Personal Monitoring Research Articles

Personal exposure to fine particulate matter (PM2.5) and self-reported asthma-related health

PM2.5 (fine particulate matter ≤2.5 μm in diameter) is a key pollutant that can produce acute asthma exacerbations and longer-term deterioration of respiratory health. Individual exposure to PM2.5 is unique and varies across microenvironments. Low-cost sensors (LCS) can collect data at a spatiotemporal resolution previously unattainable, allowing the study of exposures across microenvironments. The aim of this study is to investigate the acute effects of personal exposure to PM2.5 on self-reported asthma-related health.Twenty-eight non-smoking adults with asthma living in Scotland collected PM2.5 personal exposure data using LCS. Measurements were made at a 2-min time resolution for a period of 7 days as participants conducted their typical daily routines. Concurrently, participants were asked to keep a detailed time-activity diary, logging their activities and microenvironments, along with hourly information on their respiratory health and medication use. Health outcomes were modelled as a function of hourly PM2.5 concentration (plus 1- and 2-h lag) using generalized mixed-effects models adjusted for temperature and relative humidity.Personal exposures to PM2.5 varied across microenvironments, with the largest average microenvironmental exposure observed in private residences (11.5 ± 48.6 μg/m3) and lowest in the work microenvironment (2.9 ± 11.3 μg/m3). The most frequently reported asthma symptoms, wheezing, chest tightness and cough, were reported on 3.4%, 1.6% and 1.6% of participant-hours, respectively. The odds of reporting asthma symptoms increased per interquartile range (IQR) in PM2.5 exposure (odds ratio (OR) 1.29, 95% CI 1.07–1.54) for same-hour exposure. Despite this, no association was observed between reliever inhaler use (non-routine, non-exercise related) and PM2.5 exposure (OR 1.02, 95% CI 0.71–1.48).Current air quality monitoring practices are inadequate to detect acute asthma symptom prevalence resulting from PM2.5 exposure; to detect these requires high-resolution air quality data and health information collected in situ. Personal exposure monitoring could have significant implications for asthma self-management and clinical practice.

Condensation particle counters: Exploring the limits of miniaturisation

The measurement of airborne particles continues to attract increased interest for the detection and characterisation of air pollution, emissions, fire detection, occupational and climate impacts. Concerted efforts have reduced the size and cost of optical counters that have enabled widespread sensor deployment. Currently the World Health Organisation (WHO) enforces particle regulations that are mass-based which has resulted in an influx of low-cost portable optical particle counters (OPCs) calibrated to mass standards and widely utilised for research and personal monitoring. However, OPCs are incapable of measuring particles with diameters dp≲ 300 nm which misses ultrafine particles (≤ 100 nm) that often represent the majority of particles by number concentration. Condensation particle counters (CPCs) capable of measuring ultrafine particle number concentration with high instrument sensitivity, accuracy and precision have recently been targeted for miniaturisation.Herein, we explore the limitations of the miniaturisation of a CPC growth chamber (CPC-GC). Results presented in this work use water, but equivalent expressions can be obtained for other working fluids or design criteria using the same methods. Scaling analysis considers phenomenological constraints of supersaturation, heat transfer, particle penetration and droplet growth to establish limits during miniaturisation.The results showed that optimal thermal and saturation conditions are similar where the optimum device aspect ratio is L∗≥Lmin,s∗= 1.68 Re + 7.98 and L∗≥Lmin,th∗= 1.78 Re + 8.02 respectively. L∗ is the non-dimensional aspect ratio and Lmin∗ is the minimum non-dimensional device length required as a function of the flow Reynolds number Re. Higher Reynolds numbers lead to a convection-dominated regime, where the saturation and temperature profiles take a parabolic shape. As the Reynolds number approaches unity, the reduced mass and thermal Péclet numbers result in vapour and heat diffusing upstream of the condenser, creating ellipsoidal saturation and temperature profiles. To achieve a satisfactory performance, the heat and mass transport within the CPC-GC must be convection-dominated, leading to a minimum value of Re ≳2. Desired penetration P≥0.9 for particles with an initial diameter dpinit=10nm is achieved by designs with an aspect ratio, L∗≤Lmax,p∗= 2.5 Re. Together, these constraints define an allowable range of aspect ratios for a CPC-GC as a function of the Reynolds number.The final size of the grown droplets can be controlled by varying the interaction residence time τint, defined as the time a particle spends in the supersaturation profile. Having defined a target final droplet size for optical detection, and hence having fixed τint, the aspect ratio necessary to achieve the desired growth is a function of the Reynolds number and of the dimensional radius of the CPC-GC (Ricpc) according to: Lgrowth∗=γRe/(Ricpc)2 with γ= 2τintν, where ν is the kinematic viscosity of air. Selecting a value of Ricpc such that the line for Lgrowth∗ falls in the centre of the allowable operating region defined above will optimise the dimensional CPC-GC for the selected design criteria.Limits from this model were used to realising a prototype successfully tested experimentally against a reference commercial CPC within a laboratory environment. The outcome of the present work enables designers and researchers with a toolkit to optimise the next generation of high performance miniaturised CPC-GC designs.

