Goal-directed therapy guided by the FloTrac sensor in major surgery: a systematic review and meta-analysis.

OBJECTIVE: To provide insights into the potential benefits of goal-directed therapy guided by FloTrac in reducing postoperative complications and improving outcomes. METHODS: We performed a systematic review and meta-analysis of randomized controlled trials to evaluate goal-directed therapy guided by FloTrac in major surgery, comparing goal-directed therapy with usual care or invasive monitoring in cardiac and noncardiac surgery subgroups. The quality of the articles and evidence were evaluated with a risk of bias tool and GRADE. RESULTS: We included 29 randomized controlled trials with 3,468 patients. Goal-directed therapy significantly reduced the duration of hospital stay (mean difference -1.43 days; 95%CI 2.07 to -0.79; I2 81%), intensive care unit stay (mean difference -0.77 days; 95%CI -1.18 to -0.36; I2 93%), and mechanical ventilation (mean difference -2.48 hours, 95%CI -4.10 to -0.86, I2 63%). There was no statistically significant difference in mortality, myocardial infarction, acute kidney injury or hypotension, but goal-directed therapy significantly reduced the risk of heart failure or pulmonary edema (RR 0.46; 95%CI 0.23 - 0.92; I2 0%). CONCLUSION: Goal-directed therapy guided by the FloTrac sensor improved clinical outcomes and shortened the length of stay in the hospital and intensive care unit in patients undergoing major surgery. Further research can validate these results using specific protocols and better understand the potential benefits of FloTrac beyond these outcomes.

Bioreactance assessment of cardiac output lacks reliability for the follow-up of patients with pulmonary hypertension.

Cardiac output (CO) is one of the primary prognostic factors evaluated during the follow-up of patients treated for pulmonary hypertension (PH). It is recommended that it be measured using the thermodilution technique during right heart catheterization. The difficulty to perform iterative invasive measurements on the same individual led us to consider a non-invasive option. The aims of the present study were to assess the agreement between CO values obtained using bioreactance (Starling™ SV) and thermodilution, and to evaluate the ability of the bioreactance monitor to detect patients whose CO decreased by more than 15% during follow-up and, accordingly, its usefulness for patient monitoring. A prospective cohort study evaluating the performance of the Starling™ SV monitor was conducted in patients with clinically stable PH. Sixty patients referred for hemodynamic assessment were included. CO was measured using both the thermodilution technique and bioreactance during two follow-up visits. A total of 60 PH patients were included. All datasets were available at the baseline visit (V0) and 50 of them were usable during the follow-up visit (V1). Median [IQR] CO was 4.20 l/min [3.60-4.70] when assessed by bioreactance, and 5.30 l/min [4.57-6.20] by thermodilution (p<0.001). The Spearman correlation coefficient was 0.51 [0.36-0.64], and the average deviation on Bland-Altman plot was -1.25 l/min (95% CI [-1.48-1.01], p<0.001). The ability of the monitor to detect a variation in CO of more than 15% between two follow-up measurements, when such variation existed using thermodilution, was insufficient for clinical practice (AUC = 0.54, 95% CI [0.33-0.75]).

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series.

INTRODUCTION: Diabetic foot ulcers (DFU) are a devastating complication of diabetes. There are numerous challenges with preventing diabetic foot complications and barriers to achieving the care processes suggested in established foot care guidelines. Multi-faceted digital health solutions, which combine multimodal sensing, patient-facing biofeedback, and remote patient monitoring (RPM), show promise in improving our ability to understand, prevent, and manage DFUs. METHODS: Patients with a history of diabetic plantar foot ulcers were enrolled in a prospective cohort study and equipped with custom sensory insoles to track plantar pressure, plantar temperature, step count, and adherence data. Sensory insole data enabled patient-facing biofeedback to cue active plantar offloading in response to sustained high plantar pressures, and RPM assessments in response to data trends of concern in plantar pressure, plantar temperature, or sensory insole adherence. Three non-consecutive case participants that ultimately presented with pre-ulcerative lesions (a callus and/or erythematous area on the plantar surface of the foot) during the study were selected for this case series. RESULTS: Across three illustrative patients, continuous plantar pressure monitoring demonstrated promise for empowering both the patient and provider with information for data-driven management of pressure offloading treatments. CONCLUSION: Multi-faceted digital health solutions can naturally enable and reinforce the integrative foot care guidelines. Multi-modal sensing across multiple physiologic domains supports the monitoring of foot health at various stages along the DFU pathogenesis pathway. Furthermore, digital health solutions equipped with remote patient monitoring unlock new opportunities for personalizing treatments, providing periodic self-care reinforcement, and encouraging patient engagement-key tools for improving patient adherence to their diabetic foot care plan.

Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform.

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors.

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring.

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.

Subcranial Encephalic Temnograph-Shaped Helmet for Brain Stroke Monitoring.

In this contribution, a wearable microwave imaging system for real-time monitoring of brain stroke in the post-acute stage is described and validated. The system exploits multistatic/multifrequency (only 50 frequency samples) data collected via a low-cost and low-complexity architecture. Data are collected by an array of only 16 antennas moved by pneumatic system. Phantoms, built from ABS material and filled with appropriate Triton X-100-based mixtures to mimic the different head human tissues, are employed for the experiments. The microwave system exploits the differential scattering measures and the Incoherent MUSIC algorithm to provide a 3D image of the region under investigation. The shown results, although preliminary, confirm the potential of the proposed microwave system in providing reliable results, including for targets whose evolution is as small as 16 mL in volume.

Advancements in Polymer-Assisted Layer-by-Layer Fabrication of Wearable Sensors for Health Monitoring.

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy.

Shoulder pain represents the most frequently reported musculoskeletal disorder, often leading to significant functional impairment and pain, impacting quality of life. Home-based rehabilitation programs offer a more accessible and convenient solution for an effective shoulder disorder treatment, addressing logistical and financial constraints associated with traditional physiotherapy. The aim of this systematic review is to report the monitoring devices currently proposed and tested for shoulder rehabilitation in home settings. The research question was formulated using the PICO approach, and the PRISMA guidelines were applied to ensure a transparent methodology for the systematic review process. A comprehensive search of PubMed and Scopus was conducted, and the results were included from 2014 up to 2023. Three different tools (i.e., the Rob 2 version of the Cochrane risk-of-bias tool, the Joanna Briggs Institute (JBI) Critical Appraisal tool, and the ROBINS-I tool) were used to assess the risk of bias. Fifteen studies were included as they fulfilled the inclusion criteria. The results showed that wearable systems represent a promising solution as remote monitoring technologies, offering quantitative and clinically meaningful insights into the progress of individuals within a rehabilitation pathway. Recent trends indicate a growing use of low-cost, non-intrusive visual tracking devices, such as camera-based monitoring systems, within the domain of tele-rehabilitation. The integration of home-based monitoring devices alongside traditional rehabilitation methods is acquiring significant attention, offering broader access to high-quality care, and potentially reducing healthcare costs associated with in-person therapy.

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Objective monitoring tools for improved management of childhood asthma.

Asthma is a common chronic disease amongst children. Epidemiological studies showed that the mortality rate of asthma in children is still high worldwide. Asthma control is therefore essential to minimize asthma exacerbations, which can be fatal if the condition is poorly controlled. Frequent monitoring could help to detect asthma progression and ensure treatment effectiveness. Although subjective asthma monitoring tools are available, the results vary as they rely on patients' self-perception. Emerging evidence suggests several objective tools could have the potential for monitoring purposes. However, there is no consensus to standardise the use of objective monitoring tools. In this review, we start with the prevalence and severity of childhood asthma worldwide. Then, we detail the latest available objective monitoring tools, focusing on their effectiveness in paediatric asthma management. Publications of spirometry, fractional exhaled nitric oxide (FeNO), hyperresponsiveness tests and electronic monitoring devices (EMDs) between 2016 and 2023 were included. The potential advantages and limitations of each tool were also discussed. Overall, this review provides a summary for researchers dedicated to further improving objective paediatric asthma monitoring and provides insights for clinicians to incorporate different objective monitoring tools in clinical practices.

Remote Physiologic Monitoring Use Among Medicaid Population Increased, 2019-21.

Remote physiologic monitoring use increased more than 1,300 percent from 2019 to 2021, and use varied by state. This increase was driven by a small number of (predominantly internal medicine) providers. Female beneficiaries, residents of metropolitan areas, and people diagnosed with diabetes or hypertension had the highest rates of use.

Monitoring the venous circulation: novel techniques and applications.

