

Nature-inspired spider web shaped UHF RFID reader antenna for IoT and healthcare applications.

This paper proposes a nature-inspired spider web-shaped ultra-high frequency (UHF) radio frequency identification (RFID) reader antenna and battery-free sensor-based system for healthcare applications. This antenna design consists of eight concentric decagons of various sizes and five straight microstrip lines.These lines are connected to the ground using 50 [Formula: see text] resistors from both ends, except for one microstrip line that is reserved for connecting a feeding port. The reader antenna design features fairly strong and uniform electric and magnetic field characteristics. It also exhibits wideband characteristics, covering whole UHF RFID band (860-960 MHz) and providing a tag reading volume of 200 [Formula: see text] 200 [Formula: see text] 20 mm[Formula: see text]. Additionally, it has low gain characteristics, which are necessary for the majority of nearfield applications to prevent the misreading of other tags. Moreover, the current distribution in this design is symmetric throughout the structure, effectively resolving orientation sensitivity issues commonly encountered in low-cost linearly polarized tag antennas. The measurement results show that the reader antenna can read medicine pills tagged using low-cost passive/battery-free RFID tags, tagged expensive jewelry, intervenes solution, and blood bags positioned in various orientations. As a result, the proposed reader antenna-based system is a strong contender for near-field RFID, healthcare, and IoT applications.

Acoustic Lung Imaging Utilized in Continual Assessment of Patients with Obstructed Airway: A Systematic Review.

Smart respiratory therapy is enabled by continual assessment of lung functions. This systematic review provides an overview of the suitability of equipment-to-patient acoustic imaging in continual assessment of lung conditions. The literature search was conducted using Scopus, PubMed, ScienceDirect, Web of Science, SciELO Preprints, and Google Scholar. Fifteen studies remained for additional examination after the screening process. Two imaging modalities, lung ultrasound (LUS) and vibration imaging response (VRI), were identified. The most common outcome obtained from eleven studies was positive observations of changes to the geographical lung area, sound energy, or both, while positive observation of lung consolidation was reported in the remaining four studies. Two different modalities of lung assessment were used in eight studies, with one study comparing VRI against chest X-ray, one study comparing VRI with LUS, two studies comparing LUS to chest X-ray, and four studies comparing LUS in contrast to computed tomography. Our findings indicate that the acoustic imaging approach could assess and provide regional information on lung function. No technology has been shown to be better than another for measuring obstructed airways; hence, more research is required on acoustic imaging in detecting obstructed airways regionally in the application of enabling smart therapy.

Configuring reconfigurable intelligent surfaces using a practical codebook approach.

It is proven that the scattering, reflection, and refraction properties of electromagnetic signals can be adapted and managed by using reconfigurable intelligent surfaces (RISs). In this paper, we have investigated the performance of a single-input-single-output (SISO) wideband system in terms of achievable data rate by optimizing the phases of RIS elements and performing a fair power allocation for each subcarrier over the entire bandwidth. A new beamforming codebook is developed from which the maximizing signal-to-noise (SNR) configuration is selected. The channel state information (CSI) along with the selected maximizing SNR configuration is then used by the proposed power algorithm to obtain the optimal configuration of the RIS. To validate our proposed method, it is compared with state-of-the-art semidefinite relaxation (SDR) scheme in terms of performance, complexity and run-time consumption. Our method shows dramatically lower computational complexity than the SDR method and achieves an order of 2.5 increase in the achievable data rate with an optimized RIS compared with an un-configured surface.

Flexible Antenna with Circular/Linear Polarization for Wideband Biomedical Wireless Communication.

A wideband low-profile radiating G-shaped strip on a flexible substrate is proposed to operate as biomedical antenna for off-body communication. The antenna is designed to produce circular polarization over the frequency range 5-6 GHz to communicate with WiMAX/WLAN antennas. Furthermore, it is designed to produce linear polarization over the frequency range 6-19 GHz for communication with the on-body biosensor antennas. It is shown that an inverted G-shaped strip produces circular polarization (CP) of the opposite sense to that produced by G-shaped strip over the frequency range 5-6 GHz. The antenna design is explained and its performance is investigated through simulation, as well as experimental measurements. This antenna can be viewed as composed of a semicircular strip terminated with a horizontal extension at its lower end and terminated with a small circular patch through a corner-shaped strip extension at its upper end to form the shape of "G" or inverted "G". The purpose of the corner-shaped extension and the circular patch termination is to match the antenna impedance to 50 Ω over the entire frequency band (5-19 GHz) and to improve the circular polarization over the frequency band (5-6 GHz). To be fabricated on only one face of the flexible dielectric substrate, the antenna is fed through a co-planar waveguide (CPW). The antenna and the CPW dimensions are optimized to obtain the most optimal performance regarding the impedance matching bandwidth, 3dB Axial Ratio (AR) bandwidth, radiation efficiency, and maximum gain. The results show that the achieved 3dB-AR bandwidth is 18% (5-6 GHz). Thus, the proposed antenna covers the 5 GHz frequency band of the WiMAX/WLAN applications within its 3dB-AR frequency band. Furthermore, the impedance matching bandwidth is 117% (5-19 GHz) which enables low-power communication with the on-body sensors over this wide range of the frequency. The maximum gain and radiation efficiency are 5.37 dBi and 98%, respectively. The overall antenna dimensions are 25 × 27 × 0.13 mm3 and the bandwidth-dimension ratio (BDR) is 1733.

A sub 1 GHz ultra miniaturized folded dipole patch antenna for biomedical applications.

A miniaturized folded dipole patch antenna (FDPA) design for biomedical applications operating at sub 1 GHz (434 MHz) band is presented. Antenna is fabricated on FR-4 substrate material having dimensions of 16.40 mm [Formula: see text] 8.60 mm [Formula: see text] 1.52 mm (0.023[Formula: see text] [Formula: see text] 0.012[Formula: see text] [Formula: see text] 0.002[Formula: see text]). Indirect feed coupling is applied through two parallel strips at bottom layer of the substrate. The antenna size is reduced by 83% through lumped inductor placed at the center path of the radiating FDPA, suitable for biomedical (implantable) applications and hyperthermia. Moreover, Impedance matching is achieved without using any Balun transformer or any other complex matching network. The proposed antenna provides an impedance bandwidth of 6 MHz (431-437 MHz) below - 10 dB and a gain of - 31 dB at 434 MHz. The designed antenna is also placed on a human body model to evaluate its performance for hyperthermia through Specific Absorption Rate (SAR), Effective Field Size (EFS), and penetration depth (PD).