Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

BACKGROUND: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. OBJECTIVE: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. METHODS: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. RESULTS: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. CONCLUSIONS: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.

Anisotropic Elastomer Ionomer Composite-Based Strain Sensors: Achieving High Sensitivity and Wide Detection for Human Motion Detection and Wireless Transmission.

Anisotropic strain sensors capable of multidirectional sensing are crucial for advanced sensor applications in human motion detection. However, current anisotropic sensors encounter challenges in achieving a balance among high sensitivity, substantial stretchability, and a wide linear detection range. To address these challenges, a facile freeze-casting strategy was employed to construct oriented filler networks composed of carbon nanotubes and conductive carbon black within a brominated butyl rubber ionomer (iBIIR) matrix. The resulting anisotropic sensor based on the iBIIR composites exhibited distinct gauge factors (GF) in the parallel and vertical directions (GF∥ = 4.91, while GF⊥ = 2.24) and a broad linear detection range over a strain range of 190%. This feature enables the sensor to detect various human activities, including uniaxial pulse, finder bending, elbow bending, and cervical spine movements. Moreover, the ion-cross-linking network within the iBIIR, coupled with strong π-cation interactions between the fillers and iBIIR macromolecules, imparted high strength (12.3 MPa, nearly twice that of pure iBIIR) and an ultrahigh elongation at break (>1800%) to the composites. Furthermore, the sensor exhibited exceptional antibacterial effectiveness, surpassing 99% against both Escherichia coli and Staphylococcus aureus. Notably, the sensor was capable of wireless sensing. It is anticipated that anisotropic sensors will have extensive application prospects in flexible wearable devices.

Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study.

BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. The Moveo device comprises 4 sensors positioned on the back of the hands and feet accompanied by a mobile application that gathers data and ML algorithms that are hosted on a cloud platform. The sensors measure movement signals, which are then transferred to the cloud through the mobile application. The cloud triggers a pipeline for feature extraction and subsequently feeds the ML model with these extracted features. METHODS: The pilot study included 23 participants. Eleven patients with diabetes and suspected diabetic neuropathy were included in the experimental group. In the control group, 8 patients had suspected radiculopathy, and 4 participants were healthy. All participants underwent an electrodiagnostic examination (EDx) and a Moveo examination, which consists of sensors placed on the feet and back of the participant's hands and use of the mobile application. The participant performs six tests that are part of a standard neurological examination, and a ML algorithm calculates the probability of diabetic neuropathy. A user experience questionnaire was used to compare participant experiences with regard to both methods. RESULTS: The total accuracy of the algorithm is 82.1%, with 78% sensitivity and 87% specificity. A high linear correlation up to 0.722 was observed between Moveo and EDx features, which underpins the model's adequacy. The user experience questionnaire revealed that the majority of patients preferred the less painful method. CONCLUSIONS: Moveo represents an accurate, easy-to-use device suitable for home environments, showing promising results and potential for future usage.

Multi-Functional Nano-Doped Hollow Fiber from Microfluidics for Sensors and Micromotors.

Nano-doped hollow fiber is currently receiving extensive attention due to its multifunctionality and booming development. However, the microfluidic fabrication of nano-doped hollow fiber in a simple, smooth, stable, continuous, well-controlled manner without system blockage remains challenging. In this study, we employ a microfluidic method to fabricate nano-doped hollow fiber, which not only makes the preparation process continuous, controllable, and efficient, but also improves the dispersion uniformity of nanoparticles. Hydrogel hollow fiber doped with carbon nanotubes is fabricated and exhibits superior electrical conductivity (15.8 S m-1), strong flexibility (342.9%), and versatility as wearable sensors for monitoring human motions and collecting physiological electrical signals. Furthermore, we incorporate iron tetroxide nanoparticles into fibers to create magnetic-driven micromotors, which provide trajectory-controlled motion and the ability to move through narrow channels due to their small size. In addition, manganese dioxide nanoparticles are embedded into the fiber walls to create self-propelled micromotors. When placed in a hydrogen peroxide environment, the micromotors can reach a top speed of 615 µm s-1 and navigate hard-to-reach areas. Our nano-doped hollow fiber offers a broad range of applications in wearable electronics and self-propelled machines and creates promising opportunities for sensors and actuators.

Fundamental Study of a Wristwatch Sweat Lactic Acid Monitor.

