Internet Of Medical Things Systems Research Articles

Secure cross‐chain transactions for medical data sharing in blockchain‐based Internet of Medical Things

SummaryBlockchain technology has greatly promoted the development of the Internet of Medical Things (IoMT) from a centralized form to a distributed form. However, with many independent blockchain‐based IoMT (BIoMT) systems being established by different institutions, these heterogeneous blockchain systems have created a new “data islands” problem. This paper proposes a multi‐chain fusion (MCF) multi‐party governance model based on relay chain technology. The MCF model helps establish cross‐chain interaction and communication among the multiple heterogeneous blockchain systems in different medical institutions through the cross‐chain gateway. Meanwhile, by utilizing the inter‐blockchain transfer protocol (IBTP), the MCF model takes responsibility for legitimacy verification and reliable routing of cross‐chain transactions, thus addressing the “data islands problem” inherent in traditional blockchain‐based medical systems. Then, an elliptic curve signature scheme is adopted to protect user privacy in cross‐chain transactions, which helps establish transaction verification for the MCF model in BIoMT systems. Finally, several experiments are carried out to prove the feasibility of the cross‐chain scheme and the security of the elliptic curve signature scheme, and the performance index of the cross‐chain system is evaluated. The performance results show that the MCF model can provide secure and efficient cross‐chain services for sharing medical data among heterogeneous BIoMT systems, thus addressing the “data islands problem” inherent in traditional blockchain‐based medical systems.

Internet of Medical Things (IoMT) optimization for healthcare: A deep learning-based interference avoidance model

The Internet of Medical Things (IoMT) is a significant component of the broader Internet of Things (IoT), IoMT is responsible for monitoring, collecting, and transmitting critical medical data to central medical computer centers. IoMT faces significant challenges, including energy efficiency, interference from other devices in the spectrum, latency, and security and privacy concerns. As a result, in hospital settings, the development of a precise and dependable IoMT system is crucial. Consequently, this investigation offered an innovative, accurate, and dependable IoMT method. The suggested model intends to reduce or eliminate interference caused by other transmitting devices sharing the same IoMT spectrum within hospitals or medical institutions. It also presents an interference-avoidance distributed deep learning model for IoMT to medical receptionists. This model uses data from the Lagrange optimization technique to determine the ideal distance between interfering devices and medical receptions. As a result, the required system signal-to-interference-plus-noise (SINRth) is met while maintaining the highest energy efficiency (EE) and system achievable data rate (R). The proposed analytical model and deep learning model demonstrate the efficacy of the proposed approach by achieving the required system signal-to-interference-plus-noise (SINRth) with the best energy efficiency (EE) and system achievable data rate (R) while suppressing interference to any medical receptions.

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

Attribute-based key management for patient-centric and trusted data access in blockchain-enabled IoMT

Internet of Medical Things (IoMT) provides real-time data collection and access services to medical entities by connecting their devices to Internet. However, within such a complex architecture, achieving patient-centric data access still presents trustworthiness challenges of transmission security and usage reliability. Ciphertext-policy attribute-based encryption (CP-ABE) and blockchain are both the promising primitives that support patients with secure access control over their data. Nevertheless, both have their unique key management issues. In this paper, we propose a novel patient-centric and trusted data access (PCT-DA) system in blockchain-enabled IoMT. Firstly, we develop an online/offline encryption and verifiable outsourced decryption CP-ABE scheme to relieve the computation pressure on patients and users. Secondly, we design a flexible attribute-based key management mechanism to overcome the security drawbacks of encryption and attribute keys, such as single point of failure, key escrow and privacy leakage. Finally, we build a trusted integrity audit contract to guarantee the incentive fairness between patients and users in access services. Moreover, theoretical analyses prove that the PCT-DA system not only achieves confidentiality, integrity, traceability and forward/backward security, but also guarantees static security under random oracles. Simulation experiments indicate that our proposal reduces the time cost by approximately 14.10 seconds over state-of-the-art and is more practical in blockchain-enabled IoMT system.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

Quantum-based privacy-preserving techniques for secure and trustworthy internet of medical things an extensive analysis