A low-cost methodology for developing personal health monitoring devices to examine psychological states and the impact of exercises

A low-cost methodology for developing personal health monitoring devices to examine psychological states and the impact of exercises

Recodification of Slovak Criminal Proceedings: Early Ideas, Concrete Steps and Its Subsequent Application

The article deals with recodification of Slovak criminal proceedings. It is divided into three sections. The first section is focused on the early idea of recodification of Slovak national criminal law and the introduction of innovations of national criminal proceedings. While the second section is focused on adoption of new Criminal Proceeding Code No. 301/2005 Zb. and understanding of “redesigned” criminal proceedings, the third section is focused on necessary improvement of criminal proceedings in the light of the electronic monitoring of persons.

Estimating exposures from spray products using robotic simulations.

There is an increasing need for exposure data to enable more precise information for risk estimates and improved public health protection. While personal monitoring data are preferred, it is often difficult to collect due to the resources needed to complete a human research study. In this study, we successfully programmed a robotic arm to mimic human use (spraying) of a fabric crafts protector (FCP) and human cleaning (spraying and wiping) of a glass pane with glass cleaner (GC). The robot was then used in place of human subjects to assess inhalation exposures to volatile organic compounds (VOCs) during the use of the FCP and GC. Air sampling data were collected while the robot used the products to estimate personal exposures to VOCs. Average VOC concentrations were 1.57 ppm for FCP spraying and 0.17 ppm for GC spraying and wiping. During FCP spraying, average acetone concentrations were 0.88 ppm and average isopropyl alcohol concentrations were 0.26 ppm. During GC spraying and wiping, average 2-butoxyethanol concentrations were 0.15 ppm. Air sampling data were found to be within the range of data reported in the literature during human use of similar glass cleaning products. No data was found in the literature during use of fabric protector spray products. This study contributes exposure measurement data with detailed contextual information to help characterize inhalation exposures during the use of 2 spray products. In addition, the study offers a systematic, efficient method for generating exposure data which can be used to improve health and safety risk assessments used for public health protection.

Dynamic human exposure to airborne bacteria-associated antibiotic resistomes revealed by longitudinal personal monitoring data

Airborne antibiotic-resistant bacteria (ARB) can critically impact human health. We performed resistome profiling of 283 personal airborne exposure samples from 15 participants spanning 890 days and 66 locations. We found a greater diversity and abundance of airborne bacteria community and antibiotic resistomes in spring than in winter, and temperature contributed largely to the difference. A total of 1123 bacterial genera were detected, with 16 genera dominating. Of which, 7/16 were annotated as major antibiotic resistance gene (ARG) hosts. The participants were exposed to a highly dynamic collection of ARGs, including 322 subtypes conferring resistance to 18 antibiotic classes dominated by multidrug, macrolide-lincosamide-streptogramin, β-lactam, and fosfomycin. Unlike the overall community-level bacteria exposure, an extremely high abundance of specific ARG subtypes, including lunA and qacG, were found in some samples. Staphylococcus was the predominant genus in the bacterial community, serving as a primary bacterial host for the ARGs. The annotation of ARG-carrying contigs indicated that humans and companion animals were major reservoirs for ARG-carrying Staphylococcus. This study contextualized airborne antibiotic resistomes in the precision medicine framework through longitudinal personal monitoring, which can have broad implications for human health.