A lactic acid (LA) monitoring system aimed at sweat monitoring was fabricated and tested. The sweat LA monitoring system uses a continuous flow of phosphate buffer saline, instead of chambers or cells, for collecting and storing sweat fluid excreted at the skin surface. To facilitate the use of the sweat LA monitoring system by subjects when exercising, the fluid control system, including the sweat sampling device, was designed to be unaffected by body movements or muscle deformation. An advantage of our system is that the skin surface condition is constantly refreshed by continuous flow. A real sample test was carried out during stationary bike exercise, which showed that LA secretion increased by approximately 10 µg/cm2/min compared to the baseline levels before exercise. The LA levels recovered to baseline levels after exercise due to the effect of continuous flow. This indicates that the wristwatch sweat LA monitor has the potential to enable a detailed understanding of the LA distribution at the skin surface.

Wireless Flexible System for Highly Sensitive Ammonia Detection Based on Polyaniline/Carbon Nanotubes.

Ammonia (NH3) is a harmful atmospheric pollutant and an important indicator of environment, health, and food safety conditions. Wearable devices with flexible gas sensors offer convenient real-time NH3 monitoring capabilities. A flexible ammonia gas sensing system to support the internet of things (IoT) is proposed. The flexible gas sensor in this system utilizes polyaniline (PANI) with multiwall carbon nanotubes (MWCNTs) decoration as a sensitive material, coated on a silver interdigital electrode on a polyethylene terephthalate (PET) substrate. Gas sensors are combined with other electronic components to form a flexible electronic system. The IoT functionality of the system comes from a microcontroller with Wi-Fi capability. The flexible gas sensor demonstrates commendable sensitivity, selectivity, humidity resistance, and long lifespan. The experimental data procured from the sensor reveal a remarkably low detection threshold of 0.3 ppm, aligning well with the required specifications for monitoring ammonia concentrations in exhaled breath gas, which typically range from 0.425 to 1.8 ppm. Furthermore, the sensor demonstrates a negligible reaction to the presence of interfering gases, such as ethanol, acetone, and methanol, thereby ensuring high selectivity for ammonia detection. In addition to these attributes, the sensor maintains consistent stability across a range of environmental conditions, including varying humidity levels, repeated bending cycles, and diverse angles of orientation. A portable, stable, and effective flexible IoT system solution for real-time ammonia sensing is demonstrated by collecting data at the edge end, processing the data in the cloud, and displaying the data at the user end.

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements.

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Wearable Sensing Device Integrated with Prestored Reagents for Cortisol Detection in Sweat.

Wearable sweat sensors have achieved rapid development since they hold great potential in personalized health monitoring. However, a typical difficulty in practical processes is the control of working conditions for biorecognition elements, e.g., pH level and ionic strength in sweat may decrease the affinity between analytes and recognition elements. Here, we developed a wearable sensing device for cortisol detection in sweat using an aptamer as the recognition element. The device integrated functions of sweat collection, reagent prestorage, and signal conversion. Especially, the components of prestored reagents were optimized according to the inherent characteristics of sweat samples and electrodes, which allowed us to keep optimal conditions for aptamers. The sweat samples were transferred from the inlet of the device to the reagent prestored chamber, and the dry preserved reagents were rehydrated with sweat and then arrived at the aptamer-modified electrodes. Sweat samples of volunteers were analyzed by the wearable sensing device, and the results showed a good correlation with those of the ELISA kit. We believe that this convenient and reliable wearable sensing device has significant potential in self-health monitoring.

Recent Advances in Two-Dimensional Nanomaterials for Healthcare Monitoring.

The development of advanced technologies for the fabrication of functional nanomaterials, nanostructures, and devices has facilitated the development of biosensors for analyses. Two-dimensional (2D) nanomaterials, with unique hierarchical structures, a high surface area, and the ability to be functionalized for target detection at the surface, exhibit high potential for biosensing applications. The electronic properties, mechanical flexibility, and optical, electrochemical, and physical properties of 2D nanomaterials can be easily modulated, enabling the construction of biosensing platforms for the detection of various analytes with targeted recognition, sensitivity, and selectivity. This review provides an overview of the recent advances in 2D nanomaterials and nanostructures used for biosensor and wearable-sensor development for healthcare and health-monitoring applications. Finally, the advantages of 2D-nanomaterial-based devices and several challenges in their optimal operation have been discussed to facilitate the development of smart high-performance biosensors in the future.

Microfluidic Sensing Textile for Continuous Monitoring of Sweat Glucose at Rest.

