Wearable Medical Devices Research Articles

Risk Assessment for Workers with Wearable Medical Devices Exposed to Electromagnetic Fields.

The exponential diffusion of wearable medical devices (WMD) in recent years has involved people of all ages, including workers. Workers who use WMDs should be considered at a particular risk from electromagnetic fields, and in accordance with EU Directive 2013/35/EU, they require an individual risk assessment. Currently, there is no international standard that provides specific guidance on how to perform such a risk assessment. This paper focuses on the effects of electromagnetic fields on WMDs and does not consider the direct effects on human body tissues. It aims to offer practical recommendations to employers and/or health physicists for the risk assessment of workers with WMDs. Focusing on EU countries, we first describe the requirements outlined by the technical standard for the electromagnetic compatibility (EMC) of medical electrical equipment EN 60601-1-2. Then, some general guidelines on how to perform the risk assessment are provided. The assessment can be conducted by comparing the field values measured in the workplace with the immunity test levels specified in the technical standards of medical electrical equipment. If the measured values are lower than the immunity test levels indicated in the standard and the distance from the electromagnetic source is greater than the distance used by the manufacturer during the EMC (electromagnetic compatibility) tests (typically 30 cm), the risk for the worker may be considered acceptable. However, if the measured values exceed the immunity test levels or the distance criteria, a specific evaluation based on a case-by-case analysis is required.

Wearing Your Heart (Monitor) on Your Sleeve: Will Data Be the New Doctor?

Imagine wearing a shirt or a wristband that tells you how hard you are exercising or warns you if you are getting sick. These are simple examples of wearable medical devices that could transform healthcare—either by helping people to manage serious diseases that they already have, or by spotting the early signs of a disease so that it can be treated before the person even feels sick. This article will focus on two examples of wearables: one for the management of diabetes and another that may be able to prevent heart attacks. Wearables like these and many others that are being developed could greatly improve human health. However, these technologies come with some big challenges, because they collect a lot of personal data. We need to find ways to protect people’s privacy by keeping that sensitive data safe. In the future, wearables will certainly help doctors do their jobs… but might they even replace doctors altogether?

An Optical Signal Simulator for the Characterization of Photoplethysmographic Devices.

(1) Background: An optical simulator able to provide a repeatable signal with desired characteristics as an input to a photoplethysmographic (PPG) device is presented in order to compare the performance of different PPG devices and also to test the devices with PPG signals available in online databases. (2) Methods: The optical simulator consists of an electronic board containing a photodiode and LEDs at different wavelengths in order to simulate light reflected by the body; the PPG signal taken from the chosen database is reproduced by the electronic board, and the board is used to test a wearable PPG medical device in the form of earbuds. (3) Results: The PPG device response to different average and peak-to-peak signal amplitudes is shown in order to assess the device sensitivity, and the fidelity in tracking the actual heart rate is also investigated. (4) Conclusions: The developed optical simulator promises to be an affordable, flexible, and reliable solution to test PPG devices in the lab, allowing the testing of their actual performances thanks to the possibility of using PPG databases, thus gaining useful and significant information before on-the-field clinical trials.

An IoT-Based Novel Hybrid Seizure Detection Approach for Epileptic Monitoring

This research focuses on a new Electroencephalogram (EEG)-based system for the early detection of epileptic episodes that is made possible by the Internet of Things (IoT). The system is made up of two important units, namely, 1) the multichannel EEG recording unit; and 2) the seizure detection unit. Since epileptic information is more useful when included in multichannel EEG data from many brain regions, the primary goals of this work are: designing and developing the seizure detection unit, making use of Spike-Statistical (SS)- Flower Pollination Algorithm (FPA) based Critical Spectral Verge (CSV) derived features termed as SS-CSV; and employing the convolutional neural network (CNN) method in an IoT-enabled EEG monitoring system to detect EEG seizures. The presented system performed better, with an average accuracy of 98.48% with the CNN classifier. Neuroexperts will find this approach very useful to analyse seizure information, especially in wearable medical devices.

PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber-Physical Systems.

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber-physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient's Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle's Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient's gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient's BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time.

Solvent-Resistant Adhesive Gel with Thermal Post-Tunability.