PURPOSE OF REVIEW: Venous pressure is an often-unrecognized cause of patient morbidity. However, bedside assessment of PV is challenging. We review the clinical significance of venous pressure measurement, existing techniques, and introduce the Venous Excess Ultrasound (VExUS) Score as a novel approach using doppler ultrasound to assess venous pressure. RECENT FINDINGS: Studies show clear associations between elevated venous pressure and adverse outcomes in critically ill patients. Current venous pressure measurement techniques include physical examination, right heart catheterization (RHC), two-dimensional ultrasound, and a variety of labor-intensive research-focused physiological maneuvers. Each of these techniques have specific shortcomings, limiting their clinical utility. To address these gaps, Beaubien-Souligny et al. introduced the VExUS Score, a novel doppler ultrasound-based method that integrates IVC diameter with doppler measurements of the hepatic, portal, and renal veins to generate a venous congestion assesment. Studies show strong correlations between VExUS score and RHC measurements, and well as an association between VExUS score and improvement in cardiorenal acute kidney injury, diuretic response, and fluid status shifts. However, studies in noncardiac populations have been small, heterogenous, and inconclusive. SUMMARY: Early studies evaluating the use of doppler ultrasound to assess venous congestion show promise, but further research is needed in diverse patient populations and clinical settings.

Wearable wireless continuous vital signs monitoring on the general ward.

PURPOSE OF REVIEW: Wearable wireless sensors for continuous vital signs monitoring (CVSM) offer the potential for early identification of patient deterioration, especially in low-intensity care settings like general wards. This study aims to review advances in wearable CVSM - with a focus on the general ward - highlighting the technological characteristics of CVSM systems, user perspectives and impact on patient outcomes by exploring recent evidence. RECENT FINDINGS: The accuracy of wearable sensors measuring vital signs exhibits variability, especially notable in ambulatory patients within hospital settings, and standard validation protocols are lacking. Usability of CMVS systems is critical for nurses and patients, highlighting the need for easy-to-use wearable sensors, and expansion of the number of measured vital signs. Current software systems lack integration with hospital IT infrastructures and workflow automation. Imperative enhancements involve nurse-friendly, less intrusive alarm strategies, and advanced decision support systems. Despite observed reductions in ICU admissions and Rapid Response Team calls, the impact on patient outcomes lacks robust statistical significance. SUMMARY: Widespread implementation of CVSM systems on the general ward and potentially outside the hospital seems inevitable. Despite the theoretical benefits of CVSM systems in improving clinical outcomes, and supporting nursing care by optimizing clinical workflow efficiency, the demonstrated effects in clinical practice are mixed. This review highlights the existing challenges related to data quality, usability, implementation, integration, interpretation, and user perspectives, as well as the need for robust evidence to support their impact on patient outcomes, workflow and cost-effectiveness.

Monitoring CO2 kinetics as a marker of cardiopulmonary efficiency.

PURPOSE OF REVIEW: To describe current and near future developments and applications of CO2 kinetics in clinical respiratory and cardiovascular monitoring. RECENT FINDINGS: In the last years, we have witnessed a renewed interest in CO2 kinetics in relation with a better understanding of volumetric capnography and its derived parameters. This together with technological advances and improved measurement systems have expanded the monitoring potential of CO2 kinetics including breath by breath continuous end-expiratory lung volume and continuous noninvasive cardiac output. Dead space has slowly been gaining relevance in clinical monitoring and prognostic evaluation. Easy to measure dead space surrogates such as the ventilatory ratio have demonstrated a strong prognostic value in patients with acute respiratory failure. SUMMARY: The kinetics of carbon dioxide describe many relevant physiological processes. The clinical introduction of new ways of assessing respiratory and circulatory efficiency based on advanced analysis of CO2 kinetics are paving the road to a long-desired goal in clinical monitoring of critically ill patients: the integration of respiratory and circulatory monitoring during mechanical ventilation.

Monitoring lung recruitment.