Wearable sweat sensors have received considerable attention due to their great potential for noninvasive continuous monitoring of an individual's health status applications. However, the low secretion rate and fast evaporation of sweat pose challenges in collecting sweat from sedentary individuals for noninvasive analysis of body physiology. Here, we demonstrate wearable textiles for continuous monitoring of sweat at rest using the combination of a heating element and a microfluidic channel to increase localized skin sweat secretion rates and combat sweat evaporation, enabling accurate and stable monitoring of trace amounts of sweat. The Janus sensing yarns with a glucose sensing sensitivity of 36.57 mA cm-2 mM-1 are embroidered into the superhydrophobic heated textile to collect sweat directionally, resulting in improved sweat collection efficiency of up to 96 and 75% retention. The device also maintains a highly durable sensing performance, even in dynamic deformation, recycling, and washing. The microfluidic sensing textile can be further designed into a wireless sensing system that enables sedentary-compatible sweat analysis for the continuous, real-time monitoring of body glucose levels at rest.

Eye-Readable and Wearable Colorimetric Sensor Arrays for In Situ Monitoring of Volatile Organic Compounds.

Wearable sensors utilize changes in color as a response to physiological stimuli, making them easily recognizable by the naked eye. These colorimetric wearable sensors offer benefits such as easy readability, rapid responsiveness, cost-effectiveness, and straightforward manufacturing techniques. However, their applications in detecting volatile organic compounds (VOCs) in situ have been limited due to the low concentration of complex VOCs and complicated external interferences. Aiming to address these challenges, we introduced readable and wearable colorimetric sensing arrays with a microchannel structure and highly gas-sensitive materials for in situ detection of complex VOCs. The highly gas-sensitive materials were designed by loading gas-sensitive dyes into the porous metal-organic frameworks and further depositing the composites on the electrospun nanofiber membrane. The colorimetric sensor arrays were fabricated using various gas-sensitive composites, including eight dye/MOF composites that respond to various VOCs and two Pd2+/dye/MOF composites that respond to ethylene. This enables the specific recognition of multiple characteristic VOCs. A microfluidic channel made of polydimethylsiloxane (PDMS) was integrated with different colorimetric elements to create a wearable sensor array. It was attached to the surface of fruits to collect and monitor VOCs using the DenseNet classification method. As a proof of concept, we demonstrated the feasibility of the wearable sensing system in monitoring the ripening process of fruits by continuously measuring the VOC emissions from the skin of the fruit.

Estimation of lower back muscle force in a lifting task using wearable IMUs.

Low back pain is commonly reported in occupational settings due to factors such as heavy lifting and poor ergonomic practices, often resulting in significant healthcare expenses and lowered productivity. Assessment tools for human motion and ergonomic risk at the workplace are still limited. Therefore, this study aimed to assess lower back muscle and joint reaction forces in laboratory conditions using wearable inertial measurement units (IMUs) during weight lifting, a frequently high-risk workplace task. Ten able-bodied participants were instructed to lift a 28 lbs. box while surface electromyography sensors, IMUs, and a camera-based motion capture system recorded their muscle activity and body motion. The data recorded by IMUs and motion capture system were used to estimate lower back muscle and joint reaction forces via musculoskeletal modeling. Lower back muscle patterns matched well with electromyography recordings. The normalized mean absolute differences between muscle forces estimated based on measurements of IMUs and cameras were less than 25 %, and the statistical parametric mapping results indicated no significant difference between the forces estimated by both systems. However, abrupt changes in motion, such as lifting initiation, led to significant differences (p < 0.05) between the muscle forces. Furthermore, the maximum L5-S1 joint reaction force estimated using IMU data was significantly lower (p < 0.05) than those estimated by cameras during weight lifting and lowering. The study showed how kinematic errors from IMUs propagated through the musculoskeletal model and affected the estimations of muscle forces and joint reaction forces. Our findings showed the potential of IMUs for in-field ergonomic risk evaluations.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.

BACKGROUND: Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. METHODS: This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). RESULTS: Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. CONCLUSION: Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions.

Conventional snap fasteners used in clothing are often used as electrical connectors in e-textile and wearable applications for signal transmission due to their wide availability and ease of use. Nonetheless, limited research exists on the validation of these fasteners, regarding the impact of contact-induced high-amplitude artefacts, especially under motion conditions. In this work, three types of fasteners were used as electromechanical connectors, establishing the interface between a regular sock and an acquisition device. The tested fasteners have different shapes and sizes, as well as have different mechanisms of attachment between the plug and receptacle counterparts. Experimental evaluation was performed under static conditions, slow walking, and rope jumping at a high cadence. The tests were also performed with a test mass of 140 g. Magnetic fasteners presented excellent electromechanical robustness under highly dynamic human movement with and without the additional mass. On the other hand, it was demonstrated that the Spring snap buttons (with a spring-based engaging mechanism) presented a sub-optimal performance under high motion and load conditions, followed by the Prong snap fasteners (without spring), which revealed a high susceptibility to artefacts. Overall, this work provides further evidence on the importance and reliability of clothing fasteners as electrical connectors in wearable systems.