The advent of Quantum Key Distribution (QKD) and quantum-based privacy-preserving techniques has ushered in a new era for securing communication channels within the Internet of Medical Things (IoMT) systems. We examine the fundamentals, uses, and ramifications of quantum cryptography in relation to healthcare data security in this thorough investigation. The journey commences with an in-depth overview of quantum cryptography, unraveling the concepts of superposition, entanglement, and quantum gates that form the bedrock of quantum computing. As we proceed, we investigate the uses of quantum cryptography, highlighting its contribution to resolving the particular issues brought about by the data-intensive and networked character of IoMT systems. The relevance of QKD in ensuring secure communication within IoMT is meticulously dissected, with case studies and experiments demonstrating the practicality and effectiveness of quantum-based privacy-preserving techniques. From telemedicine networks to wearable health devices, each case study offers valuable insights into the application of quantum-safe cryptography, showcasing its ability to fortify data integrity and confidentiality. A meticulous survey of existing research, coupled with an analysis of advancements in quantum cryptography, provides a panoramic view of the current landscape. From hardware limitations to distance constraints, the study navigates through challenges and breakthroughs, offering a roadmap for the future integration of quantum technologies into IoMT systems.Comparing quantum-based approaches with classical cryptography unveils a nuanced landscape of computational complexity, key distribution methodologies, and real-time encryption considerations. This comparative analysis serves as a guide for healthcare practitioners and technologists in making informed decisions regarding the adoption of quantum-based privacy-preserving techniques in IoMT environments. The case studies and experiments collectively paint a picture of the practicality and promise of quantum-based privacy-preserving techniques in IoMT scenarios. Anticipating future advancements, the exploration extends into quantum hardware improvements, standardization efforts, and the integration of quantum technologies with emerging trends like edge computing and blockchain. As the healthcare industry stands on the cusp of a quantum revolution, the comprehensive insights provided herein offer a foundation for understanding, implementing, and shaping the future of secure healthcare communication through the lens of quantum cryptography.

A trust-centric approach to intrusion detection in edge networks for medical internet of thing Ecosystems

The Internet of Medical Things (IoMT) promises transformative benefits for patient care and remote monitoring but raises significant security and privacy concerns. Ensuring the trustworthiness and security of IoMT systems at the edge is a critical challenge. This research paper presents a novel framework to address the security and trust issues specific to IoMT environments. The primary objective of this framework is to establish a robust defense against intrusion attempts and to instill trust in edge networks, securing healthcare data and medical devices. To achieve this, the paper introduces an advanced Intrusion Detection System (IDS) optimized for edge networks in the context of IoMT. This paper emphasizes developing an evaluation methodology for appraising the trustworthiness of individual network components, developing an analytical technique for scrutinizing the participation of network entities in ongoing communications, establishing a means to quantify the trust level at the network's periphery or edge, and constructing an energy-efficient trust-based framework for the identification and mitigation of malicious entities operating within the IoMT. Through comprehensive testing, including real-world simulation scenarios, the paper evaluates the performance and effectiveness of the proposed framework, validating its ability to safeguard sensitive healthcare information and support the secure operation of IoMT devices.

A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.

Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.

Combining Riemann–Lebesgue-Based Key Generator and Machine-Learning-Based Intelligent Encryption Scheme for IoMT Images Infosecurity

Imaging examinations, such as chest X-ray (CXR) images, can be used for early detection of diseases inside the human body, such as lung cancer, and cardiopulmonary-related diseases; however, they may contain patients’ private information, which raises concerns about the security levels of medical images in internet of medical things (IoMT) systems. The promising authorization protocol algorithm in 5th-generation networking technology can protect medical image confidentiality, recoverability, and availability. Hence, we propose a symmetric cryptography protocol based on the Riemann-Lebesgue (R-L) -based key generator and a machine learning (ML)-based cryptography scheme for CXR image infosecurity. The R-L-based key generator uses the composite function“, g(x)sin((1/2+N)x)”, to dynamically produce the oscillation function by controlling the terms of the nonlinear function“, g(x)”, and frequency“, N”, which are used to produce the randomnumber seed and to select non-ordered numbers and nonrepeated 256 secret keys in the key space. The ML-based scheme uses these random secret keys to train an image encryptor and decryptor, respectively. Based on the diffusion method, the ML-based cryptography scheme is used to change image pixel values. For evaluation experiments, CXR images are collected from the National Institutes of Health Chest X-ray database and used to evaluate image quality after encryption and decryption processes. For different cardiopulmonary-related disease images, the number of pixel change rates (NPCR) and unified averaged changed intensity (UACI) are used to evaluate the security level and decryption quality. The experimental results show an average NPCR of 99.6% and average UACI of 32.61% between plain and cipher images for evaluating the security level, enabling further online diagnosis applications in IoMT systems.

