Wearable Physiological Monitoring Research Articles

The study on workload analysis of construction workers operating at elevated jobsite using wearable physiological monitor

ABSTRACT Occupational accidents and casualties in the construction industry are ranked first among all industries. Construction workers are urged to avoid increasing workload and fatigue due to work environment factors in the jobsite with specific hazards of high risk. The present study adopts the photoplethysmography (PPG) monitoring technology of the wearable wristband for monitoring the heart rate (HR). The study establishes the heart rate schema of the workers, evaluate their workload levels, and verify the physiological impact of working at elevated jobsite on the workload through a statistical T-test. In this study, the T-test in pairs reaches the results of 15-minute time interval unit is the most suitable data analysis interval. The worker working in an elevated environment increases heart rate due to the afraid of height and relatively reduce the worker’s ability to adapt to tasks with high physical demands. It is estimated that more than 10% of working time increased from moderate to heavy workload.

A Low-Cost Smart Wearable Glove for Non-Invasive Health Monitoring

Wearable physiological signal monitoring systems hold notable potential in the future of personal healthcare by seamlessly integrating into daily life, providing continuous monitoring, and aiding in the early detection of health issues. This research presents a wearable health monitoring glove, with a focus on cost-effectiveness while maintaining efficiency for developing countries. The wearable glove can track vital physiological indicators like Blood Pressure, Body Temperature, Glucose level, Blood Oxygen Saturation, Hemoglobin level, ECG, Room Temperature, Humidity, and Motion Tracking. A user-friendly interface facilitates easy interaction, while efforts in energy-efficient design and power management aim to prolong battery life. Also, real-time data monitoring ensures precision in signal analysis and the extraction of vital health data of individuals. The proposed wearable hand glove utilizes a collection of sensors and integrates them towards the diverse detection of skin humidity, temperature, blood oxygen, hemoglobin, etc. non-invasively. Apart from its technical features, the research explores potential applications in healthcare, fitness tracking, and research fields, presenting a versatile solution. Beyond its technical attributes, the research explores potential applications in medical and personal healthcare, fitness tracking, sports, etc. Collaborative efforts with regulatory bodies assess the feasibility of obtaining necessary approvals or certifications, while scalability considerations pave the way for potential mass production and market deployment.

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

The development of wearable devices for ongoing physiological monitoring and analysis has been made possible by the emergence of the Internet of Things (IoT), which has completely changed the healthcare industry. This study discusses the use of such wearable IoT devices and their potential to improve healthcare delivery. Our research focuses on the development, manufacture, and implementation of wearable sensors capable of real-time monitoring of vital signs like heart rate, body temperature, blood pressure, and activity levels. These devices leverage the Internet of Things to transmit data wirelessly to a cloud service for further analysis. The core components of these gadgets are miniature sensors, low-power microcontrollers, and wireless communication modules. These developments permit the invisible and continuous collection of data, which aids in the early detection of irregularities and permits urgent medical treatments. Data collected by these devices is processed by sophisticated analytics and machine learning algorithms before being transmitted securely to a cloud-based server. As a result, doctors and nurses are able to anticipate health issues, monitor their patients' physical conditions in real time, and provide individualised care. Los patients have more control over their own medical decisions since they have access to their own data. Frequent use of Internet-connected devices for monitoring and analysing physical phenomena may lead to better medical care, lower hospitalisation costs, and higher quality of life. Additionally, these devices have the potential to revolutionise clinical research by providing extensive real-world data for medical research. These wearable IoT devices have the potential to enhance the management of chronic illnesses, encourage early treatments, and raise general wellbeing. Wearable physiological monitoring technology has a lot of potential for the future of healthcare thanks to the advancements of the internet of things.

Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

Highly sensitive piezoresistive and thermally responsive fibrous networks from the in situ growth of PEDOT on MWCNT-decorated electrospun PU fibers for pressure and temperature sensing

Flexible electronics have demonstrated various strategies to enhance the sensory ability for tactile perception and wearable physiological monitoring. Fibrous microstructures have attracted much interest because of their excellent mechanical properties and fabricability. Herein, a structurally robust fibrous mat was first fabricated by electrospinning, followed by a sequential process of functionalization utilizing ultrasonication treatment and in situ polymerization growth. Electrospun polyurethane (PU) microfibers were anchored with multi-walled carbon nanotubes (MWCNTs) to form conductive paths along each fiber by a scalable ultrasonic cavitation treatment in an MWCNT suspension. After, a layer of poly(3,4-ethylene dioxythiophene) (PEDOT) was grown on the surface of PU fibers decorated with MWCNTs to enhance the conductive conjunctions of MWCNTs. Due to the superior electromechanical behaviors and mechanical reinforcement of PEDOT, the PEDOT/MWCNT@PU mat-based device exhibits a wide working range (0–70 kPa), high sensitivity (1.6 kPa−1), and good mechanical robustness (over 18,000 cycles). The PEDOT/MWCNT@PU mat-based sensor also demonstrates a good linear response to different temperature variations because of the thermoelectricity of the PEDOT/MWCNT composite. This novel strategy for the fabrication of multifunctional fibrous mats provides a promising opportunity for future applications for high-performance wearable devices.

