Sensor System Research Articles

Cognitive behavioral therapy for pediatric obsessive-compulsive disorder delivered via internet videoconferencing: a manualized sensor-assisted feasibility approach

BackgroundBetween 1 and 4% of children and adolescents suffer from obsessive-compulsive disorder (OCD) worldwide, but the majority of these young people do not have access to cognitive behavioral therapy (CBT) as a first-line treatment. CBT delivered via online videoconferencing (vCBT) offers a new way to provide young people with therapy, especially in the home environment where symptoms usually occur.MethodsIn this study, we investigated the feasibility of a newly revised vCBT manual, symptom change during treatment, and effects on family life and social functioning. 20 patients with OCD, aged 12–18 years, were treated during 14 weekly sessions while using a multimodal sensor system that assessed their physiological and behavioral responses during therapy. Treatment was delivered in real time via an online videoconferencing platform. Measurements of feasibility, acceptance, and implementation were evaluated descriptively, and clinical measures were assessed with t tests.ResultsThe primary results showed that patients and parents perceived the manual-based vCBT as feasible and easy to understand. According to the therapists’ ratings, all treatment modules and the content could be carried out in accordance with the manual. As a secondary outcome, OCD symptoms improved significantly during treatment (p <.001, d = 1.87), revealed by an average decrease of more than half in the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) score. As the psychotherapy could be implemented directly in the patients’ home environment, low barriers to participation were reported, and the majority of participants reported improvements in family life after treatment.ConclusionsIn summary, the results of this feasibility study indicated a successful application of manual-based psychotherapy delivered via videoconferencing for pediatric OCD supported by a sensor system. This method should be further investigated in future randomized controlled trials with larger patient samples.Clinical trial registration: [www.ClinicalTrials.gov], identifier [NCT05291611], first submission: 2021-12-10.

Fully Automated and AI-Assisted Optical Fiber Sensing System for Multiplexed and Continuous Brain Monitoring.

Continuous and comprehensive brain monitoring is crucial for timely identification of changes or deterioration in brain function, enabling prompt intervention and personalized treatments. However, existing brain monitoring systems struggle to offer continuous and accurate monitoring of multiple brain biomarkers simultaneously. This study introduces a multiplexed optical fiber sensing system for continuous and simultaneous monitoring of six cerebrospinal fluid (CSF) biomarkers using tip-functionalized optical fibers and computational algorithms. Optimized machine learning models are developed and integrated for real-time spectra analysis, allowing for precise and continuous readout of biomarker concentrations. The developed machine learning-assisted fiber optic sensing system exhibits high sensitivity (0.04, 0.38, 0.67, 2.62, 0.0064, 0.33 I/I0 change per units of temperature, dissolved oxygen, glucose, pH, Na+, Ca2+, respectively), reversibility, and selectivity toward target biomarkers with a total diameter less than 2.5 mm. By monitoring brain metabolic and ionic dynamics, this system accurately identified brain physiology deterioration and recovery using ex vivo traumatic brain injury models. Additionally, the system successfully tracked biomarker fluctuations in clinical CSF samples with high accuracy (R2 > 0.93), demonstrating excellent sensitivity and selectivity in reflecting disease progression in real time. These findings underscore the enormous potential of automated and multiplexed optical fiber sensing systems for intraoperative and postoperative monitoring of brain physiologies.

Monitoring May 2024 solar and geomagnetic storm using broadband seismometers

The abrupt variations in the magnetic field due to the May 2024 geomagnetic storm have been recorded by broadband seismometers distributed around the world for a time interval of more than 55 h. Signals related to magnetic field variations can be identified in seismic data for frequencies below 10 mHz, but are clearer between 1.5 and 5 mHz, the frequency band corresponding to Pc5 magnetic pulsations. The number of seismological stations that detect these signals varies significantly between the various seismic networks analyzed, but in general they do not exceed 50–60%. The origin of these signals appears to be related to the interaction between telluric currents induced by magnetic field disturbances associated with the geomagnetic storm and the detection systems in broadband sensors, which measure the current intensity necessary to keep the mass of the seismometer fixed. Although detailed calibration of each seismometer will be necessary to use the seismic data to model the waveforms and amplitudes of the magnetic pulsations, the analysis of this data provides a significant densification with respect to the magnetic observatory data, as is of interest to analyze the temporal and regional evolution of the magnetics disturbances associated to the May 2024 solar storm.