Edge Server Deployment for Health Monitoring With Reinforcement Learning in Internet of Medical Things

The Internet of Medical Things (IoMT) has recently gained a lot of interest in the health care industry. IoMT enables real-time and omnipresent monitoring of a patient's health status, resulting in massive amounts of medical data being generated. The centralized massive data processing places enormous strain on the typical cloud computing, rendering it incapable of supporting a variety of real-time health care applications. Therefore, edge computing that moves application programs and data processing from central infrastructure to the edge nodes has attracted wide attention. However, adopting existing edge server (ES) deployment strategies for IoMT is not suitable due to the decentralized and high real-time service requirements of IoMT systems. In particular, traditional ES deployment strategies in IoMT system confront major load imbalance across ESs, latency issues, and energy consumption concerns. To address these challenges, a deployment strategy of ESs based on the state-action-reward-state-action (SARSA) learning, named ESL, is designed. Specifically, ESs are quantified by evaluating the silhouette coefficient (SC) and the sum of squared errors. Then, through fuzzy C-means (FCM) algorithm, the preliminary division of health monitoring units (HMUs) and the initial locations of ESs are obtained. Finally, SARSA learning is adopted to determine the deployment of ESs. Furthermore, extensive experiments and analyses confirm that ESL achieves the core objective of optimizing load balancing among ESs while also optimizing request-response latency and request processing energy consumption.

Deep Learning-Assisted Security and Privacy Provisioning in the Internet of Medical Things Systems: A Survey on Recent Advances

Deep Learning-Assisted Security and Privacy Provisioning in the Internet of Medical Things Systems: A Survey on Recent Advances

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). However, challenges arise when dealing with sensitive data due to the dependence on large datasets. To address this issue, we propose an approach that combines different magnification factors of histopathological images using a residual network and information fusion in Federated Learning (FL). FL is employed to preserve the privacy of patient data, while enabling the creation of a global model. Using the BreakHis dataset, we compare the performance of FL with centralized learning (CL). We also performed visualizations for explainable AI. The final models obtained become available for deployment on internal IoMT systems in healthcare institutions for timely diagnosis and treatment. Our results demonstrate that the proposed approach outperforms existing works in the literature on multiple metrics.

A Comprehensive Privacy-Preserving Federated Learning Scheme with Secure Authentication and Aggregation for Internet of Medical Things.

Data mining, integration, and utilization are the inevitable trend of the Internet of Medical Things (IoMT) in the context of big data. With the increasing demand for data privacy, federated learning has emerged as a new paradigm, which enables distributed joint training of medical data sources without leaving the private domain. However, federated learning is suffering from security threats as the shared local model will reveal original datasets. Privacy leakage is even more fatal in healthcare because medical data contains critically sensitive information. In addition, open wireless channels are susceptible to malicious attacks. To further safeguard the privacy of IoMT, we propose a comprehensive privacy-preserving federated learning scheme with a tactful dropout handling mechanism. The proposed scheme leverages blind masking and certificateless proxy re-encryption (CL-PRE) for secure aggregation, ensuring the confidentiality of the local model and rendering the global model invisible to any parties other than clients. It also provides authentication of uploaded models while protecting identity privacy. Compared with other relevant schemes, our solution has better performance on functional features and efficiency, and is more applicable to IoMT systems with many devices.

BIDS: Blockchain and Intrusion Detection System Coalition for Securing Internet of Medical Things Networks.

The benefits of the Internet of Medical Things (IoMT) in providing seamless healthcare to the world are at the forefront of technological advancement. However, security concerns of any IoMT systems are high since they threaten to compromise personal information of patients and can even cause health hazards. Researchers are exploring the use of various techniques to ensure a high level of security of IoMT systems. One key concern is that the computing power of any Internet of Things (IoT) device is relatively low, hence mechanisms that require low computational power are appropriate for designing Intrusion Detection Systems (IDS). In this research work, a blockchain IDS coalition is proposed for securing IoMT networks and devices. The blockchain ledger is compact and uses less processing resources. Additionally, the ledger requires less communication overhead. The cryptographic hashes in the suggested architecture ensure complete data secrecy and integrity between parties who are trusted and those who are untrustworthy. Peer-to-peer networks in both central and cluster networks are also included in this work for complete decentralization. The proposed model can counter various attacks, including Denial of Service (DoS), anonymity attacks, impersonation attacks, Man-In-The-Middle (MITM), and Cross-Site Scripting (XSS). The proposed method achieved an F1- score as high as 100% and reported an AUC value of over 99%.