(Invited) smart Wearable Electronics for Chronic Disease Management

60% of Americans live with at least one chronic disease. These diseases and associated comorbidities are now the leading causes of death in the United States. Effective management of complex chronic diseases requires body-wide, long-term, accurate, and continuous monitoring of multiple physiological signals from wearable devices to precisely determine the pathological state. These wearable physiological signal monitoring can dramatically reduce the demand of physician visiting and increase patients’ engagement and treatment adherence rate. Although wearable bioelectronic devices have shown huge potential in chronic disease management, existing technology still mostly relies on rigid chip components, where the interface between chips and skin/tissue has been a bottleneck that limits the system performance (noise, robustness etc). To address these challenges, my research has been involved in the exploration of rational system design concepts, material and device fabrication innovation, and tailored algorithms to enable smart wearable electronics that targets next-generation chronic disease management.Here I would like to discuss two of my developed technology platforms to elaborate the concept of smart wearables. First, I will describe a body area sensor network technology platform. In the system design concept side, this technology utilizes a novel wireless hybridization strategy and an unconventional detuned RFID working regime that provide an ideal skin interface. In the material/device side, this system integrates several skin-conforming polymer-based soft devices (ring oscillators, RF diodes, antennas, transistors etc.) as an integrated soft multimode sensing sticker. (1,2) Next, I will describe a wireless closed-loop smart bandage that can accelerate and monitor the wound healing process. (3) Such smart bandage also integrates new design concepts (wireless, battery-free design that combines both sensing and stimulation), novel materials (low-impedance, high-toughness, and tunable adhesion hydrogel as skin interface) and closed-loop control algorithms. Overall, the developed technology platform could assess multiple health outcomes and treatment responses to various chronic diseases. Ultimately, this technology will help to reduce chronic disease burden, lower medical costs, and provide a better quality of life for patients.

A machine learning-based decision support system for temporal human cognitive state estimation during online education using wearable physiological monitoring devices

Over the last decade, there has been a considerable increase in the popularity of online education. As a result, the online learning or e-learning industry has flourished, providing benefits to students, learners, educators, and education experts. Despite the advantages of e-learning, it also has its drawbacks. While e-learning enables students to access the learning materials at their convenience from any location, one of the significant challenges is the lack of monitoring of their level of attention during e-learning sessions. It is challenging to ascertain whether a student is actively engaged in the learning process. To address this issue, we have proposed a decision support system (DSS) based on wearable physiological sensor signals (i.e., Electroencephalogram (EEG) signals) that can inform the instructor whether a student is attentive. For developing DSS, we recorded an EEG-based dataset using a neurosky device, and 100 individuals participated in the study. The learning state is divided into two categories: attentive and inattentive. In this paper, machine learning techniques are employed to integrate the proposed DSS, which can predict, analyse, and validate the student’s level of attention or inattention throughout the e-learning session. The findings show that the Support Vector Machine (SVM) approach is the most efficient method for attention prediction, achieving an accuracy of 91.68% compared to logistic regression and ridge regression. Additionally, we examined the frequency bands that were most significant in predicting the learning state, with beta and alpha waves being identified as the key contributors in predicting attention. To further evaluate the data, we use K-means and Hierarchical algorithms to cluster beta and alpha data points. K-means effectively identifies an ideal representative of an attentive or inattentive state. Thus, EEG waves can effectively reveal whether a student is attentive during real-time e-learning sessions, providing a promising approach for providing a valuable tool for decision support in the E-Learning Environment.

Single-Step Transfer-Printing Microfabrication of Soft Bioelectronics Using PDMS with Tuned Adhesion Forces

