Subject's Chest Research Articles

Development of a new integrated IN-VIVO counting system at the QST.

A new in-vivo counting system that functions as both a whole-body counter (WBC) and a lung counter (LC) was developed at the QST to enhance its dose assessment capability. This paper presents an overview of this system and the results of its performance tests. For use of the system as a WBC, three high purity germanium (HPGe) detectors installed in a 20-cm-thick iron shielding chamber are linearly arrayed over a subject lying on the bed, whereas two of the three HPGe detectors are placed over the subject's chest from side to side when using the system as an LC. The new in-vivo system was calibrated using three de-facto phantoms owned by the QST: an adult-male BOttle Manikin ABsorption (BOMAB) phantom, a Lawrence Livermore National Laboratory (LLNL) phantom and a Japan Atomic Energy Research Institute (JAERI) phantom. Monte Carlo simulations were also performed to determine an optimum location for the three detector array in the WBC mode and revealed that the peak efficiency for the BOMAB phantom (662keV) was little varied as long as the middle detector was placed above the thorax and abdomen parts of the phantom. The calculated peak efficiencies agreed well with the observed peak efficiencies for photons with energies over 100keV. For lung counting, a tentative Minimum Detectable Activity of 241Am was evaluated as 9.5Bq for a counting time of 30minutes, and a Japanese male subject with an average chest wall thinness (2.27cm). The developed system is now ready for use.

X-Ray Imaging in the Simulated Microgravity Environment of Parabolic Flight.

INTRODUCTION: The advancement of human spaceflight has made urgent the need to develop medical imaging technology to ensure a high level of in-flight care. To date, only ultrasound has been used in spaceflight. Radiography has multiple advantages over ultrasound, including lower operator dependence, more rapid acquisition, typically higher spatial resolution, and characterization of tissue with acoustic impedance precluding ultrasound. This proof-of-concept work demonstrates for the first time the feasibility of performing human radiographs in microgravity.METHODS: Radiographs of a phantom and human subject's hand, knee, chest, cervical spine, and pelvis were obtained aboard a parabolic flight in microgravity and simulated lunar gravity with various subject and operator positions. Control radiographs were acquired with the same system on the ground. These radiographs were performed with a Food and Drug Administration-approved ultra-portable, wireless, battery-powered, digital x-ray system.RESULTS: The radiographs of the phantom acquired in reduced gravity were qualitatively and quantitatively compared to the ground controls and found to exhibit similar diagnostic adequacy. There was no statistically significant difference in contrast resolution or spatial resolution with a spatial resolution across all imaging environments up to the Nyquist frequency of 3.6 line-pairs/mm and an average contrast-to-noise ratio of 2.44.DISCUSSION: As mass, power, and volume limitations lessen over the coming decades and the miniaturization of imaging equipment continues, in-flight implementation of nonsonographic modalities will become practical. Given the demonstrated ease of use and satisfactory image quality, portable radiography is ready to be the new frontier of space medical imaging.Lerner D, Pohlen M, Wang A, Walter J, Cairnie M, Gifford S. X-ray imaging in the simulated microgravity environment of parabolic flight. Aerosp Med Hum Perform. 2023; 94(10):786-791.

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition.

This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject's chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer's activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition.

Remote Estimation of Blood Pressure Using Millimeter-Wave Frequency-Modulated Continuous-Wave Radar.

This paper proposes to remotely estimate a human subject's blood pressure using a millimeter-wave radar system. High blood pressure is a critical health threat that can lead to diseases including heart attacks, strokes, kidney disease, and vision loss. The commonest method of measuring blood pressure is based on a cuff that is contact-based, non-continuous, and cumbersome to wear. Continuous remote monitoring of blood pressure can facilitate early detection and treatment of heart disease. This paper investigates the possibility of using millimeter-wave frequency-modulated continuous-wave radar to measure the heart blood pressure by means of pulse wave velocity (PWV). PWV is known to be highly correlated with blood pressure, which can be measured by pulse transit time. We measured PWV using a two-millimeter wave radar focused on the subject's chest and wrist. The measured time delay provided the PWV given the length from the chest to the wrist. In addition, we analyzed the measured radar signal from the wrist because the shape of the pulse wave purveyed information on blood pressure. We investigated the area under the curve (AUC) as a feature and found that AUC is strongly correlated with blood pressure. In the experiment, five human subjects were measured 50 times each after performing different activities intended to influence blood pressure. We used artificial neural networks to estimate systolic blood pressure (SBP) and diastolic blood pressure (SBP) with both PWV and AUC as inputs. The resulting root mean square errors of estimated blood pressure were 3.33 mmHg for SBP and 3.14 mmHg for DBP.

