Implantable photonic nano-modulators open perspectives for advanced optical interfaces with deep brain areas.

An emerging trend at the forefront of optical neural interfaces leverages the optical properties of photonic nanostructures to modulate light delivery and collection patterns in deep brain regions. This perspective article surveys the early works that have spearheaded this promising strategy, and discusses its promise towards the establishment of a class of augmented nano-neurophotonic probes.

Eco-Friendly Production of Polyvinyl Alcohol/Carboxymethyl Cellulose Wound Healing Dressing Containing Sericin.

Wound dressing production represents an important segment in the biomedical healthcare field, but finding a simple and eco-friendly method that combines a natural compound and a biocompatible dressing production for biomedical application is still a challenge. Therefore, the aim of this study is to develop wound healing dressings that are environmentally friendly, low cost, and easily produced, using natural agents and a physical crosslinking technique. Hydrogel wound healing dressings were prepared from polyvinyl alcohol/carboxymethyl cellulose and sericin using the freeze-thawing method as a crosslinking method. The morphological characterization was carried out by scanning electron microscopy (SEM), whereas the mechanical analysis was carried out by dynamic mechanical analysis (DMA) to test the tensile strength and compression properties. Then, the healing property of the wound dressing material was tested by in vitro and ex vivo tests. The results show a three-dimensional microporous structure with no cytotoxicity, excellent stretchability with compressive properties similar to those of human skin, and excellent healing properties. The proposed hydrogel dressing was tested in vitro with HaCaT keratinocytes and ex vivo with epidermal tissues, demonstrating an effective advantage on wound healing acceleration. Accordingly, this study was successful in developing wound healing dressings using natural agents and a simple and green crosslinking method.

Exploiting holographically encoded variance to transmit labelled images through a multimode optical fiber.

Artificial intelligence has emerged as promising tool to decode an image transmitted through a multimode fiber (MMF) by applying deep learning techniques. By transmitting thousands of images through the MMF, deep neural networks (DNNs) are able to decipher the seemingly random output speckle patterns and unveil the intrinsic input-output relationship. High fidelity reconstruction is obtained for datasets with a large degree of homogeneity, which underutilizes the capacity of the combined MMF-DNN system. Here, we show that holographic modulation can encode an additional layer of variance on the output speckle pattern, improving the overall transmissive capabilities of the system. Operatively, we have implemented this by adding a holographic label to the original dataset and injecting the resulting phase image into the fiber facet through a Fourier transform lens. The resulting speckle pattern dataset can be clustered primarily by holographic label, and can be reconstructed without loss of fidelity. As an application, we describe how color images may be segmented into RGB components and each color component may then be labelled by distinct hologram. A ResUNet architecture was then used to decode each class of speckle patterns and reconstruct the color image without the need for temporal synchronization between sender and receiver.

Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers' performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.

Optimizing the internal phase reference to shape the output of a multimode optical fiber.

Pre-shaping light to achieve desired amplitude distributions at the tip of a multimode fiber (MMF) has emerged as a powerful method allowing a wide range of imaging techniques to be implemented at the distal facet. Such techniques rely on measuring the transmission matrix of the optically turbid waveguide which scrambles the coherent input light into an effectively random speckle pattern. Typically, this is done by measuring the interferogram between the output speckle and a reference beam. In recent years, an optical setup where the reference beam passes through the MMF has become an attractive configuration because of the high interferometric stability of the common optical path. However, the merits and drawbacks of an internal reference beam remain controversial. The measurement of the transmission matrix is known to depend on the choice of internal reference and has been reported to result in "blind spots" due to phase singularities of the reference beam. Here, we describe how the focussing efficiency of the calibration can be increased by several percent by optimising the choice of internal reference beam.

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview.

A quantitative evaluation of kinetic parameters, the joint's range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device's positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user's vital signs directly from the body in an accurate and non-invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach's subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post-operative rehabilitation and athletes' training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user's health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

Design and Fabrication of a Flexible Gravimetric Sensor Based on a Thin-Film Bulk Acoustic Wave Resonator.

