Carbon Dot Therapeutic Platforms: Administration, Distribution, Metabolism, Excretion, Toxicity, and Therapeutic Potential.

Ultrasmall nanoparticles are often grouped under the broad umbrella term of "nanoparticles" when reported in the literature. However, for biomedical applications, their small sizes give them intimate interactions with biological species and endow them with unique functional physiochemical properties. Carbon quantum dots (CQDs) are an emerging class of ultrasmall nanoparticles which have demonstrated considerable biocompatibility and have been employed as potent theragnostic platforms. These particles find application for increasing drug solubility and targeting, along with facilitating the passage of drugs across impermeable membranes (i.e., blood brain barrier). Further functionality can be triggered by various environmental conditions or external stimuli (i.e., pH, temperature, near Infrared (NIR) light, ultrasound), and their intrinsic fluorescence is valuable for diagnostic applications. The focus of this review is to shed light on the therapeutic potential of CQDs and identify how they travel through the body, reach their site of action, administer therapeutic effect, and are excreted. Investigation into their toxicity and compatibility with larger nanoparticle carriers is also examined. The future of CQDs for theragnostic applications is promising due to their multifunctional attributes and documented biocompatibility. As nanomaterial platforms become more commonplace in clinical treatments, the commercialization of CQD therapeutics is anticipated.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review.

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human-machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user's locomotion intention and requirement, whereas machine-machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment-machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

Exoskeleton Application to Military Manual Handling Tasks.

OBJECTIVE: The aim of this review was to determine how exoskeletons could assist Australian Defence Force personnel with manual handling tasks. BACKGROUND: Musculoskeletal injuries due to manual handling are physically damaging to personnel and financially costly to the Australian Defence Force. Exoskeletons may minimize injury risk by supporting, augmenting, and/or amplifying the user's physical abilities. Exoskeletons are therefore of interest in determining how they could support the unique needs of military manual handling personnel. METHOD: Industrial and military exoskeleton studies from 1990 to 2019 were identified in the literature. This included 67 unique exoskeletons, for which Information about their current state of development was tabulated. RESULTS: Exoskeleton support of manual handling tasks is largely through squat/deadlift (lower limb) systems (64%), with the proposed use case for these being load carrying (42%) and 78% of exoskeletons being active. Human-exoskeleton analysis was the most prevalent form of evaluation (68%) with reported reductions in back muscle activation of 15%-54%. CONCLUSION: The high frequency of citations of exoskeletons targeting load carrying reflects the need for devices that can support manual handling workers. Exoskeleton evaluation procedures varied across studies making comparisons difficult. The unique considerations for military applications, such as heavy external loads and load asymmetry, suggest that a significant adaptation to current technology or customized military-specific devices would be required for the introduction of exoskeletons into a military setting. APPLICATION: Exoskeletons in the literature and their potential to be adapted for application to military manual handling tasks are presented.

Characterization of Impulse Radio Intrabody Communication System for Wireless Body Area Networks.

Intrabody communication (IBC) is a promising data communication technique for body area networks. This short-distance communication approach uses human body tissue as the medium of signal propagation. IBC is defined as one of the physical layers for the new IEEE 802.15.6 or wireless body area network (WBAN) standard, which can provide a suitable data rate for real-time physiological data communication while consuming lower power compared to that of radio-frequency protocols such as Bluetooth. In this paper, impulse radio (IR) IBC (IR-IBC) is examined using a field-programmable gate array (FPGA) implementation of an IBC system. A carrier-free pulse position modulation (PPM) scheme is implemented using an IBC transmitter in an FPGA board. PPM is a modulation technique that uses time-based pulse characteristics to encode data based on IR concepts. The transmission performance of the scheme was evaluated through signal propagation measurements of the human arm using 4- and 8-PPM transmitters, respectively. 4 or 8 is the number of symbols during modulations. It was found that the received signal-to-noise ratio (SNR) decreases approximately 8.0 dB for a range of arm distances (5-50 cm) between the transmitter and receiver electrodes with constant noise power and various signal amplitudes. The SNR for the 4-PPM scheme is approximately 2 dB higher than that for the 8-PPM one. In addition, the bit error rate (BER) is theoretically analyzed for the human body channel with additive white Gaussian noise. The 4- and 8-PPM IBC systems have average BER values of 10-5 and 10-10, respectively. The results indicate the superiority of the 8-PPM scheme compared to the 4-PPM one when implementing the IBC system. The performance evaluation of the proposed IBC system will improve further IBC transceiver design.