Recent soft and stretchable bioelectronics for various wearable applications generally require special equipment and facilities for microelectromechanical systems (MEMS). As an alternative, simple photolithography-free microfabrication methods have been proposed based on transfer printing onto soft substrates; however, limitations remain in that precise control is involved for modulating adhesion forces. In this study, a simple, rapid, cost-effective transfer-printing-based microfabrication process is demonstrated without requiring any MEMS process or sophisticated transfer control. We utilized ethoxylated polyethylenimine (PEIE) to tune the adhesion properties of polydimethylsiloxane (PDMS) such that thin sensor patterns fabricated by laser-machined gold leaves are easily transferred from the low-adhesion donor PDMS onto the high-adhesion receiver PDMS layer. The microfabrication steps were optimized based on the electrical and mechanical analysis of the transferred patterns, thereby enabling stable fabrication of thin gold lines of 100 μm width. An additional advantage of the sticky PDMS (sPDMS) is presented for stronger bonding of the cover layer and gold layer around the opening windows, which may allow for greater long-term stability in aqueous conditions. The sPDMS also enables adhesive-free attachment to the skin without losing its adhesion forces over repeated applications. The feasibility of the simple microfabrication process is verified by successfully demonstrating a multifunctional wearable patch for electrical (electromyography) and mechanical (strain) monitoring. This process provides a complete set of efficient microfabrication procedures for soft bioelectronics, from metal deposition to patterning and selective encapsulation, which can be utilized in a wide range of multifunctional and wearable physiological monitoring.

Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor.

With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system. Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy. High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance. The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.

Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

“Remote Health Monitoring of elder using Wearable Sensor.”

Abstract: Wearable physiological monitoring devices are used to monitor the health status of a Patient in Intensive Care Units (ICU). These systems are wired everywhere. A wireless Physiological parameter monitoring system is presented in this paper. Literature review carried out in the area of wearable physiological monitoring systems. The proposed system includes sensors for continuous collection and evaluation of physiological parameters. A wireless personal area network has been introduced for reliable transmission of physiological information. The hardware design of this patient monitoring system is based on 8051 microcontrollers. Microcontroller acts as the gateway to ZigBee and GSM module. ZigBee wireless communication module has the advantage of lower power consumption which is attractive for portable applications. This system provides safe and accurate monitoring. It also gives the freedom of movement. The proposed system can be used to monitor the physiological condition of a person in out of hospitalcondition also. Keywords: Pulse Sensor, Temperature Sensor, ZigBee,Microcontroller, LCD, Buzzer Arduino.

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.

Wearable Physiological Multi-Vital Sign Monitoring System With Medical Standard

In recent years, due to increased attention of individual physical health condition, the demand on portable multi-vital signs monitoring devices has shown an explosive growth. This work designed a portable, multi-vital signs monitoring system, consisting of chest unit and wrist unit, with size of 32 mm <inline-formula> <tex-math notation="LaTeX">$\times32$ </tex-math></inline-formula> mm and diameter of 30 mm, respectively. The designed system is capable of transmitting measured electrocardiograph (ECG), pulse wave and body temperature signal to the mobile devices wirelessly by Bluetooth low energy (BLE) technique. The heart rate, pulse oximetry (SpO₂) and respiratory rate are extracted through time domain algorithm. The chest and wrist units can continuously monitor body vital signs for more than 12 and 24 hours, respectively. Besides, the experiment results reveal that the chest unit is able to monitor patients with heart rate abnormalities such as atrial fibrillation and premature beats. The typical heart diseases can be detected in real-time. Meanwhile, the wrist unit clearly detects the changes of the physical parameters of the subjects under different physiological conditions. The accuracy of the designed system is consistent with that of the commercial medical instrument counterpart, with further advantages of compact, portable and patient-friendly natures.

Detection of the Complete ECG Waveform with Woven Textile Electrodes.

Wearable physiological monitoring systems are becoming increasingly prevalent in the push toward autonomous health monitoring and offer new modalities for playful and purposeful interaction within human computer interaction (HCI). Sensing systems that can be integrated into garments and, therefore, daily activities offer promising pathways toward ubiquitous integration. The electrocardiogram (ECG) signal is commonly monitored in healthcare and is increasingly utilized as a method of determining emotional and psychological state; however, the complete ECG waveform with the P, Q, R, S, and T peaks is not commonly used, due to the challenges associated with collecting the full waveform with wearable systems. We present woven textile electrodes as an option for garment-integrated ECG monitoring systems that are capable of capturing the complete ECG waveform. In this work, we present the changes in the peak detection performance caused by different sizes, patterns, and thread types with data from 10 human participants. These testing results provide empirically-derived guidelines for future woven textile electrodes, present a path forward for assessing design decisions, and highlight the importance of testing novel wearable sensor systems with more than a single individual.

Investigation on new paradigm of clinical physiological monitoring by using wearable devices

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.

