Physiological Signal Monitoring Research Articles

Graded Microstructured Flexible Pressure Sensors with High Sensitivity and an Ultrabroad Pressure Range for Epidermal Pulse Monitoring.

Precisely detecting epidermal pulse waves with pressure sensors is crucial for pulse-based personalized health-monitoring technologies. However, developing a pressure sensor that simultaneously demonstrates high sensitivity and an ultrabroad pressure range and a convenient fabrication process for large-scale production is a considerable challenge. Herein, by utilizing a commercial conductive fabric (CF) and a silica gel film, we develop a high-performance pressure sensor (HPPS) for the monitoring of human physiological signals. Based on convenient turnover formwork technology, the silica gel film was fabricated by replicating the microstructure of the sandpaper surface. This microstructure and the plain weave structure on the CF surface together provide a sharp increase in the contact-separation area and structural compressibility, which are beneficial for the enhancement of output performance. Made of these two materials, the graded microstructured HPPS holds high sensitivity (4.5 mV/Pa), an ultrabroad pressure range (0-30 kPa), a wide working frequency bandwidth (up to 35 Hz), decent stability (>50,000 cycles), and a simple fabrication process that is suitable for large-scale production. Given these noticeable features, the developed HPPS not only succeeds in precisely detecting subtle pulse waves on various positions of different people but can also objectively capture changes in cardiovascular parameters caused by exercise training at different intensities in real time. These findings exhibit the enormous potential application of HPPS in tracking an individual's health status and comprehensively evaluating exercise intensity.

Highly Sensitive Humidity Sensors Based on Pt Functionalized ZIF-67 Towards Noncontact Healthcare Monitoring

In order to enhance the sensitivity, Pt coated ZIF-67 was proposed to detect humidity levels for human physiological signal monitoring. The proposed capacitive humidity sensors have superior sensitivity of 44.9 pF/%RH for ZIF-67 with 10 nm Pt, which is about 7.8 times of ZIF-67 with 2 nm Pt and 4490 times of pure ZIF-67. The specificity of water toward 7 interferential volatile organic compounds (VOCs) was analyzed for biomarker discrimination. The proposed sensor exhibits high stability and repeatability in five cycle tests. Normal, fast, deep, and slow breath were investigated to record reading, running, yoga, and sleeping activities, respectively. In addition, the low humidity level evaporated by human sweat on the skin surface can be detected with high sensitivity. This paper provides a highly sensitive humidity sensor for daily clinical monitoring and intelligent health applications.

Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor.

Stress is a physiological state that hampers mental health and has serious consequences to physical health. More-over, the COVID-19 pandemic has increased stress levels among people across the globe. Therefore, continuous monitoring and detection of stress are necessary. The recent advances in wearable devices have allowed the monitoring of several physiological signals related to stress. Among them, wrist-worn wearable devices like smartwatches are most popular due to their convenient usage. And the photoplethysmography (PPG) sensor is the most prevalent sensor in almost all consumer-grade wrist-worn smartwatches. Therefore, this paper focuses on using a wrist-based PPG sensor that collects Blood Volume Pulse (BVP) signals to detect stress which may be applicable for consumer-grade wristwatches. Moreover, state-of-the-art works have used either classical machine learning algorithms to detect stress using hand-crafted features or have used deep learning algorithms like Convolutional Neural Network (CNN) which automatically extracts features. This paper proposes a novel hybrid CNN (H-CNN) classifier that uses both the hand-crafted features and the automatically extracted features by CNN to detect stress using the BVP signal. Evaluation on the benchmark WESAD dataset shows that, for 3-class classification (Baseline vs. Stress vs. Amusement), our proposed H-CNN outperforms traditional classifiers and normal CNN by ≈5% and ≈7% accuracy, and ≈10% and ≈7% macro F1 score, respectively. Also for 2-class classification (Stress vs. Non-stress), our proposed H-CNN outperforms traditional classifiers and normal CNN by ≈3% and ≈5% accuracy, and ≈3% and ≈7% macro F1score, respectively.

CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. The emergence of wearable medical sensors (WMSs) and deep neural networks (DNNs) points to a promising approach to address this challenge. WMSs enable continuous and user-transparent monitoring of physiological signals. However, disease detection based on WMSs/DNNs and their deployment on resource-constrained edge devices remain challenging problems. To address these problems, we propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus and the resultant disease. CovidDeep does not depend on manual feature extraction. It directly operates on WMS data and some easy-to-answer questions in a questionnaire whose answers can be obtained through a smartphone application. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic (to detect the virus), and symptomatic (to detect the disease) patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. The models were also shown to perform well on other performance measures, such as false positive rate, false negative rate, and F1 score. We augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations. This makes the CovidDeep DNNs both accurate and efficient, in terms of memory requirements and computations. The resultant DNNs are embedded in a smartphone application, which has the added benefit of preserving patient privacy.

A Pilot Study on Long-term Physiological Signal Monitoring using Anhydrous Viscoplastic Electrodes.

Electrocardiography (ECG) and Electromyogram (EMG) are widely used to help physicians to diagnose various diseases. Besides, long-term physiological signals monitoring is of great significance for circumstances where certain diseases may not be observed in short-term monitoring. At present, wet electrodes are widely used in the clinic and are considered as a standard method to acquire physiological signals in high fidelity. However, current wet electrodes achieve high-quality signal acquisition by using conductive gel which will dry up as time elapses and finally leads to degradation of the signal quality. Therefore, an anhydrous viscoplastic electrode was proposed in this paper to solve the abovementioned problem. The proposed electrode, which is anhydrous and viscoplastic, enables high quality physiological signal acquisition with firm contact with the skin and it will not dry up within a long period of time. The results showed that the impedance of the proposed viscoplastic electrode could maintain relative stability after two days while that of the gel electrodes would increase significantly due to the gel dried up. Besides, the proposed electrode obtained physiological signals with high quality in both ECG and EMG tasks. After 24 hours of monitoring, the signal quality of the proposed electrode remained unchanged, indicated by the clearly recognizable time-domain signals. However, the signal waveform completely submerged in noise after the gel dried up. Moreover, the superior performance of the viscoplastic electrodes could be confirmed by the SNR difference between the two days, SNR further confirmed the superiority of the, with -2.03±2.10 dB and -3.40±8.27 dB for ECG and EMG respectively, and the SNR difference of gel electrodes were -7.59 ± 5.70 dB and -35.39±15.71 dB respectively. The proposed electrodes could be a great candidate for long-term physiological signal monitoring in risk management of healthcare.Clinical Relevance- The proposed electrode could achieve long-term physiological signals monitoring with high quality.

Modelling and Fabrication of a Multi-Modal Highly Sensitive Pressure Sensor

Flexible and skin mountable wearable health monitoring devices have gained much attention in recent years due to their non-invasive approach to continuous monitoring of weak human physiological signals. With applications such as pulse detection and electronic skin, the potential usage of the highly sensitive pressure sensor is more significant than before. In general, ultra-sensitive pressure sensors can be categorized by the operation mechanism into piezoresistive, capacitive, triboelectric, and piezoelectric sensors. Because all these mechanisms require a flexible substrate for their process, combining different sensing mechanisms leads to one multi-mechanism sensor with compounded advantages and diminished shortcomings. In this study, a multimodal sensor (combining piezoresistive, capacitive, and strain gauge modes of operation) with optimized parameters for precise monitoring of arterial pulse waveform has been proposed to allow detection of not only the pulse but also possible irregularities in its waveform that can be potentially the harbingers of various medical conditions. Finite element analysis was employed to establish the design rules for a highly sensitive piezoresistive sensor and also design rules for a micropatterned dialectic layer were developed and after the fabrication of both sensors, they were merged to form a compact multi-mechanism sensing platform. Moreover, fabrication of breathable polymeric substrate has been accomplished by methods such as electrospinning, sacrificial template, and electrostatic spray deposition to ensure long term comfortable wearability of the device. After the fabrication, this capacitive sensor was used to detect the human pulse from the wrist. Also, using bi-polar electrochemistry porous graphene was deposited on the polymeric foundation as the conductive sensing material for the piezoresistive sensor.Furthermore, the results form modelling showed that smaller feature size, lower number density, and a pyramid angle of about 60 degrees of microfeatures improve the sensitivity of the sensor. And finally, the fabrication of the multi-mechanism sensor followed the standard microfabrication techniques including photolithography and transfer printing method to integrate heterogeneous elements into the sensor. The detailed results will be discussed during the presentation.

