Healthcare Monitoring Devices Research Articles

In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors

The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations.

Wearable Pendulum-Rotor-Separated Hybrid Generator for Smart Healthcare Monitoring.

Human biomechanical energy, with features of fluctuating amplitudes and low frequency, has been considered as a potential sustainable power source for wearable healthcare monitoring devices. Developing an effective energy harvester to ensure robust energy harvesting efficiency remains highly desired. Herein, we propose a wearable pendulum-rotor-separated triboelectric-electromagnetic hybrid generator (PTEHG). The novel pendulum-rotor separation design can make the rotor propelled in one direction by the swinging pendulum, which can further facilitate a wearable hybrid energy harvester with stable energy harvesting, a broad operating bandwidth, and system reliability. By converting the biomechanical energy into electric power, the peak power density of 83.12 W/m3 is delivered by the PTEHG at a frequency of 1.6 Hz. A PTEHG-based healthcare monitoring system was also demonstrated for real-time motion tracking and fall detection. This work paves a new way for enhancing the efficiency of human biomechanical energy harvesting and presents a practical pathway for continuous healthcare monitoring.

3D Polymeric Lattice Microstructure-Based Microneedle Array for Transdermal Electrochemical Biosensing.

Microneedles (MNs) or microneedle arrays (MNAs) are critical components of minimally invasive devices comprised of a single or a series of micro-scale projections. MNs can bypass the outermost layer of the skin and painlessly access microcirculation of the epidermis and dermis layers, attracting great interest in the development of personalized healthcare monitoring and diagnostic devices. However, MN technology has not yet reached its full potential since current micro- and nanofabrication methods do not address the need of fabricating MNs with complex surfaces to facilitate the development of clinically adequate devices. This work presents a new approach that combines 3D printing technology based on two-photon polymerization with soft lithography for cost-effective and time-saving fabrication of complex MNAs. Specifically, this method relies on printing complex 3D objects efficiently replicated into polymeric substrates via soft lithography, resulting in a free-standing polymeric lattice (PL) membrane that can be transferred onto gold-coated MNs and used for electrochemical biosensing. This platform shows excellent electrochemical performance in detecting metabolite (glucose) and protein (insulin) biomarkers with a dynamic linear range sufficient for detecting biomarkers in healthy individuals and patients. The approach holds great potential for fabricating next-generationMNs, including their transfer into clinically adequate devices.

Self-management in young and middle-aged patients with hypertension: a systematic review and meta-synthesis of qualitative studies

BackgroundThere has been a sharp increase in the prevalence of hypertension in young and middle-aged people at high risk of the disease. Despite the fact that good self-management can be effective in controlling blood pressure, patients do not perform well in this area, resulting in poor blood pressure control. It is therefore particularly important to gain a thorough understanding of patients’ attitudes to self-management and the factors that influence them. The aim of this study was to synthesise the qualitative research on attitudes, motivations and challenges of self-management in young and middle-aged hypertensive patients, to analyse the synthesised results using the COM-B model and to propose appropriate improvement actions.MethodsFrom the time of construction until May 2023, the system searched PubMed, Web of Science, ProQuest, Embase, MEDLINE, CINAHL, PsyCINFO and CNKI databases. The analyses were carried out using a thematic synthesis approach to summarise the key findings. The findings were then mapped and analysed using the COM-B model.ResultsA total of 11 studies were included, involving 250 patients between the ages of 18 and 64. Four themes with ten sub-themes were identified: Poor disease recognition (low disease perception, fuzzy disease knowledge); Barriers to doctor-patient interactions (short communication time, unmet knowledge needs, incomplete guidance for disease management); Living in a hostile environment (heavy workload, lack of companionship, ignorance of families); Expectations for a healthy body (responsibility of family roles, witness the cruelty of illness). Analysis of the composite results based on the COM-B model showed that low disease perceptions, barriers to doctor-patient interactions and life circumstances with enemies on all sides were the main challenges faced by young and middle-aged hypertensive patients, whereas the expectation of a healthy body was a motivation to promote self-management of blood pressure in patients.ConclusionsThis study shows that family responsibilities are a particular motivator for self-management in young and middle-aged hypertensive patients. In response to the problems they face, we believe that meeting patients’ knowledge needs, improving healthcare professionals’ communication skills and valuing the role of community hospitals are effective ways to promote patient self-management. In the future, telemedicine, mobile healthcare and intelligent monitoring devices will provide a solution to reduce the burden on medical resources.Systematic review registrationPROSPERO CRD42018100810.

