Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

Revolutionizing healthcare: A review on cutting-edge innovations in Raspberry Pi-powered health monitoring sensors

The integration of Raspberry Pi technology into health care is a significant advancement that has the potential to revolutionise the delivery of healthcare. This study highlights the inventive uses of Raspberry Pi devices, emphasizing their economical nature, mobility, and capacity to be customised for unique healthcare requirements. Healthcare practitioners may utilize the computational capabilities of Raspberry Pi to create portable monitoring devices that can gather, analyze, and send patient data in real-time. An important benefit of Raspberry Pi-based systems is their capability to facilitate remote patient monitoring, which allows for early diagnosis of diseases and personalized healthcare interventions. This capacity shows potential for people in situations with low resources, when typical monitoring methods may not be available or feasible. Moreover, the capacity of Raspberry Pi technology to easily adjust and be used by a wide range of people makes it a powerful tool for tackling many healthcare concerns. The article promotes the need for ongoing study and advancement in health monitoring systems that utilize Raspberry Pi technology. It emphasizes the need of collaboration among technology enthusiasts, healthcare practitioners, and researchers. By cultivating these collaborations, progress in healthcare solutions based on Raspberry Pi may be expedited, resulting in enhanced patient results and more efficient healthcare provision. The Raspberry Pi technology has the capacity to bring about significant changes in healthcare by effectively meeting the changing needs of current healthcare systems. Healthcare practitioners may optimize patient care, facilitate early intervention, and ultimately boost global health outcomes by utilizing the capabilities of Raspberry Pi devices.

Experimental evaluation of wearable LED strip and side-emitting fiber for optical camera communications systems

This paper presents an experimental evaluation of two types of light-emitting diode (LED)-based distributed transmitters, namely an LED strip and an LED-coupled side-emitting optical fiber, in both laboratory and wearable optical camera communication (OCC) systems. We study the system performance in terms of success of reception (SoR) with regard to the transmission distance. The best value of SoR is achieved when the camera is facing directly to the transmitter (Tx) from a close distance of 1 m. Additionally, we compare the power consumption, the signal-to-noise ratio performance (SNR) and all the obtained values under optimal conditions are better than the forward error correction (FEC) limit in OCC systems.

Optical Camera Communications in Healthcare: A Wearable LED Transmitter Evaluation during Indoor Physical Exercise

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise.

Choice of Piezoelectric Element over Accelerometer for an Energy-Autonomous Shoe-Based System

Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems-equipped with one or more sensors, either of the same or different types-capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject's overall movement, known as the human gait. Beyond sensing, these systems also provide a platform for hosting ambient energy harvesters. They hold the potential to harvest energy from foot movements and operate related low-power devices sustainably. This article proposes two types of strategies (Strategy 1 and Strategy 2) for an energy-autonomous shoe-based system. Strategy 1 uses an accelerometer as a sensor for gait acquisition, which reflects the classical choice. Strategy 2 uses a piezoelectric element for the same, which opens up a new perspective in its implementation. In both strategies, the piezoelectric elements are used to harvest energy from foot activities and operate the system. The article presents a fair comparison between both strategies in terms of power consumption, accuracy, and the extent to which piezoelectric energy harvesters can contribute to overall power management. Moreover, Strategy 2, which uses piezoelectric elements for simultaneous sensing and energy harvesting, is a power-optimized method for an energy-autonomous shoe system.

Flexible two-dimensional MXene-based antennas

With the growing development of the Internet of things, wearable electronic devices have been extensively applied in civilian and military fields. As an essential component of data transmission in wearable electronics, a flexible antenna is one of the key aspects of research. Conventional metal antennas suffer from a large skin depth, and cannot satisfy the requirements of wearable electronics such as light weight, flexibility, and thinness. Recently, a group of two-dimensional metallic metal carbides (named MXenes) have been explored as building blocks for high-performance flexible antennas with excellent flexibility and superior mechanical strength. The appearance of hydrophilic functional groups at the surface of a MXene allows simple, scalable, and environmentally friendly manufacturing of MXene-based antennas. In this minireview, some pioneering works of MXene-based flexible radio frequency components are summarized, and the existing bottlenecks and the future trends of this promising field are discussed.

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.

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

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.

A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte's response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance.

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.

A BLE-Connected Piezoresistive and Inertial Chest Band for Remote Monitoring of the Respiratory Activity by an Android Application: Hardware Design and Software Optimization

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated.

A Soft and Skin-Interfaced Smart Patch Based on Fiber Optics for Cardiorespiratory Monitoring

Wearables are valuable solutions for monitoring a variety of physiological parameters. Their application in cardiorespiratory monitoring may significantly impact global health problems and the economic burden related to cardiovascular and respiratory diseases. Here, we describe a soft biosensor capable of monitoring heart (HR) and respiratory (RR) rates simultaneously. We show that a skin-interfaced biosensor based on fiber optics (i.e., the smart patch) is capable of estimating HR and RR by detecting local ribcage strain caused by breathing and heart beating. The system addresses some of the main technical challenges that limit the wide-scale use of wearables, such as the simultaneous monitoring of HR and RR via single sensing modalities, their limited skin compliance, and low sensitivity. We demonstrate that the smart patch estimates HR and RR with high fidelity under different respiratory conditions and common daily body positions. We highlight the system potentiality of real-time cardiorespiratory monitoring in a broad range of home settings.

Assessment of Dual-Tree Complex Wavelet Transform to Improve SNR in Collaboration with Neuro-Fuzzy System for Heart-Sound Identification

The research paper proposes a novel denoising method to improve the outcome of heart-sound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.

An Energy-Autonomous Smart Shirt Employing Wearable Sensors for Users’ Safety and Protection in Hazardous Workplaces

Wearable devices represent a versatile technology in the IoT paradigm, enabling non-invasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where a WiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P¯ = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

The status and perspectives of nanostructured materials and fabrication processes for wearable piezoresistive sensors

The wearable sensors have attracted a growing interest in different markets, including health, fitness, gaming, and entertainment, due to their outstanding characteristics of convenience, simplicity, accuracy, speed, and competitive price. The development of different types of wearable sensors was only possible due to advances in smart nanostructured materials with properties to detect changes in temperature, touch, pressure, movement, and humidity. Among the various sensing nanomaterials used in wearable sensors, the piezoresistive type has been extensively investigated and their potential have been demonstrated for different applications. In this review article, the current status and challenges of nanomaterials and fabrication processes for wearable piezoresistive sensors are presented in three parts. The first part focuses on the different types of sensing nanomaterials, namely, zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) piezoresistive nanomaterials. Then, in second part, their fabrication processes and integration are discussed. Finally, the last part presents examples of wearable piezoresistive sensors and their applications.