Conformable AlN Piezoelectric Sensors as a Non-invasive Approach for Swallowing Disorder Assessment

Continuous Estimation of Swallowing Motion With EMG and MMG Signals

Oropharyngeal dysphagia (OD) is a symptom of swallowing dysfunction that is associated with aspiration, severe respiratory complications, and even death. OD is a highly prevalent condition in populations including the elderly and patients with neurological diseases (e.g., stroke and Parkinson's disease (PD)). Assessment of swallow function is crucial for managing OD, yet depends on devices for long-term monitoring during daily life and relevant methods for accurately assessing swallow function. The videofluoroscopic swallowing study (VFSS) is usually considered a gold standard method. However, it has several limitations, such as radiation exposure, the need for technical experts, high cost, and clinical use only. This study investigates the performances of electromyography (EMG) and mechanomyography (MMG) signals, which can be easily measured using wearable sensors, to continuously estimate swallowing movement. Meanwhile, three methods, i.e., Gaussian process regression (GPR), LSTM, and random forest (RF), are used for swallowing motion estimation based on EMG/MMG signals measured from six healthy subjects and a patient with PD, respectively. Moreover, a depth camera-based approach is proposed to provide the reference laryngeal displacement (i.e., the swallowing movement). The experimental results show that EMG models with three machine learning methods can accurately estimate swallowing movement. For the healthy subjects, the mean correlation coefficient (CC) is about 0.90 and the normalized root mean square error (NRMSE) is less than 0.15. For the PD patient, the CC is 0.804 and the NRMSE is 0.205 when using RF. The performance of the MMG model is comparable to that of EMG: CC/NRMSE of the LSTM model is 0.844/0.150 (healthy subjects); CC/NRMSE of RF model is 0.727/0.204 (PD patient). To the best of our knowledge, this is the first study proving that both EMG and MMG are two effective means for an accurate continuous estimation of swallowing motion, enabling the possibility of a safe and convenient evaluation and management of OD.

Advancements in flexible biomechanical energy harvesting for smart health applications

Advancing flexible electronics enables timely smart health management and diagnostic interventions. However, current health electronics typically rely on replaceable batteries or external power sources, requiring direct contact with the human skin or organs. This setup often results in rigid and bulky devices, reducing user comfort during long-term use. Flexible biomechanical energy harvesting technology, based on triboelectric or piezoelectric strategies, offers a promising approach for continuous and comfortable smart health applications, providing a sustainable power supply and self-powered sensing. This review systematically examines biomechanical energy sources around the human body, explores various energy harvesting mechanisms and their applications in smart health, and concludes with insights and future perspectives in this field.

Enhancing cardiovascular health monitoring: Simultaneous multi-artery cardiac markers recording with flexible and bio-compatible AlN piezoelectric sensors

Continuous monitoring of cardiovascular parameters like pulse wave velocity (PWV), blood pressure wave (BPW), stiffness index (SI), reflection index (RI), mean arterial pressure (MAP), and cardio-ankle vascular index (CAVI) has significant clinical importance for the early diagnosis of cardiovascular diseases (CVDs). Standard approaches, including echocardiography, impedance cardiography, or hemodynamic monitoring, are hindered by expensive and bulky apparatus and accessibility only in specialized facilities. Moreover, noninvasive techniques like sphygmomanometry, electrocardiography, and arterial tonometry often lack accuracy due to external electrical interferences, artifacts produced by unreliable electrode contacts, misreading from placement errors, or failure in detecting transient issues and trends. Here, we report a bio-compatible, flexible, noninvasive, low-cost piezoelectric sensor for continuous and real-time cardiovascular monitoring. The sensor, utilizing a thin aluminum nitride film on a flexible Kapton substrate, is used to extract heart rate, blood pressure waves, pulse wave velocities, and cardio-ankle vascular index from four arterial pulse sites: carotid, brachial, radial, and posterior tibial arteries. This simultaneous recording, for the first time in the same experiment, allows to provide a comprehensive cardiovascular patient's health profile. In a test with a 28-year-old male subject, the sensor yielded the SI = 7.1 ± 0.2 m/s, RI = 54.4 ± 0.5 %, MAP = 86.2 ± 1.5 mmHg, CAVI = 7.8 ± 0.2, and seven PWVs from the combination of the four different arterial positions, in good agreement with the typical values reported in the literature. These findings make the proposed technology a powerful tool to facilitate personalized medical diagnosis in preventing CVDs.

Flexible piezoelectric materials and strain sensors for wearable electronics and artificial intelligence applications

With the rapid development of artificial intelligence, the applications of flexible piezoelectric sensors in health monitoring and human-machine interaction have attracted increasing attention. Recent advances in flexible materials and fabrication technologies have promoted practical applications of wearable devices, enabling their assembly in various forms such as ultra-thin films, electronic skins and electronic tattoos. These piezoelectric sensors meet the requirements of high integration, miniaturization and low power consumption, while simultaneously maintaining their unique sensing performance advantages. This review provides a comprehensive overview of cutting-edge research studies on enhanced wearable piezoelectric sensors. Promising piezoelectric polymer materials are highlighted, including polyvinylidene fluoride and conductive hydrogels. Material engineering strategies for improving sensitivity, cycle life, biocompatibility, and processability are summarized and discussed focusing on filler doping, fabrication techniques optimization, and microstructure engineering. Additionally, this review presents representative application cases of smart piezoelectric sensors in health monitoring and human-machine interaction. Finally, critical challenges and promising principles concerning advanced manufacture, biological safety and function integration are discussed to shed light on future directions in the field of piezoelectrics.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Piezoelectric Biomaterials Inspired by Nature for Applications in Biomedicine and Nanotechnology