Federated Learning and Blockchain-Enabled Privacy-Preserving Healthcare 5.0 System: A Comprehensive Approach to Fraud Prevention and Security in IoMT

The proliferation of Internet and Communication Technologies (ICTs) has ushered in a period often referred to as Industry 5.0. The subsequent development is accompanied by the healthcare industry coining Healthcare 5.0. Healthcare 5.0 incorporates the Internet of Things (IoT), enabling medical imaging technologies to facilitate early diagnosis of diseases and enhance the quality of healthcare facilities' service. Nevertheless, the healthcare sector is currently experiencing a delay in adopting Artificial Intelligence (AI) and big data technologies compared to other sectors under the umbrella of Industry 5.0. This delay may be attributed to the prevailing concerns about data privacy within the healthcare domain. In recent times, there has been a noticeable increase in the use of Machine Learning (ML) enabled adaptive Internet of Medical Things (IoMT) systems with different technologies for medical applications. ML is an essential component of the IoMT system, as it optimizes the trade-off between delay and energy consumption. The issue of data fraud in classical learning models inside the distributed IoMT system for medical applications remains a significant research challenge in practical settings. This paper proposes Federated Learning and Blockchain-Enabled Privacy-Preserving (FL-BEPP) for Fraud Prevention and Security (FPS) in the IoMT framework. The system incorporates numerous dynamic strategies. This research examines the medical applications that exhibit hard constraints, such as deadlines, and soft constraints, such as resource consumption, when executed on distributed fog and cloud nodes. The primary objective of FL-BEPP is to effectively detect and safeguard the confidentiality and integrity of data across many tiers, including local fog nodes and faraway clouds. This is achieved by minimizing power use and delay while simultaneously meeting the time constraints associated with healthcare workloads.

Hybrid deep learning model for wearable sensor‐based stress recognition for Internet of Medical Things (IoMT) system

SummaryThere are numerous, interrelated, and multi‐dimensional aspects that influence a person's mental health, one of them being stress. Smart wearable technology having physiological and motion sensors has paved the way for real‐time data collection to deliver cutting‐edge information about the stress of individuals. It is now possible to build an Internet of Medical Things (IoMT) system that can recognise the user's stress, revealing the elements that cause stress. However, there are significant gaps in the existing system for stress recognition. To begin with, stress recognition is primarily studied for a specific set of people, such as occupational stress among office working persons or hospital staff during emergency duties, and so forth. Second, most past work on stress recognition has focused on extracting handcrafted traits, which necessitates human interaction and skill. To overcome above mentioned challenges, this work proposes a novel IoMT framework for continuous stress recognition for mental well‐being. This paper presents a hybrid deep learning (DL) approach for automatically retrieving features and classification into various stress states for the IoMT system to address these difficulties. The proposed expanded system gathers data from wearable physiological sensors and feeds it into a convolutional neural network–long short‐term memory (CNN‐LSTM), a hybrid DL classifier. The suggested approach has been tested on the wearable stress and affect detection (WESAD) dataset. It has a reported accuracy of 90.45%, which is higher than previously reported accuracies from existing machine learning (ML) and DL approaches.

Consumer-Centric Internet of Medical Things for Cyborg Applications Based on Federated Reinforcement Learning

The Internet of Medical Things (IoMT) is the new digital healthcare application paradigm that offers many healthcare services to users. IoMT-based emerging healthcare applications such as cyborgs, the combination of advanced artificial intelligence (AI) robots, and doctors performing surgical operations remotely from hospitals to patients in their homes. For instance, robot-based knee replacement procedures, and thigh medical care real-time performance monitoring systems are cyborg applications. The paper introduces the multi-agent federated reinforcement learning policy (MFRLP) indicated in mobile and fog agents based on the socket remote procedure call (RPC) paradigm. The goal is to design a consumer-centric cyborg-efficient training testing system that executes the overall application mechanism with minimum delays in the IoMT system. The study develops the RPC based on reinforcement learning and federated learning that adopts dynamic changes in the environment for cyborg applications. As a result, MFRLP minimized the training and testing in the mobile and fog environments by 50%, local processing time by 40%, and processing time by 50% compared to existing machine learning (ML) methods for cyborg applications. The code is publicly available at https://github.com/prayagtiwari/CIoMT