Sensing systems are becoming less and less invasive. In this context, flexible materials offer new opportunities that are impossible to achieve with bulky and rigid chips. Standard silicon sensors cannot be adapted to curved shapes and are susceptible to big deformations, thus discouraging their use in wearable applications. Another step forward toward minimising the impacts of the sensors can be to avoid the use of cables and connectors by exploiting wireless transmissions at ultra-high frequencies (UHFs). Thin-film bulk acoustic wave resonators (FBARs) represent the most promising choice among all of the piezoelectric microelectromechanical system (MEMS) resonators for the climbing of radio frequencies. Accordingly, the fabrication of FBARs on flexible and wearable substrates represents a strategic step toward obtaining a new generation of highly sensitive wireless sensors. In this work, we propose the design and fabrication of a flexible gravimetric sensor based on an FBAR on a polymeric substrate. The resonator presents one of the highest electromechanical coupling factors in the category of flexible AlN-based FBARs, equal to 6%. Moreover, thanks to the polymeric support layer, the presence of membranes can be avoided, which leads to a faster and cheaper fabrication process and higher robustness of the structure. The mass sensitivity of the device was evaluated, obtaining a promising value of 23.31 ppm/pg. We strongly believe that these results can pave the way to a new class of wearable MEMS sensors that exploit ultra-high-frequency (UHF) transmissions.

Wearable Heart Rate Monitoring Device Communicating in 5G ISM Band for IoHT.

Advances in wearable device technology pave the way for wireless health monitoring for medical and non-medical applications. In this work, we present a wearable heart rate monitoring platform communicating in the sub-6GHz 5G ISM band. The proposed device is composed of an Aluminium Nitride (AlN) piezoelectric sensor, a patch antenna, and a custom printed circuit board (PCB) for data acquisition and transmission. The experimental results show that the presented system can acquire heart rate together with diastolic and systolic duration, which are related to heart relaxation and contraction, respectively, from the posterior tibial artery. The overall system dimension is 20 mm by 40 mm, and the total weight is 20 g, making this device suitable for daily utilization. Furthermore, the system allows the simultaneous monitoring of multiple subjects, or a single patient from multiple body locations by using only one reader. The promising results demonstrate that the proposed system is applicable to the Internet of Healthcare Things (IoHT), and particularly Integrated Clinical Environment (ICE) applications.

Toward Plasmonic Neural Probes: SERS Detection of Neurotransmitters through Gold-Nanoislands-Decorated Tapered Optical Fibers with Sub-10 nm Gaps.

Integration of plasmonic nanostructures with fiber-optics-based neural probes enables label-free detection of molecular fingerprints via surface-enhanced Raman spectroscopy (SERS), and it represents a fascinating technological horizon to investigate brain function. However, developing neuroplasmonic probes that can interface with deep brain regions with minimal invasiveness while providing the sensitivity to detect biomolecular signatures in a physiological environment is challenging, in particular because the same waveguide must be employed for both delivering excitation light and collecting the resulting scattered photons. Here, a SERS-active neural probe based on a tapered optical fiber (TF) decorated with gold nanoislands (NIs) that can detect neurotransmitters down to the micromolar range is presented. To do this, a novel, nonplanar repeated dewetting technique to fabricate gold NIs with sub-10 nm gaps, uniformly distributed on the wide (square millimeter scale in surface area), highly curved surface of TF is developed. It is experimentally and numerically shown that the amplified broadband near-field enhancement of the high-density NIs layer allows for achieving a limit of detection in aqueous solution of 10-7  m for rhodamine 6G and 10-5  m for serotonin and dopamine through SERS at near-infrared wavelengths. The NIs-TF technology is envisioned as a first step toward the unexplored frontier of in vivo label-free plasmonic neural interfaces.

A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization.

Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR-respiration rate; TI/TE-inhalation/exhalation time; IER-inhalation-to-exhalation time; V-flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland-Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = -0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/-1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications.

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview.

Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep's functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG-electroencephalography, ECG-electrocardiography, EMG-electromyography, EOG-electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.

Tapered fibertrodes for optoelectrical neural interfacing in small brain volumes with reduced artefacts.

Deciphering the neural patterns underlying brain functions is essential to understanding how neurons are organized into networks. This deciphering has been greatly facilitated by optogenetics and its combination with optoelectronic devices to control neural activity with millisecond temporal resolution and cell type specificity. However, targeting small brain volumes causes photoelectric artefacts, in particular when light emission and recording sites are close to each other. We take advantage of the photonic properties of tapered fibres to develop integrated 'fibertrodes' able to optically activate small brain volumes with abated photoelectric noise. Electrodes are positioned very close to light emitting points by non-planar microfabrication, with angled light emission allowing the simultaneous optogenetic manipulation and electrical read-out of one to three neurons, with no photoelectric artefacts, in vivo. The unconventional implementation of two-photon polymerization on the curved taper edge enables the fabrication of recoding sites all around the implant, making fibertrodes a promising complement to planar microimplants.

Holographic Manipulation of Nanostructured Fiber Optics Enables Spatially-Resolved, Reconfigurable Optical Control of Plasmonic Local Field Enhancement and SERS.