Advancements in Flexible Electronics Fabrication: Film Formation, Patterning, and Interface Optimization for Cutting-Edge Healthcare Monitoring Devices.

Flexible electronics can seamlessly adhere to human skin or internal tissues, enabling the collection of physiological data and real-time vital sign monitoring in home settings, which give it the potential to revolutionize chronic disease management and mitigate mortality rates associated with sudden illnesses, thereby transforming current medical practices. However, the development of flexible electronic devices still faces several challenges, including issues pertaining to material selection, limited functionality, and performance instability. Among these challenges, the choice of appropriate materials, as well as their methods for film formation and patterning, lays the groundwork for versatile device development. Establishing stable interfaces, both internally within the device and in human-machine interactions, is essential for ensuring efficient, accurate, and long-term monitoring in health electronics. This review aims to provide an overview of critical fabrication steps and interface optimization strategies in the realm of flexible health electronics. Specifically, we discuss common thin film processing methods, patterning techniques for functional layers, interface challenges, and potential adjustment strategies. The objective is to synthesize recent advancements and serve as a reference for the development of innovative flexible health monitoring devices.

Curvature-Adaptive Compact Triple-Band Metamaterial Uniplanar Compact Electromagnetic Bandgap-Based Printed Antenna for Wearable Wireless and Medical Body Area Network Applications

A novel, compact, monopole apple-shaped, triple-band metamaterial-printed wearable antenna backed by a uniplanar compact electromagnetic bandgap (UC-EBG) structure is introduced in this paper for wearable wireless and medical body area network (WBAN/MBAN) applications. A tri-band UC-EBG structure has been utilized as a ground plane to minimize the impact of antenna radiation on the human body and improve antenna performance for the proposed wearable antenna. Metamaterial triangular complementary split ring resonators (TCSRRs) are incorporated into the antenna and UC-EBG structure, resulting in a compact UC-EBG-backed antenna with an overall size of 39 × 39 × 2.84 mm3 (0.41 λg × 0.41 λg × 0.029 λg). The printed textile antenna operates at 2.45 GHz for the wireless local area network (WLAN), 3.5 GHz for 5G new radio (NR), and 5.8 GHz for the industrial, scientific, and medical (ISM) bands with improved gain and high-efficiency values. Furthermore, the performance of the antenna is analyzed on the human body, where three models of curved body parts are considered: a child’s arm (worst case) with a 40 mm radius, an adult’s arm with a 60 mm radius, and an adult’s leg with a 70 mm radius. The results demonstrate that the proposed antenna is an attractive candidate for wearable healthcare and fitness monitoring devices and other WBAN/MBAN applications due to its compact size, high performance, and low SAR values.

Nanocomposite hydrogel for skin motion sensing – An antifreezing, nanoreinforced hydrogel with decorated AuNP as a multicrosslinker

In this study, we present a nanocomposite hydrogel designed for skin motion sensing. The hydrogel is based on poly(acrylamide) crosslinked with gold nanoparticles covalently bound to the polymer matrix, yielding a robust, highly elastic and conductive material. The choice of amino acid derivative – N,N’-diacryloylcystine salt (BISS) – as a crosslinker allows for the introduction of gold nanoparticles, due to the presence of sulfide groups in its structure. During the nanoparticle modification process, covalent bonds between gold and sulfur atoms are formed as the disulfide bond is cleaved. In result of this self-assembly process, a multifunctional Au–BISS crosslinker is formed, enhancing the material’s mechanical properties and introducing electrical conductivity. To confer anti-freezing properties and limit water evaporation, a binary mixture of water and glycerol was used. The resultant hydrogel exhibits high elasticity, strain sensitivity across a wide strain range and various types of deformation (elongation, bending, compression) with exceptional response time (120 ms) and recovery time (90 ms). The material’s cold-resistance, resilience, and conductivity make it well-suited for real-time monitoring of joint movements and speech recognition, with potential applications in electronic skin and healthcare monitoring devices.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Feature engineering and deep learning-based approach for event detection in Medical Internet of Things (MIoT)