Development and Field Deployment of a ppb-Level SO2/NO2 Dual-Gas Sensor System for Agricultural Early Fire Identification.

Sulfur dioxide (SO2) and nitrogen dioxide (NO2) are chemical indicators of crop straw combustion as well as significant atmospheric pollutants. It is challenging to promptly detect natural "wildfires" during agricultural production, which often lead to uncontrollable and substantial economic losses. Moreover, both "wildfires" and artificial "straw burning" practices pose severe threats to the ecological environment and human health. Consequently, developing sensors capable of rapid and high-precision quantitative analysis of SO2/NO2 is essential and urgent for detecting early fires in agricultural activities. Here, we demonstrate an incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) sensing system utilizing a 366 nm ultraviolet light emitting diode, designed for real-time, high-precision monitoring of SO2 and NO2 and is used for early fire detection validation. The optical resonant cavity is constructed within a 60 mm cage system mechanical structure, achieving a maximum optical path length of nearly 2 km with a length of ∼460 mm. The output light carrying information about the species and concentration of the analyte molecules is coupled into the miniaturized grating spectrometer via a fiber, and continuous spectral fitting and concentration inversion are performed on the computer. We propose a spectral analysis and concentration inversion model based on an improved particle swarm optimization-support vector machine (IPSO-SVM) algorithm. By discrimination of the absorption spectral characteristics of SO2/NO2, we achieve superior prediction accuracy. Experimental results indicate that the detection limits of SO2 and NO2 under the optimized averaging time are 77.5 parts per billion by volume (ppbv) and 0.037 ppbv, respectively. The field deployment of the sensor in scenarios such as continuous outdoor air pollution monitoring, in situ combustion feature identification, and early fire mobile detection has demonstrated the superior reliability and sensitivity of this sensor system.

Pseudologic Optical Circuit Method for Advanced Color Sensing in IGZO Phototransistor Arrays with Chlorophyll Absorption Layers.

Recently, the elimination of color filters has become a key focus in photodetector research because of the potential to create more compact and cost-effective sensor systems. In this study, a novel concept of a filter-free color-discrimination photosensor using an indium gallium zinc oxide (IGZO, In/Ga/Zn = 3.1:2.6:1.0)-based phototransistor with an integrated chlorophyll absorption layer (CAL) and a solution-processed oxide absorption layer (SAL) was developed. Chlorophyll, known for its role in photosynthesis as a natural light absorber, offers distinct characteristics compared to conventional photodetectors (i.e., SAL/IGZO), whereby the photoresponsivity decreases with increasing wavelength. Using the ability of chlorophyll to absorb blue and red light, the proposed CAL/IGZO phototransistor exhibited a higher photoresponsivity to red light than to green light. The device achieved a photoresponsivity of 1570 A/W for red light and 681 A/W for green light, with a photosensitivity of 8.35 × 105 and 8.96 × 104 and a detectivity of 8.47 × 1011 and 6.80 × 1010 Jones, respectively, under an illumination intensity of 1 mW/mm2. Furthermore, by integrating the proposed CAL/IGZO phototransistor with a SAL/IGZO phototransistor, which exhibited a different order of photoresponse across RGB wavelengths, an innovative color-discrimination pixel pseudologic circuit was successfully developed. The capability of this circuit to distinguish colors across various light intensities was validated through experimental data and SPICE simulations, with the output voltage ranges confirmed as -2.61 to -3.51 V for red, 1.56 to 2.69 V for green, and -0.22 to -0.68 V for blue over light intensities from 0.1 to 3 mW/mm2. This innovative approach allows effective color detection without conventional color filters, providing an advanced solution for photodetection technologies.