PURPOSE OF REVIEW: This review explores lung recruitment monitoring, covering techniques, challenges, and future perspectives. RECENT FINDINGS: Various methodologies, including respiratory system mechanics evaluation, arterial bold gases (ABGs) analysis, lung imaging, and esophageal pressure (Pes) measurement are employed to assess lung recruitment. In support to ABGs analysis, the assessment of respiratory mechanics with hysteresis and recruitment-to-inflation ratio has the potential to evaluate lung recruitment and enhance mechanical ventilation setting. Lung imaging tools, such as computed tomography scanning, lung ultrasound, and electrical impedance tomography (EIT) confirm their utility in following lung recruitment with the advantage of radiation-free and repeatable application at the bedside for sonography and EIT. Pes enables the assessment of dorsal lung tendency to collapse through end-expiratory transpulmonary pressure. Despite their value, these methodologies may require an elevated expertise in their application and data interpretation. However, the information obtained by these methods may be conveyed to build machine learning and artificial intelligence algorithms aimed at improving the clinical decision-making process. SUMMARY: Monitoring lung recruitment is a crucial component of managing patients with severe lung conditions, within the framework of a personalized ventilatory strategy. Although challenges persist, emerging technologies offer promise for a personalized approach to care in the future.

Physiologic Determinants of Near-Infrared Spectroscopy-Derived Cerebral and Tissue Oxygen Saturation Measurements in Critically Ill Patients.

OBJECTIVES: Near-infrared spectroscopy (NIRS) is a potentially valuable modality to monitor the adequacy of oxygen delivery to the brain and other tissues in critically ill patients, but little is known about the physiologic determinants of NIRS-derived tissue oxygen saturations. The purpose of this study was to assess the contribution of routinely measured physiologic parameters to tissue oxygen saturation measured by NIRS. DESIGN: An observational sub-study of patients enrolled in the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) randomized feasibility trial. SETTING: Two ICUs in the United Kingdom. PATIENTS: Patients were recruited for the RADAR-2 study, which compared a conservative approach to fluid therapy and deresuscitation with usual care. Those included in this sub-study underwent continuous NIRS monitoring of cerebral oxygen saturations (SctO2) and quadriceps muscle tissue saturations (SmtO2). INTERVENTION: Synchronized and continuous mean arterial pressure (MAP), heart rate (HR), and pulse oximetry (oxygen saturation, Spo2) measurements were recorded alongside NIRS data. Arterial Paco2, Pao2, and hemoglobin concentration were recorded 12 hourly. Linear mixed effect models were used to investigate the association between these physiologic variables and cerebral and muscle tissue oxygen saturations. MEASUREMENTS AND MAIN RESULTS: Sixty-six patients were included in the analysis. Linear mixed models demonstrated that Paco2, Spo2, MAP, and HR were weakly associated with SctO2 but only explained 7.1% of the total variation. Spo2 and MAP were associated with SmtO2, but together only explained 0.8% of its total variation. The remaining variability was predominantly accounted for by between-subject differences. CONCLUSIONS: Our findings demonstrated that only a small proportion of variability in NIRS-derived cerebral and tissue oximetry measurements could be explained by routinely measured physiologic variables. We conclude that for NIRS to be a useful monitoring modality in critical care, considerable further research is required to understand physiologic determinants and prognostic significance.

Wireless Sensor System Based on Organohydrogel Ionic Skin for Physiological Activity Monitoring.

Supermolecular hydrogel ionic skin (i-skin) linked with smartphones has attracted widespread attention in physiological activity detection due to its good stability in complex scenarios. However, the low ionic conductivity, inferior mechanical properties, poor contact adhesion, and insufficient freeze resistance of most used hydrogels limit their practical application in flexible electronics. Herein, a novel multifunctional poly(vinyl alcohol)-based conductive organohydrogel (PCEL5.0%) with a supermolecular structure was constructed by innovatively employing sodium carboxymethyl cellulose (CMC-Na) as reinforcement material, ethylene glycol as antifreeze, and lithium chloride as a water retaining agent. Thanks to the synergistic effect of these components, the PCEL5.0% organohydrogel shows excellent performance in terms of ionic conductivity (1.61 S m-1), mechanical properties (tensile strength of 70.38 kPa and elongation at break of 537.84%), interfacial adhesion (1.06 kPa to pig skin), frost resistance (-50.4 °C), water retention (67.1% at 22% relative humidity), and remoldability. The resultant PCEL5.0%-based i-skin delivers satisfactory sensitivity (GF = 1.38) with fast response (348 ms) and high precision under different deformations and low temperature (-25 °C). Significantly, the wireless sensor system based on the PCEL5.0% organohydrogel i-skin can transmit signals from physiological activities and sign language to a smartphone by Bluetooth technology and dynamically displays the status of these movements. The organohydrogel i-skin shows great potential in diverse fields of physiological activity detection, human-computer interaction, and rehabilitation medicine.