Minimum toe clearance events in divided attention treadmill walking in older and young adults: a cross-sectional study.

BACKGROUND: Falls in older adults during walking frequently occur while performing a concurrent task; that is, dividing attention to respond to other demands in the environment. A particularly hazardous fall-related event is tripping due to toe-ground contact during the swing phase of the gait cycle. The aim of this experiment was to determine the effects of divided attention on tripping risk by investigating the gait cycle event Minimum Toe Clearance (MTC). METHODS: Fifteen older adults (mean 73.1 years) and 15 young controls (mean 26.1 years) performed three walking tasks on motorized treadmill: (i) at preferred walking speed (preferred walking), (ii) while carrying a glass of water at a comfortable walking speed (dual task walking), and (iii) speed-matched control walking without the glass of water (control walking). Position-time coordinates of the toe were acquired using a 3 dimensional motion capture system (Optotrak NDI, Canada). When MTC was present, toe height at MTC (MTC_Height) and MTC timing (MTC_Time) were calculated. The proportion of non-MTC gait cycles was computed and for non-MTC gait cycles, toe-height was extracted at the mean MTC_Time. RESULTS: Both groups maintained mean MTC_Height across all three conditions. Despite greater MTC_Height SD in preferred gait, the older group reduced their variability to match the young group in dual task walking. Compared to preferred speed walking, both groups attained MTC earlier in dual task and control conditions. The older group's MTC_Time SD was greater across all conditions; in dual task walking, however, they approximated the young group's SD. Non-MTC gait cycles were more frequent in the older group across walking conditions (for example, in preferred walking: young - 2.9 %; older - 18.7 %). CONCLUSIONS: In response to increased attention demands older adults preserve MTC_Height but exercise greater control of the critical MTC event by reducing variability in both MTC_Height and MTC_Time. A further adaptive locomotor control strategy to reduce the likelihood of toe-ground contacts is to attain higher mid-swing clearance by eliminating the MTC event, i.e. demonstrating non-MTC gaits cycles.

The highs and lows of lifting loads: SPM analysis of multi-segmental spine angles in healthy adults during manual handling with increased load.

Introduction: Manual handling personnel and those performing manual handling tasks in non-traditional manual handling industries continue to suffer debilitating and costly workplace injuries. Smart assistive devices are one solution to reducing musculoskeletal back injuries. Devices that provide targeted assistance need to be able to predict when and where to provide augmentation via predictive algorithms trained on functional datasets. The aim of this study was to describe how an increase in load impacts spine kinematics during a ground-to-platform manual handling task. Methods: Twenty-nine participants performed ground-to-platform lifts for six standardised loading conditions (50%, 60%, 70%, 80%, 90%, and 100% of maximum lift capacity). Six thoracic and lumbar spine segments were measured using inertial measurement units that were processed using an attitude-heading-reference filter and normalised to the duration of the lift. The lift was divided into four phases weight-acceptance, standing, lift-to-height and place-on-platform. Statistical significance of sagittal angles from the six spine segments were identified through statistical parametric mapping one-way analysis of variance with repeated measures and post hoc paired t-tests. Results: Two regions of interest were identified during a period of peak flexion and a period of peak extension. There was a significant increase in spine range of motion and peak extension angle for all spine segments when the load conditions were increased (p < 0.001). There was a decrease in spine angles (more flexion) during the weight acceptance to standing phase at the upper thoracic to upper lumbar spine segments for some condition comparisons. A significant increase in spine angles (more extension) during the place-on-platform phase was seen in all spine segments when comparing heavy loads (>80% maximum lift capacity, inclusive) to light loads (<80% maximum lift capacity) (p < 0.001). Discussion: The 50%-70% maximum lift capacity conditions being significantly different from heavier load conditions is representative that the kinematics of a lift do change consistently when a participant's load is increased. The understanding of how changes in loading are reflected in spine angles could inform the design of targeted assistance devices that can predict where and when in a task assistance may be needed, possibly reducing instances of back injuries in manual handling personnel.

A Green Synthesis Route to Derive Carbon Quantum Dots for Bioimaging Cancer Cells.