Adhesives have received extensive attention in flexible bioelectronics, wearable electronic medical devices, and biofuel cells. However, it is a challenge to achieve late regulation of performance once polymer-based gels are formed. Here, a double-network organogel composed of a hydrophilic and hydrophobic polymer network and a polyamide acid network was successfully prepared. In diverse liquid environments (including isopropyl alcohol, glycerol, epichlorohydrin, n-propanol, dichloromethane, triethanolamine, ethanol absolute, hydrogen peroxide, and ethyl acetate), the organogel adhesive demonstrated remarkable properties. It exhibits a strong tensile strength of 200 kPa, a high fracture strain reaching 560%, and an impressive adhesion strength of 38 kPa. In addition, the organogel demonstrates exceptional adhesive properties toward polytetrafluoroethylene, plastics, metals, rubber, and glass. Note that the organogel could also regulate adhesive and tough performance by thermally triggering a cyclization reaction even after the organogel has been formed. The strategy provides a new idea for designing soft materials with post-tunability.

A Systematic Analysis of Wearable Devices as Brain-Computer Interface Based on Neuromodulation

As the global health landscape deteriorates, the imperative of ensuring our well-being becomes increasingly apparent. The evolution of integrated circuit miniaturization technology has propelled the advancement of brain-computer interfaces to the forefront of research. Breakthroughs in recording and stimulating deep brain activity in humans have paved the way for a groundbreaking healthcare delivery method, referred to as the "Neural Stack" – a wearable medical device leveraging neuromodulation. This innovation contributes significantly to both the development of brain-computer technology and the broader field of neuroscience. In this article, the author delve into recent milestones within the realm of brain-computer interfaces, dissecting the associated challenges and envisioning future developments and applications. Given the interdisciplinary nature of brain-computer technology, contemporary scholars in the new era are compelled to explore and comprehend its intricacies. We are on the brink of an era where all things are interconnected, with brain-computer interfaces serving as the conduit for information exchange.

Validation of a Wearable Medical Device for Automatic Diagnosis of OSA against Standard PSG.

The objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis. This observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022. A total of 80 patients were recruited for the trial. The patients had a median Epworth scoring of 10, a mean of 10.4, and a range of 0-24. The mean AHI obtained with PSG plus sleep clinician marking was 23.2, median 14.3 and range 0-108. The study demonstrated a diagnostic accuracy (based on AHI) of 95.24%, sensitivity of 92.86%, specificity of 97.14%, positive predictive value of 96.30%, negative predictive value of 94.44%, positive likelihood ratio of 32.50 and negative likelihood ratio of 0.07. The AcuPebble SA100 (EU) device has demonstrated an accurate automated diagnosis of OSA in patients undergoing in-clinic sleep testing when compared against the gold-standard reference of in-clinic PSG.

An area efficient inductorless super-regenerative receiver front-end for biomedical implant devices

Wearable and implantable medical devices are of great importance in diagnosis and treatment as they provide continuous monitoring and data collection. Considering the comfort of the patient and ease-of-operation, these devices require wireless data transmission. In this work, a novel area and energy efficient inductorless super-regenerative receiver (SRR) is proposed for medical implants dedicated to patient monitoring and treatment. The proposed SRR was fabricated in UMC CMOS 180 nm technology. The SRR occupies only 0.072mm2 which corresponds to an order of magnitude area reduction compared to the state-of-the-art. The SRR achieves −70dBm sensitivity and the highest data rate is 4Mbps. 3.22nJ/b energy efficiency is obtained with 12.9mW power consumption from a 1.8V power supply.

Flexible Artificial Mechanoreceptor Based on Microwave Annealed Morphotropic Phase Boundary of HfxZr1‐xO2 Thin Film

AbstractThe development of artificial tactile receptor systems is important in the fields of prosthetic devices, interfaces for the metaverse, and sensors. A pressure sensor and memory device may be used in this system to replicate the tactile detecting capabilities of human skin. The implementation of systems that take into account mass production and miniaturization is still difficult. Here, a flexible artificial tactile receptor built using conventional semiconductor processes that combine a vertically stacked piezoelectric sensor with neuromorphic memory is presented. As a fundamental component for both sensors and memory, hafnium zirconium oxide (HZO) formed by using semiconductor deposition technique is introduced. Due to its exceptional piezoelectric performance, the morphotropic phase boundary of HZO is studied. The entire materials and processes are highly compatible with conventional semiconductor processes, including microwave annealing‐based low‐temperature crystallization. Even after 10,000 times of bending stress, the sensor and memory constructed on a flexible substrate exhibit consistent pressure detection characteristics over a wide range of 2–25 kPa. The feasibility of the approach is further demonstrated by a deep neural network simulation, which reached 90.8% braille recognition accuracy. Wearable electronics and medical devices are two examples of industrial domains that can use these flexible, exceptionally durable devices.