Superelastic carbon aerogels with anisotropic and hierarchically-enhanced cellular structure for wearable piezoresistive sensors.

Elastic carbon aerogels have promising applications in the field of wearable sensors. Herein, a new strategy for preparing carbon aerogels with excellent compressive strength and strain, shape recovery, and fatigue resistance was proposed based on the structure design and carbonization optimization of nanocellulose-based precursor aerogels. By the combination of directional freezing and zinc ion cross-linking, bacterial cellulose (BC)/alginate (SA) composite aerogels with high elasticity and compressive strength were first achieved. The existance of zinc ions also significantly improved the carbon retention rate and inhibited structural shrinkage, thus making the carbon aerogels retain ultra-high elasticity and fatigue resistance after compression. Moreover, the carbon aerogel possessed excellent piezoresistive pressure sensing performance with a wide detection range of 0-7.8 kPa, high sensitivity of 11.04 kpa-1, low detection limit (2 % strain), fast response (112 ms), and good durability (over 1,000 cycles). Based on these excellent properties, the carbon aerogel pressure sensors were further successfully used for human motion monitoring, from joint motion to and speech recognition.

An integrated wearable differential microneedle array for continuous glucose monitoring in interstitial fluids.

Monitoring biomarkers in human interstitial fluids (ISF) using microneedle sensors has been extensively studied. However, most of the previous studies were limited to simple in vitro demonstrations and lacked system integration and analytical performance. Here we report a miniaturized, high-precision, fully integrated wearable electrochemical microneedle sensing device that works with a customized smartphone application to wirelessly and in real-time monitor glucose in human ISF. A microneedle array fabrication method is proposed which enables multiple individually addressable, regionally separated sensing electrodes on a single microneedle system. As a demonstration, a glucose sensor and a differential sensor are integrated in a single sensing patch. The differential sensing electrodes can eliminate common-mode interference signals, thus significantly improving the detection accuracy. The basic mechanism of microneedle penetration into the skin was analyzed using the finite element method (FEM). By optimizing the structure of the microneedle, the puncture efficiency was improved while the puncture force was reduced. The electrochemical properties, biocompatibility, and system stability of the microneedle sensing device were characterized before human application. The test results were closely correlated with the gold standard (blood). The platform can be used not only for glucose detection, but also for various ISF biomarkers, and it expands the potential of microneedle technology in wearable sensing.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems.

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Portable Head-Mounted System for Mobile Forearm Tracking.

Computer vision (CV)-based systems using cameras and recognition algorithms offer touchless, cost-effective, precise, and versatile hand tracking. These systems allow unrestricted, fluid, and natural movements without the constraints of wearable devices, gaining popularity in human-system interaction, virtual reality, and medical procedures. However, traditional CV-based systems, relying on stationary cameras, are not compatible with mobile applications and demand substantial computing power. To address these limitations, we propose a portable hand-tracking system utilizing the Leap Motion Controller 2 (LMC) mounted on the head and controlled by a single-board computer (SBC) powered by a compact power bank. The proposed system enhances portability, enabling users to interact freely with their surroundings. We present the system's design and conduct experimental tests to evaluate its robustness under variable lighting conditions, power consumption, CPU usage, temperature, and frame rate. This portable hand-tracking solution, which has minimal weight and runs independently of external power, proves suitable for mobile applications in daily life.

Dual-band MIMO antenna with low mutual coupling for 2.4/5.8 GHz communication and wearable technologies.

To satisfy the requirements of modern communication systems and wearables using 2.4/5.8 GHz band this paper presents a simple, compact, and dual-band solution. The antenna is extracted from a circular monopole by inserting various patches and stubs. The genetic algorithm is utilized to optimize the parameters and achieve the best possible results regarding bandwidth and gain. Afterward, a 2-port multiple-input-multiple-output (MIMO) configuration is created by positioning an identical second single element perpendicularly to the first one. The electrical size of the suggested MIMO configuration is 0.26 λL × 0.53 λL, where λL represents the free space wavelength at lower resonance of 2.45 GHz. The common ground technique is adopted to further reduce and achieve the accepted level of mutual coupling of the MIMO configuration. The presented MIMO antenna offers a low mutual coupling of < -27 dB with 0.2 envelope correlation coefficient (ECC). The antenna has a gain of around 6.2 dBi and 6.5 dBi at resonating frequencies of 2.45 GHz and 5.4 GHz. Furthermore, the specific absorption rate (SAR) analysis of the MIMO antenna offers a range inside of the standard values, showing its potential for On/Off body communications. The comparison with already published works shows that the proposed antenna achieves better results in either compact size or wide operational bandwidth along with low mutual coupling.