Carbon quantum dots (CQDs) are known for their biocompatibility and versatile applications in the biomedical sector. These CQDs retain high solubility, robust chemical inertness, facile modification, and good resistance to photobleaching, which makes them ideal for cell bioimaging. Many fabrication processes produce CQDs, but most require expensive equipment, toxic chemicals, and a long processing time. This study developed a facile and rapid toasting method to prepare CQDs using various slices of bread as precursors without any additional chemicals. This fast and cost-effective toasting method could produce CQDs within 2 h, compared with the 10 h process in the commonly used hydrothermal method. The CQDs derived from the toasting method could be used to bioimage two types of colon cancer cells, namely, CT-26 and HT-29, derived from mice and humans, respectively. Significantly, these CQDs from the rapid toasting method produced equally bright images as CQDs derived from the hydrothermal method.

Prediction of gait trajectories based on the Long Short Term Memory neural networks.

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

Soft gold nanowire sponge antenna for battery-free wireless pressure sensors.

The past decade has witnessed growing interest in developing soft wearable pressure sensors with the ultimate goal of transforming today's hospital-centered diagnosis to tomorrow's patient-centered bio-diagnosis. In this context, battery-free wireless antenna-based pressure sensors will be highly advantageous for ubiquitous real-time health monitoring. However, current wireless antennas are largely based on thin films from traditional bulk metallic films or novel nanomaterials with an air-cavity design, which can only be operated in a limited pressure range due to the rigidity of active films and/or inherent cavity dimensions. Herein we report a soft battery-free wireless pressure sensor that is based on a three-dimensional (3D) porous gold nanowire foam-elastomer composite and is fabricated by solution-based conformal electroless plating technology, followed by elastomer encapsulation. We observe a transducer trade-off point for our foam antenna, below which the inductive effect and capacitive effect function together and above which the capacitive effect dominates. When an external pressure is applied, initially the inductance and capacitance increase simultaneously but the capacitance decreases afterwards. This can be transformed into a variable resonant frequency that first decreases linearly and then increases (in the capacitance domination pressure range). Importantly, the linear detection range of the sensor can be tuned simply by adjusting the thickness of the sponge or the rigidity of the elastomer (PDMS). We can achieve a wide pressure range of 0-248 kPa, which is the largest linear detection range reported in the literature (typically from 0 to 30 kPa) to the best of our knowledge. As a proof of concept, we further demonstrated that our gold nanowire foam sensor can be used to weigh people under both static and dynamic conditions.

A real-time fuzzy logic biofeedback controller for freestyle swimming body posture adjustment.

Wearable body area networks (BANs) have been widely used in activity measurements for kinematic information collection. This paper presents the design and implementation of a wearable device used as a training tool in freestyle swimming. The device supplies a close-loop control mechanism via a fuzzy logic controller. Swimming posture data is collected quantitatively and audibly fed back to swimmers in real time through bone conductors. Two recreational swimmers were invited to participate in a series of experiments including 7 days of baseline capability test (no feedback), 7 days of feedback training, and 2 days of retention test. It was found that both swimmers could well adapt to the feedback instructions. A maximum of 7.62% of lap time improvement and 29.64% of trunk roll improvement were observed in FB training, and such pattern was maintained after feedback was removed. We conclude that real-time fuzzy logic feedback can be used to improve recreational swimmers performance.

Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.

This study determined whether the kinematics of lower limb trajectories during walking could be extrapolated using long short-term memory (LSTM) neural networks. It was hypothesised that LSTM auto encoders could reliably forecast multiple time-step trajectories of the lower limb kinematics, specifically linear acceleration (LA) and angular velocity (AV). Using 3D motion capture, lower limb position-time coordinates were sampled (100 Hz) from six male participants (age 22 ± 2 years, height 1.77 ± 0.02 m, body mass 82 ± 4 kg) who walked for 10 min at 5 km/h on a 0% gradient motor-driven treadmill. These data were fed into an LSTM model with a sliding window of four kinematic variables with 25 samples or time steps: LA and AV for thigh and shank. The LSTM was tested to forecast five samples (i.e., time steps) of the four kinematic input variables. To attain generalisation, the model was trained on a dataset of 2,665 strides from five participants and evaluated on a test set of 1 stride from a sixth participant. The LSTM model learned the lower limb kinematic trajectories using the training samples and tested for generalisation across participants. The forecasting horizon suggested higher model reliability in predicting earlier future trajectories. The mean absolute error (MAE) was evaluated on each variable across the single tested stride, and for the five-sample forecast, it obtained 0.047 m/s2 thigh LA, 0.047 m/s2 shank LA, 0.028 deg/s thigh AV and 0.024 deg/s shank AV. All predicted trajectories were highly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The motion prediction model may have a wide range of applications, such as mitigating the risk of falls or balance loss and improving the human-machine interface for wearable assistive devices.