Securing Pacemakers using Runtime Monitors over Physiological Signals

Wearable and implantable medical devices (IMDs) are increasingly deployed to diagnose, monitor, and provide therapy for critical medical conditions. Such medical devices are safety-critical cyber-physical systems (CPSs). These systems support wireless features introducing potential security vulnerabilities. Although these devices undergo rigorous safety certification processes, runtime security attacks are inevitable. Based on published literature, IMDs such as pacemakers and insulin infusion systems can be remotely controlled to inject deadly electric shocks and excess insulin, posing a threat to a patient’s life. While prior works based on formal methods have been proposed to detect potential attack vectors using different forms of static analysis, these have limitations in preventing attacks at runtime. This paper discusses a formal framework for detecting cyber-physical attacks on a pacemaker by monitoring its security policies at runtime. We propose a wearable device that senses the Electrocardiogram (ECG) and Photoplethysmogram (PPG) of the body to detect attacks in a pacemaker. To facilitate the design of this device, we map the security policies of a pacemaker w.r.t ECG and PPG, paving the way for designing formal verification monitors for pacemakers for the first time using multiple physiological signals. The proposed monitoring framework allows the synthesis of parallel monitors from a given set of desired security policies, where all the monitors execute concurrently and generate an alarm to the user in the case of policy violation. Our implementation and the performance evaluation results demonstrate the technical feasibility of designing such a wearable device for attack detection in pacemakers. This device is separate from the pacemaker, ensuring no need for re-certification of pacemakers. Our approach is amenable to the application of security patches when new attack vectors are detected, making the approach ideal for runtime monitoring of medical CPSs.

Stretchable, flexible, and breathable MXene/dopamine/thermoplastic polyurethane nanofiber membrane with outstanding strain sensing and electromagnetic interference shielding performances

Outstanding strain sensing and electromagnetic interference (EMI) shielding performances are two important indicators for high-performance nanofiber membrane in flexible wearable electronic device. Here, simple electrospinning was employed to prepare the nanofiber membrane of thermoplastic polyurethane (TPU) as substrate. Then high-performance nanofiber membrane was developed by synthesizing dopamine on TPU nanofibers to form poly-dopamine (PDA) coatings (named PT) and ultrasonically loading MXene on PT (labeled MPT). Specifically, TPU substrate provides flexibility and breathability, PDA coating improves the adhesion between MXene and TPU, and MXene contributes to strain sensing and EMI shielding performances. Owing to the PDA coatings, MPT nanofiber membrane has outstanding EMI shielding efficiency (EMI SE) of 69.86 dB at 150 μm thickness and an absolute effective shielding value of 8383.33 dBcm2g−1. The combination of PDA with MXene improves the EMI SE of the MPT membrane by ∼ 1.6 times compared to other TPU-based membrane. MPT strain sensor exhibits great sensing performance with a remarkable detection threshold of 0.1%, strain coefficient of up to 2600, broad working range of 200%, and rapid response/recovery time (77/186 ms). Meanwhile, MPT strain sensor also shows remarkable ability to real-time monitor human health/motion, such as joint bending, breathing, speaking and heart rate, which makes significant contributions in the field of smart medicine. This nanofiber membrane can provide a universal strategy for the development of intelligent wearable medical devices.

An Enhanced Autoencoder-based Anomaly Detection Model for time series Data from Wearable Medical Devices.

In today's era of rapidly advancing technologies, such as sensors and the Internet of Things (IoT), and the increasing focus on promoting healthy lifestyles, smart wearable devices play a crucial role in real-time detection and diagnosis of physical health conditions. Through analyzing the multi-featured time series data captured by these devices with multiple sensors, we can uncover hidden diseases and provide timely treatment. Therefore, it is imperative to study an anomaly detection model with robust feature learning and anomaly diagnosis capabilities. To address this need, this paper proposes an enhanced autoencoder-based anomaly detection model for time series data obtained from wearable medical devices. Initially, the model utilizes a convolutional neural network to learn the correlations between multiple features. Subsequently, a long and short-term memory network is employed to capture the sequence correlations, and an multi-head attention mechanism is used to mitigate the performance degradation caused by increasing the sequence length. The residual loss is also used to effectively mitigate the vanishing gradient problem. Finally, the model is evaluated using two widely recognized public datasets: the HeartDisease dataset, which contains information on patients with heart conditions, and the MIMIC dataset, a comprehensive database of de-identified health data related to critical care. The experimental results demonstrate that our model can achieve an accuracy of 95.37% and 95.56% on the two datasets, respectively. Compared to the best performing baseline methods, our model improves 8.6% and 12.3% on the two datasets, respectively. Overall, our model enables efficient analysis of sequential data, effectively captures long-term dependencies, and significantly improves the success rate of early health diagnosis for individuals.

An LSTM-based Neural Network Wearable System for Blood Glucose Prediction in People with Diabetes.