Medical Internet of Things (MIoT) is becoming increasingly mainstream as their applications continue to grow. These applications include remote healthcare monitoring, drug storage, alarm systems, and medical wearable devices. Remote patient monitoring, especially using wearable devices, has improved the health and protection of numerous people, while avoiding unnecessary hospital visits. Ideally, healthcare monitoring systems should accurately and timely detect if the patient is entering a critical condition, and alert the concerned doctor accordingly. However, in practice, there are two kinds of alarms raised by such systems, namely, events and anomalies. Events are non-erroneous data that do not conform to the expected values, indicating abnormality in the patient’s condition. On the other hand, anomalies are erroneous data that may result from faulty sensors, electromagnetic interference, malicious attacks, or hardware tampering. These anomalies lead to false alarms which affect the decision making process and may cause danger to the patient’s life. Traditionally, wearable MIoTs, measuring different vitals, frequently communicate with the patient’s phone, or a local processing unit (LPU), in order to analyze and process the collected data. However, the increase in processing capabilities of IoT devices has made on-device event detection possible. This adds an extra layer of defense while avoiding unnecessary communication with LPU. In this paper, a healthcare monitoring system is proposed which compromises of two layers - (1) an online lightweight approach, based on multivariate long-short term memory (LSTM) autoencoder, embedded in each MIoT, that is able to detect data abnormality and alarms the LPU, (2) a correlation technique, at the LPU, that differentiates between anomalies and events, and alerts the patient’s doctor only in case of an event. Since a single sensor reading does not provide enough clinical insight to detect the full length of an event, a feature engineering approach is implemented to extract interpretable statistical, dynamic and physiological features that provide the clinical insight needed. The proposed approach is simulated using Medical Information Mart for Intensive Care (MIMIC) dataset for various vital signs, such as heart rate, systolic and diastolic blood pressure. It is compared with four benchmarks, where the results show the robustness of the model in differentiating between anomalies and events, with event detection sensitivity and specificity above 93%.

Ultrasensitive Flexible Pressure Sensors with Broad Linear Detection Range Enabled by Magnetically Induced Self‐Assembled Parallel Circuit

AbstractFlexible pressure sensors have attracted intensive interest for promising applications in healthcare monitoring and flexible electronic devices. Nonetheless, designing flexible pressure sensors to achieve both ultrahigh sensitivity (>1000 kPa−1) and an extensive linear range (>50 kPa) poses a significant challenge. In this study, a facile magnetic‐facilitated self‐assembly parallel circuit strategy to prepare high‐performance flexible pressure sensors in Nickel particles/polydimethylsiloxane (Ni/PDMS) matrices is proposed. The optimized magnetic‐induced Ni/PDMS piezoresistive sensor (MANPS) exhibits an ultrahigh sensitivity of 3707 kPa−1 over a wide broad linear range (0–80 kPa), and excellent durability with the capability of withstanding 2000 pressure cycles. The MANPS is able to detect a broad range of pressures from pulse to plantar pressures (≈60 kPa), demonstrating great potential for whole‐body healthcare monitoring. Additionally, the sensor is further assembled into an array configuration (2 × 5 pixels) for the assessment of plantar pressure distribution. Importantly, the sensitivity and linear range of the MANPS can be readily adjusted by modifying the mixing ratio of the curing agent, thereby significantly broadening its potential applications.

A computational fractional order model for optimal control of wearable healthcare monitoring devices for maternal health

The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.

An Emerging Era: Conformable Ultrasound Electronics.

Conformable electronics are regarded as the next generation of personal healthcare monitoring and remote diagnosis devices. In recent years, piezoelectric-based conformable ultrasound electronics (cUSE) have been intensively studied due to their unique capabilities, including nonradiative monitoring, soft tissue imaging, deep signal decoding, wireless power transfer, portability, and compatibility. This review provides a comprehensive understanding of cUSE for use in biomedical and healthcare monitoring systems and a summary of their recent advancements. Following an introduction to the fundamentals of piezoelectrics and ultrasound transducers, the critical parameters for transducer design are discussed. Next, five types of cUSE with their advantages and limitations are highlighted, and the fabrication of cUSE using advanced technologies is discussed. In addition, the working function, acoustic performance, and accomplishments in various applications are thoroughly summarized. It is noted that application considerations must be given to the tradeoffs between material selection, manufacturing processes, acoustic performance, mechanical integrity, and the entire integrated system. Finally, current challenges and directions for the development of cUSE are highlighted, and research flow is provided as the roadmap for future research. In conclusion, these advances in the fields of piezoelectric materials, ultrasound transducers, and conformable electronics spark an emerging era of biomedicine and personal healthcare.