The application of support vector machines for detecting recovery from knee replacement surgery using spatio-temporal gait parameters.

Knee osteoarthritis (OA) is one of the leading causes of disability among the elderly which, depending on severity, may require surgical intervention. Knee replacement surgery provides pain relief and improves physical function including gait. However gait dysfunction such as altered spatio-temporal measures may persist after the surgery. In this paper, we investigated the application of support vector machines (SVM) to classify gait patterns indicative of knee OA before surgery based on 12 spatio-temporal gait parameters and investigated whether SVMs could be used to predict gait improvement 2 and 12 months following knee replacement surgery. Test results for the pre-operative data indicated that the SVM could successfully identify individuals with OA gait from the healthy using all of the spatio-temporal parameters with a maximum leave one out accuracy of 100% for the training set and 88.89% for the test set. Findings indicated that three patients still had altered gait patterns 2 months post-knee replacement surgery, but all individuals showed improvement in gait 12 months following surgery. Consequently, the SVM detected improvement in gait function due to surgical intervention at 2 and 12 months following knee replacement which coincided with clinical assessment of the knee. This suggests that spatio-temporal parameters contain important discriminative information which may be used for the identification of pathological gait using an SVM classifier.

A Review of Implant Communication Technology in WBAN: Progress and Challenges.

Over the past six decades, there has been tremendous progress made in the field of medical implant communications. A comprehensive review of the progress, current state of the art, and future direction is presented in this paper. Implanted medical devices (IMDs) are designed mainly for the purpose of diagnostic, therapeutic, and assistive applications in heathcare, active living, and sports technology. The primary target of IMDs' design revolves around reliable communications, sustainable power sources, and a high degree of miniaturization while maintaining biocompatibility to surrounding tissues adhering to the human safety limits set by appropriate guidelines. The role of the Internet of Things and intelligent data analysis in implant device networks as future research is presented in this paper. Finally, in addition to reviewing the state of the art, a novel intuitive lower bound on implant size is presented.

Local Crack-Programmed Gold Nanowire Electronic Skin Tattoos for In-Plane Multisensor Integration.

Sensitive, specific, yet multifunctional tattoo-like electronics are ideal wearable systems for "any time, any where" health monitoring because they can virtually become parts of the human skin, offering a burdenless "unfeelable" wearing experience. A skin-like, multifunctional electronic tattoo made entirely from gold using a standing enokitake-mushroom-like vertically aligned nanowire membrane in conjunction with a programmable local cracking technology is reported. Unlike previous multifunctional systems, only a single material type is needed for the integrated gold circuits involved in interconnects and multiplexed specific sensors, thereby avoiding the use of complex multimaterials interfaces. This is possiblebecause the programmable local cracking technology allows for the arbitrary fine-tuning of the properties of elastic gold conductors from strain-insensitive to highly strain-sensitive simply by adjusting localized crack size, shape, and orientations-a capability impossible to achieve with previous bulk cracking technology. Furthermore, in-plane integration of strain/pressure sensors, anisotropic orientation-specific sensors, strain-insensitive stretchable interconnects, temperature sensors, glucose sensors, and lactate sensors without the need of soldering or gluing are demonstrated. This strategy opens a new general route for the design of next-generation wearable electronic tattoos.

Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly.

Trip related falls are a prevalent problem in the elderly. Early identification of at-risk gait can help prevent falls and injuries. The main aim of this study was to investigate the effectiveness of a wavelet based multiscale analysis of a gait variable [minimum foot clearance (MFC)] in comparison to MFC histogram plot analysis in extracting features for developing a model using support vector machines (SVMs) for screening of balance impairments in the elderly. MFC during walking on a treadmill was recorded on 13 healthy elderly and 10 elderly with a history of tripping falls. Features extracted from MFC histogram and then multiscale exponents between successive wavelet coefficient levels after wavelet decomposition of MFC series were used as inputs to the SVM to classify two gait patterns. The maximum accuracy of classification was found to be 100% for a SVM using a subset of selected wavelet based features, compared to 86.95% accuracy using statistical features. For estimating the relative risk of falls, the posterior probabilities of SVM outputs were calculated. These results suggest superior performance of SVM in the detection of balance impairments based on wavelet-based features and it could also be useful for evaluating for falls prevention intervention.

A Survey on Biofeedback and Actuation in Wireless Body Area Networks (WBANs).