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings.

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject's chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors' location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R2 > 0.99), a Pearson's correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.

System-Identification-Based Activity Recognition Algorithms With Inertial Sensors.

This paper focuses on activity recognition using a single wearable inertial measurement sensor placed on the subject's chest. The ten activities that need to be identified include lying down, standing, sitting, bending and walking, among others. The activity recognition approach is based on using and identifying a transfer function associated with each activity. The appropriate input and output signals for each transfer function are first determined based on the norms of the sensor signals excited by that specific activity. Then the transfer function is identified using training data and a Wiener filter based on the auto-correlation and cross-correlation of the output and input signals. The activity occurring in real-time is recognized by computing and comparing the input-output errors associated with all the transfer functions. The performance of the developed system is evaluated using data from a group of Parkinson's disease subjects, including data obtained in a clinical setting and data obtained through remote home monitoring. On average, the developed system provides better than 90% accuracy in identifying each activity as it occurs. Activity recognition is particularly useful for PD patients in order to monitor their level of activity, characterize their postural instability and recognize high risk-activities in real-time that could lead to falls.

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching.

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R2 > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices.

Quantifying the Effect of Body Habitus on Cardiac Auscultation Via Computational Hemoacoustics.

The effect of body habitus on auscultation of heart murmurs is investigated via computational hemoacoustic modeling. The source of the heart murmur is first obtained from a hemodynamic simulation of blood flow through a stenosed aortic valve. This sound source is then placed at the aortic valve location in four distinct human thorax models, and the propagation of the murmur in each thorax model is simulated by solving the elastic wave equations in the time-domain. Placing the same sound source in different thorax models allows for the disambiguation of the effect of body habitus on cardiac auscultation. The surface acceleration resulting from the murmur on each subject's chest surface shows that subjects with higher body-mass index and thoracic cross-sectional area yield smaller acceleration values for the S1 sound. Moreover, the spectral analysis of the signal shows that slope from linear regression in the normal heart sound frequency range (10-150 Hz) is larger for children at the aortic, pulmonic, and mitral auscultation points compared to that for adults. The slope in the murmur frequency range (150-400 Hz) was larger for female subjects at the mitral point compared to that for male subjects. The trends from the results show the potential of the proposed computational method to provide quantitative insights regarding the effect of various anatomical factors on cardiac auscultation.

Substrate integrated waveguide‐based simultaneous transmit and receive antenna for full‐duplex wearable devices

In this paper, a low-size and compact substrate integrated waveguide (SIW)-based simultaneous transmit and receive antenna is developed for full-duplex wearable telemetry applications. The antenna operates at 4.9 GHz (Wireless Local Area Network [WLAN]) and 5.8 GHz (Industrial, Scientific and Medical [ISM]) bands with acceptable gains. Keeping a proper gap between the two ports achieves an isolation of better than 29.8 dB. This antenna is kept compact (0.20 λg2, where λg is the wavelength at 4.9 GHz) by additional arc-shaped slot in each radiator, which adds additional capacitance. The suggested antenna is simulated inside a 52 × 63 × 17 mm3 dummy wearable telemetry device and is worn on the subject's chest. Thanks to SIW technology, it has unidirectional radiation patterns, allowing a minimum beam towards the human body, and thus, offers lower specific absorption rate (SAR) at both frequency bands. The unique advantage is its capacity to receive and transmit the telemetry signals simultaneously. Its low profile, flexibility in the resonant bands, simultaneous transmission and reception, and lower SAR values make this antenna suitable for full-duplex wearable telemetry devices.

WiPhone

Recent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI amplitude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.