This article proposes the first hardware implemen-tation of a low-power LSTM neural network targeting a wearable medical device designed to predict blood glucose at a 30-minute horizon. This work aims to reduce energy consumption by propos-ing new activation functions that target hardware implementation. On top of this proposal, we also prove there is room for improve-ment in energy consumption by applying neural network optimiza-tions at the algorithmic, such as quantization, and architecture level, LSTM hyperparameters, that consider the target hardware. To validate our proposal, we devise an optimized version of the neural network aimed to be wearable and, therefore, to reduce its energy consumption while preserving its accuracy as much as possible. The hardware is implemented on a Xilinx Virtex-7 FPGA VC707 Evaluation Kit. It is compared with (i) a faithful design of the original neural network implemented on the same evaluation kit, (ii) three state-of-the-art LSTM-based FPGA implementations, and (iii) software implementations running in cutting-edge smartphones:OnePlus NordTM and an Apple iPhone 13 ProTM with artificial in-telligence hardware accelerators. Our proposal consumes between ×1020 and ×7 less energy than the software implementations, being the most efficient system compared to the smartphones. On the other hand, its energy efficiency, measured in GFLOP/J, is between ×2.84 and ×7.82 greater than other state-of-the-art LSTM implementations, proving to be the most suitable implementation for a wearable system for blood glucose prediction.

An Overview of Wearable Medical Device Applications

An Overview of Wearable Medical Device Applications

A multi-functional flexible piezoresistive sensor based on sericin-functionalized CNTs decorated carbon foams for wearable thermal-therapy devices

With the rapid development of science and technology, multifunctional wearable devices have become a major trend in medical field. There is an urgent need to fabricate multifunctional materials with thermal insulation, mechanical flexibility, piezoresistance, and adjustable heating behavior to cope with the thermal management and health monitoring of human body in the harsh environment. To address this issue, three-dimensional melamine-based carbon foams (CMF) have been developed, with sericin-functionalized carbon nanotubes (CNTs) and in situ-grown carbon nanofibers (CNFs) in the CMF skeleton. The strategy of hierarchical porous micro-nanostructure enhanced the synergistic effect between CNTs and CNFs, resulting in exceptional resilience at 0–80 % compressive strain and cyclic stability at 20 % strain. Such carbon foam composites demonstrated excellent piezoresistive sensing performance, wide pressure range (0–20 kPa), high sensitivity (S = 0.11 kPa−1), and quick response (response/recovery time of 150/120 ms). They also showed good thermal insulation and Joule heating properties, particularly in presenting strain-dependent Joule heating mode at low voltage. Such amazing characteristics provide inspiration for temperature regulation of flexible, energy-efficient heating devices, and make them promising for smart sportswear, human-machine interfaces, and wearable medical devices.

Highly sensitive skin-like wearable optical sensor for human physiological signals monitoring

Pusle signal is an important information related to the cardiovascular system. Long-term and accurate pulse monitoring is of great significance. Wearable fiber-optic sensors show great promise in this field. In this work, a highly sensitive wearable skin-like optical pressure sensor for human physiological signals monitoring is demonstrated by embedding bent fiber in a silica gel film. By employing small-core fiber (SCF) and fabric, the skin-like optical pressure sensor achieves high sensitivity (2.2 kPa−1), fast response (5 ms), low detection limit (4 Pa) and remarkable stability (over 40,000 cycles). As a proof of concept, monitoring of pulse from the most parts of human body and finger motion have been demonstrated based on the proposed sensor through the power variation of the output light. Moreover, abnormal pulse signals in unhealthy states are demonstrated. This novel photonics-based sensor provides a new basis for developing high-performance wearable medical devices.

Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.

Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.

Data-driven approach to quantify trust in medical devices using Bayesian networks.

Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.

Encrypt ECG signals based on the optimized 4-bit absolute value comparator

As people become more conscious of private information security and advancements in Electrocardiogram (ECG) identity identification technology, the need to encrypt private data intensifies. New wearable medical devices continuously gather vast amounts of personal health data, underscoring the urgency of safeguarding privacy. This paper focuses on encrypting and decrypting rich characteristic signals like ECG. It delves into the core aspects, including the logic circuit diagram of a four-bit digit absolute comparator using Complementary Metal Oxide Semiconductor (CMOS) technology and optimizing delay through logic effort to reduce power consumption. XOR gates and other gate circuits, as well as complement subtraction principles, are employed in building logic gates. Calculating delay involves selecting the longest critical path and determining the specific parameters (g, h, and p) for each logic gate. Subsequently, energy consumption is computed using a formula, revealing a trade-off between energy and delay. After optimization, the circuit's energy consumption is 184.832, and the delay is 34.13. In summary, the 4-bit absolute value detector plays a crucial role in encryption, addressing the growing concern for privacy and data security.