A Policy-Hiding Attribute-Based Access Control Scheme in Decentralized Trust Management

Internet of Medical Things (IoMT) technologies significantly improve the quality of health care, especially at the time when COVID-19 is becoming a worldwide pandemic. Due to the complexity of devices and user nodes in the IoMT system, there should be some ways to ensure the security and quality of the service or information. Decentralized trust management techniques are efficient means of promoting application security and reliability in these cases. However, the majority of currently utilized access control schemes cannot be applied in decentralized trust management systems or perform poorly owing to the numerous restrictions of decentralized systems. In this paper, we present a policy-hiding and multi-authority key generation CP-ABE scheme (PM-CPABE) for decentralized trust management systems, which could provide fine-grained access control capabilities. Meanwhile, the proposed scheme does not require any fully trusted entity, thus it can be well adapted to decentralized trust management systems. The scheme also implements policy hiding to protect user privacy. In addition, it supports large universe and outsourced decryption. The security analyses and performance comparisons give evidence of our scheme is security and efficient.

A scalable and lightweight group authentication framework for Internet of Medical Things using integrated blockchain and fog computing

The Internet of Medical Things (IoMT) is the network of medical devices, software applications, and healthcare information systems used for remote monitoring and delivery of healthcare services. Despite the several advantages of IoMT for today smart healthcare, security issues are growing due to the inadequate computation, limited storage and insufficient self-protection capabilities of IoMT devices. Authentication of IoMT devices is the main requirement to secure IoMT systems. Although, the recent authentication schemes based on tamper-proof decentralized architecture of blockchain technology are robust and enjoy a high level of security, yet they require high computation, more storage, and long authentication time. These issues lead to reduced scalability and time efficiency, which are necessary for large-scale, time-sensitive IoMT systems. To this end, this paper proposes a novel group authentication framework for IoMT Systems. The group authentication scheme is implemented through a four-phase process, including setup, registration, secret construction, and authentication. To enhance both efficiency and scalability, the proposed group authentication framework employs a combination of elliptic curve cryptography (ECC), Shamir’s secret sharing (SSS) algorithm, and blockchain-based fog computing technologies. We simulated the proposed framework through the Ethereum platform and Solidity language and its performance is evaluated using the Hyperledger Caliper tool. The simulation experiments of the proposed framework showed that the average latency of authenticating IoMT devices was 0.5 second and the throughput was 400 transactions per second. Our analysis of the proposed framework’s performance against other cutting-edge blockchain-based authentication techniques showed that it outperformed them in terms of latency and throughput. A security analysis of the proposed framework was conducted using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal and informal security analysis demonstrated that the proposed framework is secure and resistant to potential authentication-related attacks. We also noted that the average latency of the proposed framework maintains a fairly narrow range when the number of submitted transactions rises, indicating that it supports the scalability of the IoMT system.

A health monitoring system based on flexible triboelectric sensors for intelligence medical internet of things and its applications in virtual reality

The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications, enabling the realization of precision medicine, intelligent healthcare, and telemedicine in the era of digitalization and intelligence. However, the IoMT faces various challenges, including sustainable power supply, human adaptability of sensors and the intelligence of sensors. In this study, we designed a robust and intelligent IoMT system through the synergistic integration of flexible wearable triboelectric sensors and deep learning-assisted data analytics. We embedded four triboelectric sensors into a wristband to detect and analyze limb movements in patients suffering from Parkinson's Disease (PD). By further integrating deep learning-assisted data analytics, we actualized an intelligent healthcare monitoring system for the surveillance and interaction of PD patients, which includes location/trajectory tracking, heart monitoring and identity recognition. This innovative approach enabled us to accurately capture and scrutinize the subtle movements and fine motor of PD patients, thus providing insightful feedback and comprehensive assessment of the patients’ conditions. This monitoring system is cost-effective, easily fabricated, highly sensitive, and intelligent, consequently underscores the immense potential of human body sensing technology in a Health 4.0 society.