Integrating collaborative robots in manufacturing, logistics, and agriculture: Expert perspectives on technical, safety, and human factors

This study investigates the implementation of collaborative robots across three distinct industrial sectors: vehicle assembly, warehouse logistics, and agricultural operations. Through the SESTOSENSO project, an EU-funded initiative, we examined expert perspectives on human-robot collaboration using a mixed-methods approach. Data were collected from 31 technical experts across nine European countries through an online questionnaire combining qualitative assessments of specific use cases and quantitative measures of attitudes, trust, and safety perceptions. Expert opinions across the use cases emphasized three primary concerns: technical impacts of cobot adoption, social and ethical considerations, and safety issues in design and deployment. In vehicle assembly, experts stressed the importance of effective collaboration between cobots and exoskeletons to predict and prevent collisions. For logistics, they highlighted the need for adaptable systems capable of handling various object sizes while maintaining worker safety. In agricultural settings, experts emphasized the importance of developing inherently safe applications that can operate effectively on uneven terrain while reducing workers’ physical strain. Results reveal sector-specific challenges and opportunities: vehicle assembly operations require sophisticated sensor systems for cobot-exoskeleton integration; warehouse logistics demand advanced control systems for large object handling; and agricultural applications need robust navigation systems for uneven terrain. Quantitative findings indicate generally positive attitudes toward cobots, particularly regarding societal benefits, moderate to high levels of trust in cobot capabilities and favorable safety perceptions. The study highlights three key implications: (1) the need for comprehensive safety protocols tailored to each sector’s unique requirements, (2) the importance of user-friendly interfaces and intuitive programming methods for successful cobot integration, and (3) the necessity of addressing workforce transition and skill development concerns. These findings contribute to our understanding of human-robot collaboration in industrial settings and provide practical guidance for organizations implementing collaborative robotics while considering both technological advancement and human-centered design principles.

Assessment of Hydrological Resource Management Using Remote Sensing (RS) and Geographic Information Systems (GIS); Wadi Sannur Valley, Beni-Suef, Egypt.

Assessment of Hydrological Resource Management Using Remote Sensing (RS) and Geographic Information Systems (GIS); Wadi Sannur Valley, Beni-Suef, Egypt.

The association between foot morphology and foot loading during gait

The perceived association between foot type and injury risk has inspired the development of many foot typing methods. The purpose of this study was to determine how well different foot typing methods explained variations in regional foot loading in asymptomatic adults during gait, while considering age, sex, and walking speed as covariates. Six methods were used to determine foot type in 92 asymptomatic adults. An insole pressure sensor system measured peak force and force-time integral during treadmill walking at a self-selected pace. The foot was divided into nine regions and the association between foot type and foot loading was analyzed using generalized estimating equations (GEE) while accounting for the covariates. Over an average of 250 steps per individual, the results demonstrated variability in the association between regional foot loading and foot type. Specifically, Arch Index and X-Ray Arch Type correlated with hindfoot and midfoot loading, while Foot Posture Index correlated with medial midfoot, forefoot, and toe loading. Calcaneal Pitch and Meary’s Angle correlated with medial hindfoot, midfoot, and forefoot loading, while Varus/valgus angle correlated with loading in the forefoot and toes. The location and direction of associations were generally similar for Calcaneal Pitch, Meary’s Angle, and X-Ray Arch Type, and opposite of Arch Index. The analyses demonstrated that walking speed, age, and sex were all important factors that affected the relationship between plantar loading and foot type. Specific foot type methods correlate better with loading in specific foot regions, potentially making them better to evaluate injury risk in those regions.

Special Issue on Intelligent Sensor Systems for the IEEE Journal of Electron Devices

Special Issue on Intelligent Sensor Systems for the IEEE Journal of Electron Devices

The Effective Sorting Technologies for Reducing Aflatoxins in Nuts and Dried Figs in Turkey