Non-Contact Dual-Modality Emotion Recognition System by CW Radar and RGB Camera

Emotion recognition has a significant impact on people’s health and life quality. Recent studies have shown that emotion recognition can be achieved by analyzing audio-visual emotion channels and physiological signals. However, the main challenges are: 1. the audio-visual techniques can be easily fooled by fake facial expressions; 2. the contact physiological signal monitoring introduces additional stress and is usually unsuitable for long-term monitoring; 3. The non-contact physiological signal monitoring methods are easily affected by complex environmental conditions. In this paper, a non-contact dual-modality emotion recognition system is proposed. First, the respiratory and heartbeat signals are measured by radar and camera simultaneously. Then, a hybrid signal optimization approach is proposed to remove the influence of body motion and light conditions on the physiological signal. It includes a light-intensity-based scheme selecting proper heartbeat signal for different light conditions, and an optical-flow-based algorithm pruning signals with significant radial body motion. Finally, the features extracted from the optimized physiological signals are fused to train an emotion recognition system. As shown in the experimental results, the proposed system could achieve high classification accuracy of 89.6% for 10-fold cross-validation at sample level, and 71.0 % for cross-validation at subject level. The respiratory and heartbeat signals by non-contact approaches are demonstrated to be reliable in emotion recognition.

Multifunctional and Physically Transient Supercapacitors, Triboelectric Nanogenerators, and Capacitive Sensors

AbstractElectronic waste (e‐waste) grows in parallel with the increasing need for consumer electronics. This, unfortunately, is leading to pollution and massive ecological problems worldwide. A solution to this problem is the use of transient electronics. While transiency of a few components such as transistors and batteries have been proposed already, it is crucial to have all components in electronic devices to be transient. Therefore, the transiency of more electronic components should be demonstrated to alleviate the e‐waste problem. Herein, multifunctional nanocomposite electrodes are fabricated using poly(vinyl alcohol), carbon black, and activated carbon. These simple electrodes are then used to fabricate physically transient supercapacitors, triboelectric nanogenerators, and capacitive sensors. Transient supercapacitors are used numerous times with excellent supercapacitive behavior before being discarded, which show promise as an energy storage component for transient systems. The fabricated transient triboelectric nanogenerators are used to harvest mechanical energy, eliminated the need for an external power supply, paving the way to self‐powered devices, such as a touchpad as demonstrated herein. The fabricated transient capacitive sensors, on the other hand, have shown long linear sensitivities and offered waste‐free monitoring of physiological signals and body motions.

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda.

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

Water‐Resistant Ionogel Electrode with Tailorable Mechanical Properties for Aquatic Ambulatory Physiological Signal Monitoring

AbstractUnderwater electrocardiography (ECG) monitoring, which can monitor cardiac autonomic changes and arrhythmias during diving, is essential for sports management and healthcare. However, it is crucial yet rather challenging to achieve ECG monitoring in an aquatic environment because the interface electrodes may lose their functionality underwater. Here, an ionogel with tailorable mechanical properties is prepared by a facile one‐step polymerization and used as water‐resistant electrode. The Young's modulus and strain at break of the ionogel can be modulated in the range of 0.22–337 MPa and 349 to >10 000%, respectively. The hydrophobic polymer networks inside the ionogel endow this ionogel with excellent stability, adhesion, and self‐healing ability underwater. The ionic conductivity imparted by the free ionic groups inside the ionogel allows the ionogel to detect and transmit physiological electrical signals. Compared with commercial gel electrodes, this ionogel electrode demonstrates better adhesion ability, conductivity, and stability underwater. The ionogel electrode can collect real‐time ECG signals effectively both in the air and underwater, and the data can be used to warn users of the potential risk of a heart attack.