Evaluation of primary hemodynamic parameters using in-house developed flexible microstructured pressure sensor

Noninvasive measurement of arterial pulse provides information about the heart condition through the evaluation of primary hemodynamic parameters such as blood pressure (BP), pulse rate (PR), and pulse regularity. Moreover, BP is one of the most important biomarkers of cardiovascular disease, mainly hypertension. To prevent this, accurate measurement of BP becomes necessary. Currently, flexible pressure sensors have drawn a lot of interest due to their prospective applications in wearable devices for healthcare monitoring. We have collected the human oscillometric pulse by a polymer-based flexible capacitive pressure sensor and calculated the BP as well as PR using the maximum amplitude algorithm. The results are compared with a pre-calibrated oscillometric BP monitor available in the market. Based on the error criteria, the device followed the universal standard. In addition, the flexibility and high accuracy of the device result in the assessment of the possible medical applications, including abnormal pulse rates such as tachycardia or bradycardia and irregular heart rhythm such as cardiac arrhythmia.

Textile and metasurface integrated wide-band wearable antenna for wireless body area network applications

Wireless Body Area Network (WBAN) enhances the quality of life through personal health and remote healthcare monitoring. In this work, we present a numerical model, which is based on CST Microwave Studio simulations, and a fabrication process to achieve a wideband, low-profile, and flexible wearable antenna by integrating textiles with a metasurface (MTS). The performance of the developed antenna was investigated in free space and on a human body (chest, arm, and leg) both numerically and experimentally. The results showed that by incorporating a metasurface structure on the antenna, the simulated gain was improved from 6.1 dBi to 9.08 dBi, while the measured gain was increased from 6.63 dBi to 8.49 dBi. The increased gain in a specific direction is related to more power concentration in that direction. The antenna operates in a bandwidth ranging from 6 to 12 GHz, which is preferred for WBAN applications. The antenna with an MTS structure displayed a more uniform radiation pattern with greater directivity and a Front-to-Back Ratio (FBR) of approximately 15 dB from both numerical and measured results, which is essential for WBAN to avoid back radiation from the body while worn and to increase the wireless signal distance. Additionally, numerical analysis of the bending evaluation for different diameters revealed that its reflection coefficient is not much altered. We believe that the developed structure could assist in the fabrication of unique WBANs and healthcare monitoring devices.

SDN-Controlled Resource-Tailored Analytics for Healthcare IoT System

In this work, we propose phSDN, a programmable healthcare network architecture for resource-tailored management and on-demand allocation of analytic modules in Internet of Things (IoT) healthcare system. The identification, dynamic management of services, and fast analysis of healthcare data are a few challenges in existing remote healthcare monitoring devices. phSDN programs and reconfigures the resources and services, while updating the associated flow-rules in the data-plane of software-defined network (SDN) architecture. It is especially useful in scenarios with intermittent or constrained network connectivity and/or device availability. The newly identified flows associated with different resources are explicitly considered, while forwarding services over the SDN network. The on-the-fly programmable services enable healthcare devices to provide resource-specific analytics. The programmability over services also allows unified control over the end-devices by reconfiguring on-the-fly vendor-specific heterogeneity of resource types. Our prototype of phSDN supports three different types of resources: 1) body temperature, 2) pulse rate, and 3) blood pressure. Experiments using our implementation show that the proposed architecture minimizes the network response time by at least 43.87% and 78.4%, compared to the fog- and cloud-based approaches, respectively. The implemented system's scalability is further evaluated using Mininet by considering latency, packet delivery ratio, and throughput, as the performance metrics.

Aptamer-functionalized field-effect transistor biosensors for disease diagnosis and environmental monitoring.

Nano-biosensors that are composed of recognition molecules and nanomaterials have been extensively utilized in disease diagnosis, health management, and environmental monitoring. As a type of nano-biosensors, molecular specificity field-effect transistor (FET) biosensors with signal amplification capability exhibit prominent advantages including fast response speed, ease of miniaturization, and integration, promising their high sensitivity for molecules detection and identification. With intrinsic characteristics of high stability and structural tunability, aptamer has become one of the most commonly applied biological recognition units in the FET sensing fields. This review summarizes the recent progress of FET biosensors based on aptamer functionalized nanomaterials in medical diagnosis and environmental monitoring. The structure, sensing principles, preparation methods, and functionalization strategies of aptamer modified FET biosensors were comprehensively summarized. The relationship between structure and sensing performance of FET biosensors was reviewed. Furthermore, the challenges and future perspectives of FET biosensors were also discussed, so as to provide support for the future development of efficient healthcare management and environmental monitoring devices.