Background: RASFF notifications serve to determine the hazards responsible for food and feed. In case of determining risks to human health, the information provided to member countries via the rapid alarm system at the same time warned us to take the necessary measures. Objectives: In this research, the RASFF portal alerts over the last three years related to aflatoxins in hazelnut, fig, and peanut products were examined. The impact of image processing technologies on lowering the danger of aflatoxins in shelled hazelnuts and dried figs was also investigated, as well as Critical Control Points (CCPs) on aflatoxin hazard. Methods: The RASFF database was used in the RASFF Window portal and analyzed for aflatoxin alerts. ISO 22000:2018 was used for hazard and risk analysis for raw hazelnut, peanut, and fig products. Aflatoxin analyses were performed according to the AOAC 2000/991.31 method. Prior to and after hazelnut sorting methods, levels of aflatoxin were measured. Besides, aflatoxin contamination in peanuts harvesting in 2023 years from three local producers in Turkey was studied. Results: The number of RASFF notifications for hazelnuts, peanuts/groundnuts, dried figs, and their products for the period of 03/01/2020 to 01/06/2023 were 84, 283 and 173, respectively. As a result of HACCP implementation, especially for aflatoxin hazard management, optical-laser-manual sorting and aflatoxin detection using UV light stages are accepted as CCPs. When nuts were sorted using optical and laser systems were detected decreasing total aflatoxin levels. The mean value of Aflatoxin B1(AFB1) and total aflatoxins were reduced from 7.80±1.08 and 13.56±1.06 to 3.63±0.39 and 7.21±0.71, respectively. 79.16% of all sorted samples were found to comply with legal limits. Investigated peanut samples for aflatoxin B1 contents were found between 5.63 and 27.75 µg kg-1 in 19.5%. Conclusion: The alerts based on aflatoxin contamination above legal limits have been carried on for nuts and dried figs. The physical sorting methods using optical selection and high-resolution laser sensor systems aflatoxin-contaminated nuts from healthy nuts applied for in-shell Turkish nut sorting were able to assess the quality of the batch studied, and the systems were assigned significantly effective to reduce aflatoxin contents. Food business operators in Turkey, which is among the countries exporting agricultural and food products to the European Union (EU), have tended to prefer the most advanced technologies. Efficacies of reduction of aflatoxin by different physical and other (such as imaging, X-ray and acoustic, audio signals and hyperspectral images of moldy natural nuts etc.) methods will be developed in the near future.

Enhancing Accuracy in Gas–Water Two-Phase Flow Sensor Systems Through Deep-Learning- Based Computational Framework

Enhancing Accuracy in Gas–Water Two-Phase Flow Sensor Systems Through Deep-Learning- Based Computational Framework

Fiber Optical Sensor for Antioxidant Detection Levels in Blueberries Using Wavelength-Based LED Light Sources

Blueberries' high antioxidant content, which includes flavonoids, anthocyanins, and phenolic acids, greatly enhances their nutritional and therapeutic value. These substances are crucial targets for food quality and nutrition research because they neutralize free radicals and lower oxidative stress. However, conventional techniques for quantifying antioxidants, such as High-Performance Liquid Chromatography (HPLC), are expensive, time-consuming, and frequently needed for destructive sample preparation. This study suggests a fiber-optical sensor (FOS) system as a unique, destructive method for identifying and measuring antioxidants in blueberries. Blueberry samples are illuminated by the system using wavelength-specific LED light sources, which are guided to and from the sample via fiber optics for interaction. Using the distinctive absorption and scattering characteristics of the compounds at particular wavelengths, the reflected or transmitted light is captured by a photodetector and evaluated to calculate antioxidant amounts. This proposed system is a useful tool for the food sector because of its many benefits, which include its capacity to offer real-time analysis, portability, and adaptability to in-field applications. This paper examines the sensor's design principles, assesses how well it performs in comparison to traditional techniques, and emphasizes how useful it could be for agricultural research and quality control.

An Innovative Approach for Improving Navigation Performance of Robust Land-Based Vehicles

The Extended Kalman Filter (EKF) stands as a prominent choice within navigation systems, particularly in scenarios involving the integration of a Reduced Inertial Sensor System (RISS) with the Global Positioning System (GPS). However, despite its widespread adoption, the EKF grapples with many challenges, including the propensity to underestimate filter uncertainties, contend with unreliable GPS signals, and confront errors stemming from linearization processes. These issues invariably contribute to a decline in overall system performance. Considering these challenges, this paper endeavors to introduce a groundbreaking integration algorithm to mitigate the inherent shortcomings of the EKF. The proposed algorithm employs innovative strategies to address these challenges comprehensively. Firstly, it incorporates a dynamic self-tuning mechanism meticulously designed to improve filter configuration in real-time, ensuring adaptability to varying operating conditions. The algorithm also integrates a meticulously engineered GPS Integrity algorithm to filter out mistaken readings and bolster the reliability of the navigation solution. Furthermore, the algorithm adopts the Unscented Kalman Filter (UKF), renowned for handling non-linearities directly, thereby cutting the need for the cumbersome linearization procedures inherent in the EKF. Comparative evaluations against the traditional EKF method prove the effectiveness of the proposed approach. Significant performance enhancements are evident using two datasets from a VTI SCC1300-D04 IMU unit compared to high-precision Novatel SPAN ground truth data. These improvements are quantified through RMSE analysis, showing substantial strides in navigation accuracy. Overall, the results underscore the transformative potential of the proposed integration algorithm in advancing navigation system capabilities.