E-Skin: The Dawn of a New Era of On-Body Monitoring Systems.

Real-time “on-body” monitoring of human physiological signals through wearable systems developed on flexible substrates (e-skin) is the next target in human health control and prevention, while an alternative to bulky diagnostic devices routinely used in clinics. The present work summarizes the recent trends in the development of e-skin systems. Firstly, we revised the material development for e-skin systems. Secondly, aspects related to fabrication techniques were presented. Next, the main applications of e-skin systems in monitoring, such as temperature, pulse, and other bio-electric signals related to health status, were analyzed. Finally, aspects regarding the power supply and signal processing were discussed. The special features of e-skin as identified contribute clearly to the developing potential as in situ diagnostic tool for further implementation in clinical practice at patient personal levels.

A neonatal dataset and benchmark for non-contact neonatal heart rate monitoring based on spatio-temporal neural networks

The digital revolution of noncontact physiological signal monitoring in clinical and home health care is underway, and deep learning techniques are incredibly popular. Camera-based physiological signal monitoring for adults has made considerable progress in recent years. However, most of existing methods and datasets are developed for adult subjects, and until now, there has been no neonatal public database that is collected for developing deep learning method. Thus, in this paper, we introduce a large-scale newborn baby database, named NBHR (newborn baby heart rate estimation database), to fill the abovementioned knowledge gap. A total of 9.6 h of clinical videos (1130 videos totaling 921 GB) and reference vital signs are recorded from 257 infants at 0–6 days old. The facial videos and corresponding synchronized physiological signals, including photoplethysmograph information, heart rate, and oxygen saturation level, are recorded in our database. This large-scale database could be used to develop deep learning methods to estimate heart rate or oxygen saturation levels. Furthermore, a multitask deep learning method, called NBHRnet, is proposed to estimate heart rate based on the NBHR database, and the model is succinct that it can be deployed on a computer without GPUs. The experimental results indicate that NBHRnet yields competitive performance in predicting infant heart rate, with a mean absolute error of 3.97 bpm and a mean absolute percentage error of 3.28%; additionally, it can estimate heart rate almost instantaneously (2 s/60 frames). Our datasets are freely publicly available by request.

Flexible electronics with dynamic interfaces for biomedical monitoring, stimulation, and characterization

AbstractRecent developments in the fields of materials science and engineering technology (mechanical, electrical, biomedical) lay the foundation to design flexible bioelectronics with dynamic interfaces, widely used in biomedical/clinical monitoring, stimulation, and characterization. Examples of this technology include body motion and physiological signal monitoring through soft wearable devices, mechanical characterization of biological tissues, skin stimulation using dynamic actuators, and energy harvesting in biomedical implants. Typically, these bioelectronic systems feature thin form factors for enhanced flexibility and soft elastomeric encapsulations that provide skin‐compliant mechanics for seamless integration with biological tissues. This review examines the rapid and continuous progress of bioelectronics in the context of design strategies including materials, mechanics, and structure to achieve high performance dynamic interfaces in biomedicine. It concludes with a concise summary and insights into the ongoing opportunities and challenges facing developments of bioelectronics with dynamic interfaces for future applications.

Mobile Health, Disease Knowledge, and Self-Care Behavior in Chronic Kidney Disease: A Prospective Cohort Study

Mobile health (mHealth) management is an emerging strategy of care for patients with chronic diseases. However, the effect of mHealth management on clinical outcomes of patients with chronic kidney disease (CKD) has not been well-studied. The aim of this study was to investigate the additional influence of mHealth on disease knowledge and self-care behavior in CKD patients who had received traditional education. We designed and developed a new healthcare mobile application, called iCKD, which has several major features, including home-based physiological signal monitoring, disease health education, nutrition analysis, medication reminder, and alarms and a warning system. Trained nurses interviewed patients with CKD using structured questionnaires of disease knowledge and self-care behavior. After propensity score matching, we analyzed 107 patients who used iCKD and traditional education, and 107 who received traditional education. The patients who used iCKD had higher disease knowledge scores than those who received traditional education. In multivariate analysis, iCKD was significantly and positively associated with disease knowledge scores. Patients with high education levels could have greater disease knowledge through using mHealth. There was no significant difference in total scores of self-care behavior between the two groups. In conclusion, mHealth can significantly increase disease knowledge in patients with CKD.