Simulating the impact of particulate matter exposure on health-related behaviour: A comparative study of stochastic modelling and personal monitoring data.

Epidemiological and exposure studies concerning particulate matter (PM) often rely on data from sparse governmental stations. While low-cost personal monitors have some drawbacks, recent developments have shown that they can provide fairly accurate and fit-for-purpose data. Comparing a stochastic, i.e., agent-based model (ABM), with environmental, biometric and activity data, collected with personal monitors, could provide insight into how the two approaches assess PM exposure and dose. An ABM was constructed, simulating a PM exposure/dose assessment of 100 agents. Their actions were governed by inherent probabilities of performing an activity, based on population data. Each activity was associated with an intensity level, and a PM pollution level. The ABM results were compared with real-world results. Both approaches had comparable results, showing similar trends and a mean dose. Discrepancies were seen in the activities with the highest mean dose values. A stochastic model, based on population data, does not capture well some specifics of a local population. Combined, personal sensors could provide input for calibration, and an ABM approach can help offset a low number of participants. Implementing a function of agents influencing others transport choice, increased the importance of cycling/walking in the overall dose estimate. Activists, agents with an increased transport influence, did not play an important role at low PM levels. As concentrations rose, higher shares of activists (and their influence) caused the dose to increase. Simulating a person's PM exposure/dose in different scenarios and activities in a virtual environment provides researchers and policymakers with a valuable tool.

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.

Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.

Merging Smart and Healthy Cities to Support Community Wellbeing and Social Connection

Urban planning has long pursued the improvement of health and wellbeing through the rapidly evolving scholarship and practice of health-supportive environments, underpinned by the seminal World Health Organization’s Healthy Cities Framework. Although a much more recent development, technology has been informing urban planning, as well as advancing healthcare and personal wellbeing monitoring and assessment. Known as the Smart City movement, it has much to offer regarding life in towns and cities, as well as how they are managed, maintained, and developed. There is also a growing appreciation of the potential for smart city technology to enhance human and environmental health in the context of urban planning and public place making. This has been reinforced by the COVID-19 pandemic with its reawakening of community interest in health and wellbeing, including mental illness, a greater awareness of the importance of local environments, and an explosion of technological knowhow in the embrace of remote working, online shopping, and education. Using the example of the authors’ “Smart Social Spaces” project, this entry discusses the potential benefits of an evolving integrative concept called “Smart Healthy Social Spaces”. The aim is to support community wellbeing as part of everyday living, especially associated with social connection, in densely populated and culturally diverse urban environments, where locally situated public spaces are increasingly important for all citizens.

Evaluating the Clinical Effect of Personal Continuous Glucose Monitoring in a Diverse Population With Type 2 Diabetes.

Objective: To determine the clinical effect of personal continuous glucose monitoring (CGM) in a diverse population with type 2 diabetes (T2D). Research Design and Methods: A report was created from the electronic health record identifying adults prescribed CGM at an urban family medicine clinic between January 1, 2019, and February 23, 2022. An "index date" was identified as the start of CGM. The closest hemoglobin A1c (A1c) 6 months or more after the index date was identified as the "follow-up date." The primary outcome of this study was to compare the percentage of individuals meeting the MN Community Measure (MNCM) D5 HbA1c goal of <8% at the follow-up date versus the index date. Results: Seventy-two patients were identified after the exclusion criteria were applied. Approximately one-third of patients required utilization of an interpreter and 76% of patients were of a racial or ethnic minority. The mean HbA1c prior to CGM use was 9.8%, with 16.7% of the population meeting the MNCM D5 A1c goal of <8%. At the follow-up date, the mean A1c was 8.4% (mean difference -1.4%; p < 0.001), with 41.7% of the population meeting goal (mean difference +25%; p < 0.001). Subgroup analyses affirm that the results of the primary outcome were sustained despite insulin use status. Conclusion: A diverse population with T2D had a significant reduction in A1c and was more likely to meet the MNCM D5 A1c goal of <8% after an average of 6 months using personal CGM.