Bioelectricity provides electrostimulation to regulate cell/tissue behaviors and functions. In the human body, bioelectricity can be generated in electromechanically responsive tissues and organs, as well as biomolecular building blocks that exhibit piezoelectricity, with a phenomenon known as the piezoelectric effect. Inspired by natural bio-piezoelectric phenomenon, efforts have been devoted to exploiting high-performance synthetic piezoelectric biomaterials, including molecular materials, polymeric materials, ceramic materials, and composite materials. Notably, piezoelectric biomaterials polarize under mechanical strain and generate electrical potentials, which can be used to fabricate electronic devices. Herein, a review article is proposed to summarize the design and research progress of piezoelectric biomaterials and devices toward bionanotechnology. First, the functions of bioelectricity in regulating human electrophysiological activity from cellular to tissue level are introduced. Next, recent advances as well as structure-property relationship of various natural and synthetic piezoelectric biomaterials are provided in detail. In the following part, the applications of piezoelectric biomaterials in tissue engineering, drug delivery, biosensing, energy harvesting, and catalysis are systematically classified and discussed. Finally, the challenges and future prospects of piezoelectric biomaterials are presented. It is believed that this review will provide inspiration for the design and development of innovative piezoelectric biomaterials in the fields of biomedicine and nanotechnology.

Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review

Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending hospital stays and escalating healthcare costs. Given that this disorder is frequently underestimated in vulnerable populations, there is an urgent need for enhanced diagnostic and therapeutic strategies. Traditional diagnostic tools such as the videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES) require interpretation by clinical experts and may lead to complications. In contrast, non-invasive sensors offer a more comfortable and convenient approach for assessing swallowing function. This review systematically examines recent advancements in non-invasive swallowing function detection devices, focusing on the validation of the device designs and their implementation in clinical practice. Moreover, this review discusses the swallowing process and the associated biomechanics, providing a theoretical foundation for the technologies discussed. It is hoped that this comprehensive overview will facilitate a paradigm shift in swallowing assessments, steering the development of technologies towards more accessible and accurate diagnostic tools, thereby improving patient care and treatment outcomes.

Ultra-Broadband Flexible Thin-Film Sensor for Sound Monitoring and Ultrasonic Diagnosis

Small-scale and flexible acoustic probes are more desirable for exquisite objects like human bodies and complex-shaped components than conventional rigid ones. Herein, a thin-film flexible acoustic sensor (FA-TES) that can detect ultra-broadband acoustic signals in multiple applications is proposed. The device consists of two thin copper-coated polyvinyl chloride films, which are stimulated by acoustic waves and contact each other to generate the triboelectric signal. Interlocking nanocolumn arrays fabricated on the friction surfaces are regarded as a highly adaptive spacer enabling this device to respond to ultra-broadband acoustic signals (100 Hz-4 MHz) and enhance sensor sensitivity for film weak vibration. Benefiting from the characteristics of high shape adaptability and ultrawide response range, the FA-TES can precisely sense human physiological sounds and voice (≤10 kHz) for laryngeal health monitoring and interaction in real-time. Moreover, the FA-TES flexibly arranged on a 3D-printed vertebra model can effectively and accurately diagnose the inner defect by ultrasonic testing (≥1 MHz). It envisions that this work can provide new ideas for flexible acoustic sensor designs and optimize real-time acoustic detections of human bodies and complex components.

Neural Mechanisms of Neuro-Rehabilitation Using Transcranial Direct Current Stimulation (tDCS) over the Front-Polar Area

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation (NIBS) technique that applies a weak current to the scalp to modulate neuronal excitability by stimulating the cerebral cortex. The technique can produce either somatic depolarization (anodal stimulation) or somatic hyperpolarization (cathodal stimulation), based on the polarity of the current used by noninvasively stimulating the cerebral cortex with a weak current from the scalp, making it a NIBS technique that can modulate neuronal excitability. Thus, tDCS has emerged as a hopeful clinical neuro-rehabilitation treatment strategy. This method has a broad range of potential uses in rehabilitation medicine for neurodegenerative diseases, including Parkinson's disease (PD). The present paper reviews the efficacy of tDCS over the front-polar area (FPA) in healthy subjects, as well as patients with PD, where tDCS is mainly applied to the primary motor cortex (M1 area). Multiple evidence lines indicate that the FPA plays a part in motor learning. Furthermore, recent studies have reported that tDCS applied over the FPA can improve motor functions in both healthy adults and PD patients. We argue that the application of tDCS to the FPA promotes motor skill learning through its effects on the M1 area and midbrain dopamine neurons. Additionally, we will review other unique outcomes of tDCS over the FPA, such as effects on persistence and motivation, and discuss their underlying neural mechanisms. These findings support the claim that the FPA could emerge as a new key brain region for tDCS in neuro-rehabilitation.

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.

Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors.

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.