Recent developments in sensors for wearable device applications.

Wearable devices are a new means of human-computer interaction with different functions, underlying principles, and forms. They have been widely used in the medical and health fields, in applications including physiological signal monitoring; sports; and environmental detection, while subtly affecting people's lives and work. Wearable sensors as functional components of wearable devices have become a research focus. In this review, we systematically summarize recent progress in the development of wearable sensors and related devices. Wearable sensors in medical health applications, according to the principle of measurement, are divided into physical and chemical quantity detection. These sensors can monitor and measure specific parameters, thereby enabling continuously improvements in the quality and feasibility of medical treatment. Through the detection of human movement, such as breathing, heartbeat, or bending, wearable sensors can evaluate body movement and monitor an individual's physical performance and health status. Wearable devices detecting aspects of the environment while maintaining high adaptability to the human body can be used to evaluate environmental quality and obtain more accurate environmental information. The ultimate goal of this review is to provide new insights and directions for the future development and broader application of wearable devices in various fields.Graphical abstract.

Self-Healing, Self-Adhesive Silk Fibroin Conductive Hydrogel as a Flexible Strain Sensor.

Flexible and wearable hydrogel strain sensors have attracted tremendous attention for applications in human motion and physiological signal monitoring. However, it is still a great challenge to develop a hydrogel strain sensor with certain mechanical properties and tensile deformation capabilities, which can be in conformal contact with the target organ and also have self-healing properties, self-adhesive capability, biocompatibility, antibacterial properties, high strain sensitivity, and stable electrical performance. In this paper, an ionic conductive hydrogel (named PBST) is rationally designed by proportionally mixing polyvinyl alcohol (PVA), borax, silk fibroin (SF), and tannic acid (TA). SF can not only be a reinforcement to introduce an energy dissipation mechanism into the dynamically cross-linked hydrogel network to stabilize the non-Newtonian behavior of PVA and borax but it can also act as a cross-linking agent to combine with TA to reduce the dissociation of TA on the hydrogel network, improving the mechanical properties and viscoelasticity of the hydrogel. The combination of SF and TA can improve the self-healing ability of the hydrogel and realize the adjustable viscoelasticity of the hydrogel without sacrificing other properties. The obtained hydrogel has excellent stretchability (strain > 1000%) and shows good conformal contact with human skin. When the hydrogel is damaged by external strain, it can rapidly self-repair (mechanical and electrical properties) without external stimuli. It shows adhesiveness and repeatable adhesiveness to different materials (steel, wood, PTFE, glass, iron, and cotton fabric) and biological tissues (pigskin) and is easy to peel off without residue. The obtained PBST conductive hydrogel also has a wide strain-sensing range (>650%) and reliable stability. The hydrogel adhered to the skin surface can monitor large strain movements such as in finger joints, wrist joints, knee joints, and so on and detect swallowing, smiling, facial bulging and calming, and other micro-deformation behaviors. It can also distinguish physical signals such as light smile, big laugh, fast and slow breathing, and deep and shallow breathing. Therefore, the PBST conductive hydrogel material with multiple synergistic functions has great potential as a flexible wearable strain sensor. The PBST hydrogel has antibacterial properties and good biocompatibility at the same time, which provides a safety guarantee for it as a flexible wearable strain sensor. This work is expected to provide a new way for people to develop ideal wearable strain sensors.