(Invited) wearable Hydrogel Patch with Noninvasive, Electrochemical Glucose Sensor for Natural Sweat Detection

Recent advances in microelectronics and electrochemical sensing platforms have preceded the development of devices for personal monitoring and managing physiological and metabolic information that exploit sweat as a noninvasive, convenient approach for providing information about underlying health conditions, such as glucose level monitoring. Although most sweat glucose sensors have targeted applications during exercise and other active stimulation induced-sweat, natural sweating offers an attractive alternative with minimal effect on users that can be accessed during routine and sedentary activities without impeding personal lifestyle and preserves the correlation between blood and sweat glucose. Here, we present a noninvasive sweat glucose sensor with convenient hydrogel patches for rapid sampling of natural perspiration without external activities that stimulate sweating. The wearable hydrogel patch rapidly takes up natural sweat from the hand and serves as a medium for electrochemical sensing. A prussian blue-doped poly(3,4-ethylenedioxythiophene nanocomposite (PB-PEDOT NC) electrode provides cost-effective, stable and excellent electrocatalytic activity in sweat glucose measurements. We demonstrated sweat glucose sensor functionality by long-term measurements of glucose in sweat from human subjects consuming food and drinks. By enabling the analysis of sweat glucose during routine and sedentary activities, the sweat glucose sensor shows great promise for clinical-grade glucose management and enlarges the scope of next-generation noninvasive sensing systems.

The importance of time-resolved personal Dosimetry in space: The ISS Crew Active Dosimeter

Monitoring space radiation is of vital importance for risk reduction strategies in human space exploration. Radiation protection programs on Earth and in space rely on personal and area radiation monitoring instruments. Crew worn radiation detectors are crucial for successful crew radiation protection programs since they measure what each crewmember experiences in different shielding configurations within the space habitable volume. The Space Radiation Analysis Group at NASA Johnson Space Center investigated several compact, low power, real-time instruments for personal dosimetry. Following these feasibility studies, the Crew Active Dosimeter (CAD) has been chosen as a replacement for the legacy crew passive radiation detectors. The CAD device, based on direct ion storage technology, was developed by Mirion Dosimetry Services to meet the specified NASA design requirements for the International Space Station (ISS) and Artemis programs. After a successful Technology demonstration on ISS, the CAD has been implemented for ISS Crew operations since 2020. The current paper provides an overview of the CAD development, ISS results and comparison with the ISS Radiation Assessment Detector (RAD) and the Radiation Environment Monitor 2 (REM2) instruments.

Nanodroplet-Based Super-Resolution Ultrasound Localization Microscopy.

Over the past decade, super-resolution ultrasound localization microscopy (SR-ULM) has revolutionized ultrasound imaging with its capability to resolve the microvascular structures below the ultrasound diffraction limit. The introduction of this imaging technique enables the visualization, quantification, and characterization of tissue microvasculature. The early implementations of SR-ULM utilize microbubbles (MBs) that require a long image acquisition time due to the requirement of capturing sparsely isolated microbubble signals. The next-generation SR-ULM employs nanodroplets that have the potential to significantly reduce the image acquisition time without sacrificing the resolution. This review discusses various nanodroplet-based ultrasound localization microscopy techniques and their corresponding imaging mechanisms. A summary is given on the preclinical applications of SR-ULM with nanodroplets, and the challenges in the clinical translation of nanodroplet-based SR-ULM are presented while discussing the future perspectives. In conclusion, ultrasound localization microscopy is a promising microvasculature imaging technology that can provide new diagnostic and prognostic information for a wide range of pathologies, such as cancer, heart conditions, and autoimmune diseases, and enable personalized treatment monitoring at a microlevel.