Wireless body area networks (WBANs) have attained increasing popularity as the next generation framework of wearable technologies for human monitoring. Invasive or noninvasive wearable sensors designed in a WBAN are worn to gather vital information. Biofeedback is a recent concept where collected data are used to generate actuation signals in WBANs. Applications can be seen in various areas such as sports (e.g., locomotor velocity) or medicine (e.g., blood pressure measurement). However, since the body is closely regulated, the next generation WBAN technology must be smart enough to react to monitored data. The main aim of this paper is to review the current state of biofeedback and actuation technology on WBANs in terms of its structure, applications, benefits, and control approaches. The emphasis on the specific requirements when applying biofeedback to humans will be highlighted and discussed. Challenges and open research issues will be concluded at the end.

Non-MTC gait cycles: An adaptive toe trajectory control strategy in older adults.

Minimum-toe-clearance (MTC) above the walking surface is a critical representation of toe-trajectory control due to its association with tripping risk. Not all gait cycles exhibit a clearly defined MTC within the swing phase but there have been few previous accounts of the biomechanical characteristics of non-MTC gait cycles. The present report investigated the within-subject non-MTC gait cycle characteristics of 15 older adults (mean 73.1 years) and 15 young controls (mean 26.1 years). Participants performed the following tasks on a motorized treadmill: preferred speed walking, dual task walking (carrying a glass of water) and a dual-task speed-matched control. Toe position-time coordinates were acquired using a 3 dimensional motion capture system. When MTC was present, toe height at MTC (MTCheight) was extracted. The proportion of non-MTC gait cycles was computed for the age groups and individuals. For non-MTC gait cycles an 'indicative' toe height at the individual's average swing phase time (MTCtime) for observed MTC cycles was averaged across multiple non-MTC gait cycles. In preferred-speed walking Young demonstrated 2.9% non-MTC gait cycles and Older 18.7%. In constrained walking conditions both groups increased non-MTC gait cycles and some older adults revealed over 90%, confirming non-MTC gait cycles as an ageing-related phenomenon in lower limb trajectory control. For all participants median indicative toe-height on non-MTC gait cycles was greater than median MTCheight. This result suggests that eliminating the biomechanically hazardous MTC event by adopting more of the higher-clearance non-MTC gait cycles, is adaptive in reducing the likelihood of toe-ground contact.

A wearable biofeedback control system based body area network for freestyle swimming.

Wearable posture measurement units are capable of enabling real-time performance evaluation and providing feedback to end users. This paper presents a wearable feedback prototype designed for freestyle swimming with focus on trunk rotation measurement. The system consists of a nine-degree-of-freedom inertial sensor, which is built in a central data collection and processing unit, and two vibration motors for delivering real-time feedback. Theses devices form a fundamental body area network (BAN). In the experiment setup, four recreational swimmers were asked to do two sets of 4 x 25m freestyle swimming without and with feedback provided respectively. Results showed that real-time biofeedback mechanism improves swimmers kinematic performance by an average of 4.5% reduction in session time. Swimmers can gradually adapt to feedback signals, and the biofeedback control system can be employed in swimmers daily training for fitness maintenance.

A Circuit Model of Real Time Human Body Hydration.

Changes in human body hydration leading to excess fluid losses or overload affects the body fluid's ability to provide the necessary support for healthy living. We propose a time-dependent circuit model of real-time human body hydration, which models the human body tissue as a signal transmission medium. The circuit model predicts the attenuation of a propagating electrical signal. Hydration rates are modeled by a time constant τ, which characterizes the individual specific metabolic function of the body part measured. We define a surrogate human body anthropometric parameter θ by the muscle-fat ratio and comparing it with the body mass index (BMI), we find theoretically, the rate of hydration varying from 1.73 dB/min, for high θ and low τ to 0.05 dB/min for low θ and high τ. We compare these theoretical values with empirical measurements and show that real-time changes in human body hydration can be observed by measuring signal attenuation. We took empirical measurements using a vector network analyzer and obtained different hydration rates for various BMI, ranging from 0.6 dB/min for 22.7 [Formula: see text] down to 0.04 dB/min for 41.2 [Formula: see text]. We conclude that the galvanic coupling circuit model can predict changes in the volume of the body fluid, which are essential in diagnosing and monitoring treatment of body fluid disorder. Individuals with high BMI would have higher time-dependent biological characteristic, lower metabolic rate, and lower rate of hydration.