Graphene Solution‐Gated Field‐Effect Transistor for Ultrasound‐Based Wireless and Battery‐Free Biosensing

AbstractThe development of wireless and battery‐free sensors for biomedical applications is a fast growing research and industrial field. It promises to greatly improve the patient's comfort during the diagnosis phase, but also in the treatment of chronic diseases. While the standard technologies are based so far on electromagnetic waves, ultrasonic powering and communication is offering perspectives to further reduce the size of the sensor in order to develop minimally invasive electronic implants. Wireless and battery‐free ultrasound‐based devices for healthcare monitoring comprise a piezoelectric element for the powering and communication, and a variable shunt load that varies according to a physiological parameter of interest. The changes in the load modify the acoustic reflectivity of the piezoelectric element, which can be detected using a pulse‐echo protocol. In the present study, the use of graphene solution‐gated field‐effect transistor is introduced as a new type of shunt load. Using an mm‐sized device, it is shown that the amplitude of an ultrasonic wave that reflects on the piezoelectric component is modulated by the voltage applied on the gate of the transistor both in physiological medium and biological tissue. This study sets the basis toward a new type of ultrasound‐based wireless and battery‐free biosensors.

Ionic Gel Based Multifunctional Sensor for Body Temperature Monitoring and Joint Motion Detection

Flexible temperature and strain sensors are key components of wearable devices for healthcare monitoring. Currently, the insufficient precision, undesirable mechanical properties, and the cross interference between different external stimuli are still bottleneck problems for flexible sensors especially in body temperature and human motion monitoring. Herein, an ionic gel based on ionic liquids (ILs) and commercial thermoplastic polyurethane (TPU) fiber is fabricated via a feasible and scalable immersion method for temperature and strain multifunctional sensing. Wearable sensor based on ionic gel exhibits high temperature sensitivity (2.73% ◦C−1) and sufficient resolution (0.1 ◦C) in the skin temperature range. Meanwhile, the sensor exhibits linear and stable strain sensing performance and has the ability to respond to tiny strains. More importantly, the multifunctional fiber‐like sensor is easy to integrate with traditional textile, which is beneficial to solve the wearability problems such as discomfort and impermeability in practical applications. Notably, the stimulus caused by strain can be almost avoided by sewing smart fibers into S‐shape during the temperature monitoring, which helps decouple the temperature and strain signals. As prepared intelligent textiles can accurately capture small fluctuations in temperature and detect the action amplitude of the human body, which is important for disease prediction and rehabilitation training of patients.

Digital life as a cabaret, old chum: A dramaturgical analysis of older digitalised home residents and their wider caring networks

The use of smart and assistive devices for remote healthcare monitoring is becoming increasingly popular for older people in their homes. However, the lived and long-term experiences of such technology, for the older residents and their wider caring networks remains unclear. Using in-depth qualitative data collected between June 2019 and January 2020 from older people living in their own homes in rural Scotland, we highlight that although such monitoring could improve the experiences of older people and their wider caring networks, this may create additional care and surveillance. We employ the concept of dramaturgy, which understands society to be a stage on which actors perform, allowing us to explore how different residents and their networks make sense of their experiences with domestic healthcare monitoring. We found that some digitalised devices may reduce the degree to which older people and their wider caring networks can live authentic and truly independent lifestyles.

3D printing of PVDF-based piezoelectric nanogenerator from programmable metamaterial design: Promising strategy for flexible electronic skin

The high-performance piezoelectric nanogenerator (PENG) with good flexibility, tunability and sensitivity is desirable for the next-generation of wearable electronics. However, the 3D printing of unique architectures and arbitrary shapes could endow the conformal electronic sensors with excellent flexibility and functionality. In this work, the self-poled PENG were successfully fabricated by incorporating the oriented ionic salt-montmorillonite (IS-MMT) co-fillers into poly (vinylidene fluoride) (PVDF) matrix and then performing 3D printing. This hence could give the 3D printed PENG the locked interfacial piezoelectric polarization. More importantly, the self-adaptive conformal PENG based on auxetic metamaterial has also been developed, which is beyond the practicability of the traditional 2D PENG. The effect of various architectures of the metamaterial on the mechanical and piezoelectric responses was particularly discussed. The tailoring of the 3D printed cell with metamaterial of serpentine geometries shows the appealing advantages of high sensitivity to structure variation, thus being beneficial for easier regulation of piezoelectric output. When mounted on the joints as a wearable healthcare monitoring device, this 3D printed PENG could accurately detect the movement of muscles, demonstrating its potential in motion tracking and sign-language interpretation. Obviously, this work could provide a new idea for design and fabrication of programmable wearable electronics.