A New Attack Method Against ECG-Based Key Generation and Agreement Schemes in Body Area Networks

Body Area Networks (BAN) are wireless networks designed for deployment on or within the human body. These networks are primarily intended for application within themedical domain due to their capabilities for enablingwireless monitoring of physiological signals, and remote administration of medical devices. Due to their intended use case, securing these devices is paramount. In recent years, several key generationand agreement schemes that rely upon physiological signals of the wearer are developed. However, we have found that the application of Electrocardiogram(ECG) signals in this context may not be appropriate due to a potential vulnerability, wherein previously recorded ECG signals could be used against current and future key agreement attempts to compromise their security. This is a violation of temporal variance which is one of a few properties that make ECG signals suitable for use in key agreement schemes. By extracting the QRS complex from prior recordings and distributing them apart from one another we can construct synthetic signals that have a high level of coherence, and thus allow for the key to be intercepted. Based on the conducted experiments we have found that the proposed attack method yields a 0.7 coherence level regardless of how far away the adversary is from the target. Thismakes the success of such an attack extremely likely and is therefore a real threat to the security of these schemes.

Continuous monitoring of deep-tissue haemodynamics with stretchable ultrasonic phased arrays.

Stretchable wearable devices for the continuous monitoring of physiological signals from deep tissues are constrained by the depth of signal penetration and by difficulties in resolving signals from specific tissues. Here, we report the development and testing of a prototype skin-conformal ultrasonic phased array for the monitoring of haemodynamic signals from tissues up to 14 cm beneath the skin. The device allows for active focusing and steering of ultrasound beams over a range of incident angles so as to target regions of interest. In healthy volunteers, we show that the phased array can be used to monitor Doppler spectra from cardiac tissues, record central blood flow waveforms and estimate cerebral blood supply in real time. Stretchable and conformal skin-worn ultrasonic phased arrays may open up opportunities for wearable diagnostics.

Strain‐Isolating Materials and Interfacial Physics for Soft Wearable Bioelectronics and Wireless, Motion Artifact‐Controlled Health Monitoring

AbstractRecent developments of micro‐sensors and flexible electronics allow for the manufacturing of health monitoring devices, including electrocardiogram (ECG) detection systems for inpatient monitoring and ambulatory health diagnosis, by mounting the device on the chest. Although some commercial devices in reported articles show examples of a portable recording of ECG, they lose valuable data due to significant motion artifacts. Here, a new class of strain‐isolating materials, hybrid interfacial physics, and soft material packaging for a strain‐isolated, wearable soft bioelectronic system (SIS) is reported. The fundamental mechanism of sensor‐embedded strain isolation is defined through a combination of analytical and computational studies and validated by dynamic experiments. Comprehensive research of hard‐soft material integration and isolation mechanics provides critical design features to minimize motion artifacts that can occur during both mild and excessive daily activities. A wireless, fully integrated SIS that incorporates a breathable, perforated membrane can measure real‐time, continuous physiological data, including high‐quality ECG, heart rate, respiratory rate, and activities. In vivo demonstration with multiple subjects and simultaneous comparison with commercial devices captures the SIS's outstanding performance, offering real‐world, continuous monitoring of the critical physiological signals with no data loss over eight consecutive hours in daily life, even with exaggerated body movements.

Hierarchically Microstructure-Bioinspired Flexible Piezoresistive Bioelectronics.

The naturally microstructure-bioinspired piezoresistive sensor for human-machine interaction and human health monitoring represents an attractive opportunity for wearable bioelectronics. However, due to the trade-off between sensitivity and linear detection range, obtaining piezoresistive sensors with both a wide pressure monitoring range and a high sensitivity is still a great challenge. Herein, we design a hierarchically microstructure-bioinspired flexible piezoresistive sensor consisting of a hierarchical polyaniline/polyvinylidene fluoride nanofiber (HPPNF) film sandwiched between two interlocking electrodes with microdome structure. Ascribed to the substantially enlarged 3D deformation rates, these bioelectronics exhibit an ultrahigh sensitivity of 53 kPa-1, a pressure detection range from 58.4 to 960 Pa, a fast response time of 38 ms, and excellent cycle stability over 50 000 cycles. Furthermore, this conformally skin-adhered sensor successfully demonstrates the monitoring of human physiological signals and movement states, such as wrist pulse, throat activity, spinal posture, and gait recognition. Evidently, this hierarchically microstructure-bioinspired and amplified sensitivity piezoresistive sensor provides a promising strategy for the rapid development of next-generation wearable bioelectronics.