A Hybrid Photoplethysmography (PPG) Sensor System Design for Heart Rate Monitoring

A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor.

StressFit: a hybrid wearable physicochemical sensor suite for simultaneously measuring electromyogram and sweat cortisol

This study introduces StressFit, a novel hybrid wearable sensor system designed to simultaneously monitor electromyogram (EMG) signals and sweat cortisol levels. Our approach involves the development of a noninvasive skin patch capable of monitoring skin temperature, sweat pH, cortisol levels, and corresponding EMG signals using a combination of physical and electrochemical sensors integrated with EMG electrodes. StressFit was optimized by enhancing sensor output and mechanical resilience for practical application on curved body surfaces, ensuring accurate acquisition of cortisol, pH, body temperature, and EMG data without sensor interference. In addition, we integrated an onboard data processing unit with Internet of Things (IoT) capabilities for real-time acquisition, processing, and wireless transmission of sensor measurements. Sweat cortisol and EMG signals were measured during cycling exercises to evaluate the sensor suite’s performance. Our results demonstrate an increase in sweat cortisol levels and decrease in the EMG signal’s power spectral density following exercise. These findings suggest that combining sweat cortisol levels with EMG signals in real-time could serve as valuable indicators for stress assessment and early detection of abnormal physiological changes.

Characterization and visualization of gas–liquid two-phase flow based on wire-mesh sensor

Gas–liquid two-phase flow is prevalent in both industrial production and the natural environment, making the exploration of its flow behavior critically important. In this study, we design a wire-mesh sensor and measurement system to conduct dynamic experiments on vertical upward gas–liquid two-phase flow, obtaining multi-channel data under various conditions. We develop an integrated limited penetrable visibility graph network (ILPVGNet) to quantitatively assess the dynamic transition from slug flow to bubble flow using network metrics. The results demonstrate that this approach effectively integrates multi-channel measurements, revealing the evolution dynamics and internal coupling mechanisms of gas–liquid two-phase flow. To better observe fine flow structures and details, we perform visualization and super-resolution analysis on the multi-channel data, significantly enhancing image clarity and restoring detailed features, thereby improving our ability to intuitively analyze complex gas–liquid flow behaviors.

Full-range displacement sensor with dual FBGs combined cantilever beam based on magnetic grating

We propose a full-range displacement sensor system based on double fiber Bragg gratings (FBGs) with a cantilever beam. This sensor adopts a magnetic scale as a unique transmission mechanism. By combining a simple and low-cost cantilever beam structure makes the sensing probe very easy to realize. The optimal magnetic gap was explored through multiple sets of numerical simulations. The experiment proved the sine relationship between the center wavelength shifts of FBGs and the linear displacement, proving the feasibility of this method. Results indicate that the amplitude of the tensile compression load of FBGs are 169.76 με and 296.12 με. The fitting linearity based on sinusoidal function at an air gap of 3.5 mm is 0.9663 and 0.9566. The direction of motion can be determined by the phase difference between two FBGs. Moreover, the difference of double FBGs can realize the effect of temperature compensation. Thus, this sensor can achieve non-contact, temperature-independent, and full-range measurements. It can also be exploited to measure other parameters such as angular velocity, acceleration, and magnetic field strength.

Assessing the Validity of a Wearable Shoulder Motion Tracking System Through Comparison with Dartfish in Patients Undergoing Shoulder Joint Replacement Surgery.