Patient Monitoring for Personalized Mobile Health (PMH) based on Medical Virtual Instruments

One of the newest technologies, mobile health, has the potential to support the provision of care for older adults and offer them individualised treatment. This study’s goal is to evaluate the benefits and challenges of personalised mobile health (PMH) for elderly residential care. Virtual worlds are quickly integrating into the landscape of instructional technologies. One of the most well-known of these settings is Second Life (SL). Despite the potential of SL for health professions education, there aren’t many official SL applications for this purpose, and the effectiveness of these applications hasn’t been evaluated to the fullest extent possible. Similarly, it appears that nothing is known about the use of virtual worlds for continuing medical education. In order to better grasp the fundamentals of the aid of MVIs for personal health monitoring (PHM), we were able to pinpoint the key disease regions, sensors, channels, calculations and communication protocols. The main obstacles limiting MVIs’ degree of integration into the international health care system were also identified. The analysis demonstrates that MVIs offer an excellent possibility for the creation of affordable, personalised health systems that meet the unique equipment requirements of a certain field of medicine.

Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection Approach to Aid Elderly People

Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.

Flexible MXene decorative nonwovens with patterned structures for integrated joule heating and strain sensing

Multifunctional textile-based wearable electronics require reliable mechanical flexibility, excellent breathability. Therefore, the manufacture of electronic products with the above characteristics is of great significance for multifunctional, physical health care, and personalized medicine. Herein, we fabricated a wearable heater by depositing MXene uniformly on the patterned thermoplastic polyurethane (TPU) fiber membrane. The unique patterned junctions of the fiber membrane not only guarantee excellent air permeability for the overall structure but also enable MXene to form more stable conductive pathways. The prepared fiber film has a stable electrothermal response and a good Joule heating effect. It can reach 44 ° C instantaneously at a 2 V voltage, and the duration was far more than the 1400 s. The temperature can reach 150 ° C at 5 V voltage, to achieve the purpose of higher temperature at low voltage and low MXene content. In addition, the fiber film also has the photothermal conversion effect, which can reach 32.4 ° C when heated by light for 100 s, and the light-off temperature can be rapidly reduced to room temperature after the 40 s. In addition, it also has the function of monitoring human movement. This study provides multifunctional wearable electronics for personal medical and health monitoring and personal thermal management, because of the uniform loading of MXene and excellent mechanical properties and air permeability provided by the patterned structure. In conclusion, we expect that this work will be beneficial to improve the Joule heating performance of smart wearable devices.

Trend and distribution analysis of occupational radiation exposure among medical practices in Chongqing, China (2008-2020).

The personal dose levels of medical radiation workers in Chongqing from 2008 to 2020 were investigated and analysed. The results showed that a total of 68379 people were monitored from 2008 to 2020. The number of radiation workers increased year by year, and the number of female radiation workers increased rapidly. The monitoring data were analysed by Mann-Whitney test, Mann-Kendall test and Bonferroni method. The annual mean effective dose from 2008 to 2020 showed a decreasing trend (P<0.0001). At the 5% significance level, six occupational categories showed a significant decreasing trend (P<0.0001).The average annual effective dose for medical radiation workers in Chongqing in 2020 was 0.4482mSv and 94.73% of radiation workers received annual doses less than the public dose limit (1mSv). Personal dose monitoring results for most radiation workers were low. The protection of radiation workers in interventional radiology and nuclear medicine should be concerned.

Molecular residual disease detection in resected, muscle-invasive urothelial cancer with a tissue-based comprehensive genomic profiling-informed personalized monitoring assay.