Facile fabrication of sensitivity-tunable strain sensors based on laser-patterned micro-nano structures

Flexible/stretchable electronics have been well-developed in epidermal devices, wearable electronic systems and soft robotics for physiological signal detecting, real-time health monitoring and human–machine interfaces. As one of the most widespread-used transducers, strain sensors are playing a promising role in the developments of ongoing flexible electronics, especially equipped with tunable sensitivity (or gauge factor, GF). However, present investigations mainly focus on the enhancement of the sensitivity which required subtle designs or sophisticated fabrication processes. In this work, we report a facile fabrication strategy for configuring strain sensors with tunable sensitivity by adjusting the orientations and duty ratios of the micro-nano hierarchical Ag-coated microgrooves from laser patterning. The heterogeneous micro-nano structures enable localized large deformation to result in crack propagation on electrical layer during stretching, which endows the device with customized GF from 3.4 to 4570.6. The sensitivity-tunable strain sensor shows a great potential in monitoring various health conditions and voice recognition. This technique provides a facile and robust way to fabricate high-performance strain sensors for wearable physiological monitoring systems.

Research progress on wearable physiological parameter monitoring and its clinical applications

Wearable physiological parameter monitoring devices play an increasingly important role in daily health monitoring and disease diagnosis/treatment due to their continuous dynamic and low physiological/psychological load characteristics. After decades of development, wearable technologies have gradually matured, and research has expanded to clinical applications. This paper reviews the research progress of wearable physiological parameter monitoring technology and its clinical applications. Firstly, it introduces wearable physiological monitoring technology's research progress in terms of sensing technology and data processing and analysis. Then, it analyzes the monitoring physiological parameters and principles of current medical-grade wearable devices and proposes three specific directions of clinical application research: 1) real-time monitoring and predictive warning, 2) disease assessment and differential diagnosis, and 3) rehabilitation training and precision medicine. Finally, the challenges and response strategies of wearable physiological monitoring technology in the biomedical field are discussed, highlighting its clinical application value and clinical application mode to provide helpful reference information for the research of wearable technology-related fields.

Interface sensors with skin piezo-thermic transduction enable motion artifact removal for wearable physiological monitoring

Accurate measurements on physiological parameters using wearable monitoring devices during physical exercises are essential for personal healthcare and rehabilitation training, but still challenging owing to various motion artifacts (MA) caused by the interfacial dynamic change between wearable sensors and human skin. Here, we propose an interface sensor to detect noncontact proximity and contact pressure between wearable sensors and human skin. The interface sensor employs natural piezo-thermic transduction of human skin and enables direct interfacial proximity/pressure detection by using simple thin-film thermistors to detect the interfacial thermal field change. We develop a wearable watch-type heart rate (HR) monitor utilizing interface sensors to remove MA for a photoplethysmography (PPG) sensor through adaptive filtering. To validate the method, we conduct experiments for multiple subjects, who carry out HR monitoring using the wearable device while doing various physical exercises. The PPG-based HR estimations are corrected through MA removal using interface sensors and compared with that using conventional accelerometer-based MA removal. The experimental results verify that the interface sensors capture the interfacial dynamic change between the PPG sensor and skin better, and obtain more accurate HR estimations during irregular and muscle strength exercises. Utilizing natural transduction of human skin and simple thermometry, the interface sensor provides an advantageous way to overcome MA for wearable monitoring devices during physical activities and thus broadens wearable monitoring applications.

Study on the quality evaluation of electrocardiogram signal in wearable physiological monitoring system

As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results: sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects: the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test, P=0.133). The results show that the ECG signal quality algorithm proposed in this paper can effectively evaluate the ECG signal quality of the wearable physiological monitoring system. Compared with signal measured by Holter, the ECG signal measured by SensEcho-5B has the same ECG signal quality. Follow-up studies will further collect physiological data of large samples in real clinical environment, analyze and evaluate the quality of ECG signals, so as to continuously optimize the performance of the monitoring system.

Muscle Fibers Inspired High‐Performance Piezoelectric Textiles for Wearable Physiological Monitoring

AbstractThe next‐generation wearable biosensors with highly biocompatible, stretchable, and robust features are expected to enable the change of the current reactive and disease‐centric healthcare system to a personalized model with a focus on disease prevention and health promotion. Herein, a muscle‐fiber‐inspired nonwoven piezoelectric textile with tunable mechanical properties for wearable physiological monitoring is developed. To mimic the muscle fibers, polydopamine (PDA) is dispersed into the electrospun barium titanate/polyvinylidene fluoride (BTO/PVDF) nanofibers to enhance the interfacial‐adhesion, mechanical strength, and piezoelectric properties. Such improvements are both experimentally observed via mechanical characterization and theoretically verified by the phase‐field simulation. Taking the PDA@BTO/PVDF nanofibers as the building blocks, a nonwoven light‐weight piezoelectric textile is fabricated, which hold an outstanding sensitivity (3.95 V N−1) and long‐term stability (&lt;3% decline after 7,400 cycles). The piezoelectric textile demonstrates multiple potential applications, including pulse wave measurement, human motion monitoring, and active voice recognition. By creatively mimicking the muscle fibers, this work paves a cost‐effective way to develop high‐performance and self‐powered wearable bioelectronics for personalized healthcare.