Objective assessments of shoulder motion are paramount for effective rehabilitation and evaluation of surgical outcomes. Inertial Measurement Units (IMU) have demonstrated promise in providing unbiased movement data. This study is dedicated to evaluating the concurrent construct validity and accuracy of a wearable IMU-based sensor system, called "Motion Shirt", for the assessment of shoulder motion arcs in patients awaiting shoulder replacement surgery. This evaluation was conducted by comparing Motion Shirt data with the Dartfish Motion Analyzer software during the Functional Impairment Test-Hand and Neck/Shoulder/Arm (FIT-HaNSA) test.&#xD;Thirteen patients (age>50), who were awaiting shoulder replacement surgery, were recruited. The Motion Shirt was employed to measure angular shoulder movements in two planes during the FIT-HaNSA test. Simultaneously, two cameras recorded the participants' movements to provide reference data. Bland-Altman plots were generated to visualize agreement between the Motion Shirt and the reference data obtained from the Dartfish Motion Analyzer software. &#xD;The data analysis on Bland-Altman plots revealed a substantial level of agreement between the Motion Shirt and Dartfish analysis in measuring shoulder motion. In Task-1, no significant systematic errors were exhibited, with only 3.27% and 2.18% of points exceeding the limits of agreement (LOA) in both elevation and the Plane of Elevation (POE), signifying a high level of concordance. In Task-2, a high level of agreement was also observed in Elevation, with only 3.8% of points exceeding the LOA. However, 5.98% of points exceeded LOA in POE for Task-2. In Task-3, focused on sustained overhead activity, the Motion Shirt showed strong agreement with Dartfish in Elevation (2.44% points exceeded LOA), but in POE, 7.32% points exceeded LOA.&#xD;The Motion Shirt demonstrated a robust concordance with Dartfish Motion Analyzer system in assessing shoulder motion during the FIT-HaNSA test. These results affirm the Motion Shirt's suitability for objective motion analysis in patients awaiting shoulder replacement surgery.&#xD.

Comparison of WP-2 and MOCNESS plankton samplers for measuring zooplankton biomass in the Barents Sea ecosystem

Abstract Zooplankton in the Barents Sea has been monitored on an annual autumn survey since the late 1980s, using vertical WP-2 and oblique Multiple Opening and Closing Net and Environmental Sensing System (MOCNESS) tows over the water column. Sampling with MOCNESS is used to describe the vertical distribution and more frequent sampling with WP-2 (~3:1) to describe the horizontal distribution. We use here a large cumulative data set of 874 MOCNESS and 2850 WP-2 stations with data on size-fractioned dry-weight biomass to compare the two zooplankton sampling gears. MOCNESS is consistently collecting more biomass of the large size fraction (&amp;gt;2 mm screen size) by ~20% and less of the small fraction (&amp;lt;1 mm) by ~30% compared to WP-2. This is interpreted to reflect more extrusion of small plankton and less avoidance by larger plankton with the MOCNESS. The data set has been collected by three research vessels. There was a difference in vertical speed in oblique tows of MOCNESS among the ships but no clear effect on volume filtered per unit time. This demonstrates operational consistency and suggests the use of a constant flow factor (distance per flowmeter count) when calculating results over the time series. The issue of calibration of traditional flowmeters on oblique tows needs further examination.

Self-adhesive, stretchable, anti-freezing conductive organohydrogels with fast gelation from catechol-metal ion self-catalytic system for flexible strain sensors

Conductive hydrogels have attracted tremendous attention in flexible sensors due to their flexibility, durability, and multifunctionality. However, time and energy-consumption fabrication process and intrinsic instability in extreme environments severely limit their practical implementations. Herein, a universal and facile synergetic self-catalytic system based on catechol-based molecules and metal ions has been developed to the fast gelation (≈3s) of conductive organohydrogels in water–ethylene glycol (EG) binary solvent, which exhibits excellent stretchability (up to 630 % elongation), satisfactory self-adhesion (up to 16.3 kPa), and extreme environment applicability (−80 °C to 45 °C). This dual self-catalytic system consists of tannic acid (TA) and ferric ions (Fe3+), which form stable redox pairs to activate ammonium persulfate to generate free radicals, rapidly initiating the polymerization of monomers. Furthermore, the introduction of H2O/EG binary solvent not only facilitates the dispersion of components to improve the mechanical performance of organohydrogels, but also the generation of abundant hydrogen bonds between EG and water molecules endows extreme freezing drying resistance, and enhances self-adhesion for organohydrogels. The organohydrogels showing high sensitivity toward tensile deformation are assembled into flexible strain sensors to detect human motions with high sensitivity, exceptional stability, and excellent durability, which holds great promise in flexible electronics.