Circulating tumor DNA (ctDNA) detection postoperatively may identify patients with urothelial cancer at a high risk of relapse. Pragmatic tools building off clinical tumor next-generation sequencing (NGS) platforms could have the potential to increase assay accessibility. We evaluated the widely available Foundation Medicine comprehensive genomic profiling (CGP) platform as a source of variants for tracking of ctDNA when analyzing residual samples from IMvigor010 (ClinicalTrials.gov identifier NCT02450331), a randomized adjuvant study comparing atezolizumab with observation after bladder cancer surgery. Current methods often involve germline sampling, which is not always feasible or practical. Rather than performing white blood cell sequencing to filter germline and clonal hematopoiesis (CH) variants, we applied a bioinformatic approach to select tumor (non-germline/CH) variants for molecular residual disease detection. Tissue-informed personalized multiplex polymerase chain reaction-NGS assay was used to detect ctDNA postsurgically (Natera). Across 396 analyzed patients, prevalence of potentially actionable alterations was comparable with the expected prevalence in advanced disease (13% FGFR2/3, 20% PIK3CA, 13% ERBB2, and 37% with elevated tumor mutational burden ≥10 mutations/megabase). In the observation arm, 66 of the 184 (36%) ctDNA-positive patients had shorter disease-free survival [DFS; hazard ratio (HR) = 5.77; 95% confidence interval (CI), 3.84-8.67; P < 0.0001] and overall survival (OS; HR = 5.81; 95% CI, 3.41-9.91; P < 0.0001) compared with ctDNA-negative patients. ctDNA-positive patients had improved DFS and OS with atezolizumab compared with those in observation (DFS HR = 0.56; 95% CI, 0.38-0.83; P = 0.003; OS HR = 0.66; 95% CI, 0.42-1.05). Clinical sensitivity and specificity for detection of postsurgical recurrence were 58% (60/103) and 93% (75/81), respectively. We present a personalized ctDNA monitoring assay utilizing tissue-based FoundationOne® CDx CGP, which is a pragmatic and potentially clinically scalable method that can detect low levels of residual ctDNA in patients with resected, muscle-invasive bladder cancer without germline sampling.

A portable aptasensor for facile personalized monitoring of serum uric acid

Uric acid (UA) is an important antioxidant to balance the oxidative stress of human body. Although UA exists mainly in human urine, serum UA is much more stable and is of higher clinical value. Since UA is associated with multiple diseases, methods capable of rapid and accurate detection of UA are of great importance for personalized health monitoring. Herein, a dual-FRET aptasensor with low signal background was developed for serum UA detection. It exhibited high selectivity against small molecules with similar redox potential or structures, as well as the main components in serum. For clinical serum samples analysis, the results by the proposed aptasensor were in good agreement with those by the hospital assay. To facilitate personalized health monitoring, a 3D-printed device was developed for the aptasensor, and an RGB-based cell phone App was designed for signal readout. The portable aptasensor could detect serum UA within 10 min, and the results for serum samples were also in consistent with the hospital assay. Moreover, with the portable aptasensor, it was confirmed that serum UA was more stable than urinary UA, and thus was more clinically relevant for personalized diagnosis.

Metal-organic framework and molybdenum oxide doped highly negative hybridized triboelectric material for self-powered and continuous monitoring of biosignals

Self-powered wearable sensors have broad applications in biomotion and personal healthcare monitoring. However, poor sensing abilities, low power outputs, and limited material selection have significantly constrained their advancement. Herein, a novel orthorhombic molybdenum oxide (α-MoO3) nanowire-doped hybridized metal–organic framework (MOF)-525@silicone@α-MoO3 was prepared and used as a highly negative triboelectric material to construct a triboelectric nanogenerator (TENG) for the first time. The prepared MOF-525@α-MoO3 enhanced the capacitance and provided strong charge accumulation owing to the synergistic effect between the heterostructure network and improved dielectric properties of the nanocomposite. In addition, hybridized tribo-materials increased the charge trapping and charge-inducing ability by including a large internal surface area; thus, they were able to generate a higher negative charge. With the assistance of microstructure modification using a three-dimensional printed template, the resulting TENG exhibited ultrahigh sensitivity (105.18 V/kPa) and power density (18.38 W/m2). TENG can power small electronics as a renewable energy source. A self-powered wearable biomotion sensor based on a TENG was developed for real-time, noninvasive skin-level biomotion sensing and joint-associated activity monitoring. Furthermore, an intelligent respiration and pulse monitoring system was demonstrated using an ultrahigh-stretchability (250 %) TENG. By combining the signal processing unit with TENG, we demonstrated the practical advantages of the device in sensing electromyography signals via smartphone applications and predicting their extensive use in biomedical and personal healthcare.