Integration of plasmonic structures on step-index optical fibers is attracting interest for both applications and fundamental studies. However, the possibility to dynamically control the coupling between the guided light fields and the plasmonic resonances is hindered by the turbidity of light propagation in multimode fibers (MMFs). This pivotal point strongly limits the range of studies that can benefit from nanostructured fiber optics. Fortunately, harnessing the interaction between plasmonic modes on the fiber tip and the full set of guided modes can bring this technology to a next generation progress. Here, the intrinsic wealth of information of guided modes is exploited to spatiotemporally control the plasmonic resonances of the coupled system. This concept is shown by employing dynamic phase modulation to structure both the response of plasmonic MMFs on the plasmonic facet and their response in the corresponding Fourier plane, achieving spatial selective field enhancement and direct control of the probe's work point in the dispersion diagram. Such a conceptual leap would transform the biomedical applications of holographic endoscopic imaging by integrating new sensing and manipulation capabilities.

Neurophotonic tools for microscopic measurements and manipulation: status report.

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Design of vanadium-dioxide-based resonant structures for tunable optical response.

Phase change materials are suitable for tunable photonic devices where the optical response can be altered under external stimuli, such as heat, an electrical or an optical signal. In this scenario, we performed numerical simulations to study the optical properties of a flat unpatterned resonant structure and a grating, both coated with a thin film of vanadium dioxide (VO2). Our results suggest that it is possible to modulate broadband and narrowband reflectance spectra of the resonant structures in the visible to near-infrared range by more than 40 % when the VO2 undergoes an insulator-to-metal phase transition. Resonant devices with a tunable spectral response may find application in sensors, filters, absorbers, and detectors.

An open source three-mirror laser scanning holographic two-photon lithography system.

Two-photon polymerization is a widely adopted technique for direct fabrication of 3D and 2D structures with sub-diffraction-limit features. Here we present an open-hardware, open-software custom design for a holographic multibeam two-photon polymerization system based on a phase-only spatial light modulator and a three-mirror scanhead. The use of three reflective surfaces, two of which scanning the phase-modulated image along the same axis, allows to overcome the loss of virtual conjugation within the large galvanometric mirrors pair needed to accommodate the holographic projection. This extends the writing field of view among which the hologram can be employed for multi-beam two-photon polymerization by a factor of ~2 on one axis (i.e. from ~200µm to ~400µm), with a voxel size of ~250nm × ~1050nm (lateral × axial size), and writing speed of three simultaneous beams of 2000 voxels/s, making our system a powerful and reliable tool for advanced micro and nano-fabrications on large area.

Numerical Calculation of the Light Propagation in Tapered Optical Fibers for Optical Neural Interfaces.

As implantable optical systems recently enabled new approaches to study the brain with optical radiations, tapered optical fibers emerged as promising implantable waveguides to deliver and collect light from sub-cortical structures of the mouse brain. They rely on a specific feature of multimodal fiber optics: as the waveguide narrows, the number of guided modes decreases and the radiation can gradually couple with the environment. This happens along a taper segment whose length can be tailored to match with the depth of functional structures of the mouse brain, and can extend for a few millimeters. This anatomical requirement results in optical systems which have an active area that is very long compared to the wavelength of the light they guide and their behavior is typically estimated by ray tracing simulations, because finite element methods are too computationally demanding. Here we present a computational technique that exploits the beam-envelope method and the cylindrical symmetry of the fibers to provide an efficient and exact calculation of the electric field along the fibers, which may enable the design of neural interfaces optimized to meet different goals.

Orthogonalization of far-field detection in tapered optical fibers for depth-selective fiber photometry in brain tissue.

The field of implantable optical neural interfaces has recently enabled the interrogation of neural circuitry with both cell-type specificity and spatial resolution in sub-cortical structures of the mouse brain. This generated the need to integrate multiple optical channels within the same implantable device, motivating the requirement of multiplexing and demultiplexing techniques. In this article, we present an orthogonalization method of the far-field space to introduce mode-division demultiplexing for collecting fluorescence from the implantable tapered optical fibers. This is achieved by exploiting the correlation between the transversal wavevector k t of the guided light and the position of the fluorescent sources along the implant, an intrinsic property of the taper waveguide. On these bases, we define a basis of orthogonal vectors in the Fourier space, each of which is associated with a depth along the taper, to simultaneously detect and demultiplex the collected signal when the probe is implanted in fixed mouse brain tissue. Our approach complements the existing multiplexing techniques used in silicon-based photonics probes with the advantage of a significant simplification of the probe itself.