Body Orientation and Vital Sign Measurement With IR-UWB Radar Network.

Utilizing Impulse Radio Ultra-WideBand (IR-UWB) radar for vital sign monitoring has attracted growing interest due to the noncontact measurement without privacy concerns. Most of existing researches assume that the subject's chest is directed to the radar antenna, to ensure the strength of backscattered signals from chest movement. However, a large angle between the antenna and the subject's chest caused by the body orientation badly affects the monitoring accuracy. Multiple observations of the same cardiopulmonary activity from different orientations provide more available measurements. This paper addresses the challenge by using an IR-UWB radar network instead of a single radar. Three IR-UWB radars are placed as endpoints of an equilateral triangle to collect vital sign information of a subject sitting at the center. A Conditional Generative Adversarial Network (CGAN) method is proposed to fuse multisensory data. First, the body orientation is classified by combining signal features and a random forest classifier. Then the impact of different angles on vital sign monitoring results is discussed and validated in each orientation. The data fusion process is modelled as an extended generative network with orientation based condition to produce the enhanced vital signal. This signal is optimized with the discriminator network to a fitted sinusoidal wave with heartbeat and respiratory information. Experimental results on measuring Heartbeat Rate (HR) in different orientations reveal the effectiveness and stability of the proposed method.

BodyCompass

Monitoring sleep posture is important for avoiding bedsores after surgery, reducing apnea events, tracking the progression of Parkinson's disease, and even alerting epilepsy patients to potentially fatal sleep postures. Today, there is no easy way to track sleep postures. Past work has proposed installing cameras in the bedroom, mounting accelerometers on the subject's chest, or embedding pressure sensors in their bedsheets. Unfortunately, such solutions jeopardize either the privacy of the user or their sleep comfort. In this paper, we introduce BodyCompass, the first RF-based system that provides accurate sleep posture monitoring overnight in the user's own home. BodyCompass works by studying the RF reflections in the environment. It disentangles RF signals that bounced off the subject's body from other multipath signals. It then analyzes those signals via a custom machine learning algorithm to infer the subject's sleep posture. BodyCompass is easily transferable and can apply to new homes and users with minimal effort. We empirically evaluate BodyCompass using over 200 nights of sleep data from 26 subjects in their own homes. Our results show that, given one week, one night, or 16 minutes of labeled data from the subject, BodyCompass's corresponding accuracy is 94%, 87%, and 84%, respectively.

A Wireless Wearable Sensor Patch for the Real-Time Estimation of Continuous Beat-to-Beat Blood Pressure.

This paper proposes a wireless wearable sensor system for the continuous beat-to-beat blood pressure (BP) monitoring. The real-time BP can be estimated utilising a 2-parameter regression model based on the pulse arrival time (PAT) and heart rate (HR). The PAT is defined as the time interval between the electrocardiogram (ECG) R-peak and the corresponding maximum inclination point of photoplethysmography (PPG) signal. A wireless wearable sensor patch designed to be attached to the subject's chest is used for the measurement of ECG and PPG signals. The sensor data are transmitted through a Bluetooth low energy (BLE) module to a computer for the real-time online estimation of BP. To verify the feasibility and performance of the proposed system, a 5-day period experiment is conducted on two healthy male subjects for the training and validation of the BP estimation model. On each day, there are two 15 minutes offline sessions for data collection from the sensor patch, which are compared with the reference BP to calibrate the estimation model parameters. After that, a 10 minutes online session is carried out to validate the regression model against the reference BP device. Eventually, the 5-day period data are combined together for an overall BP estimation model, which has good correlation (r=0.82) with the reference BP measurements. The experimental results show the proposed sensor patch with the BP estimation model is capable of the online real-time BP monitoring after an initial calibration procedure.

Adaptive Accelerometry Derived Respiration: Comparison with Respiratory Inductance Plethysmography during Sleep.

Polysomnography (PSG) is a multi-parametric test used in the study of sleep and as a diagnostic tool in sleep medicine. PSG is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This work presents a novel method based on a dual tri-axis accelerometer system (Adaptive Accelerometry Derived Respiration, ADR) which was patched on the subject's chest that adaptively reconstructed thoracic and abdominal respiratory efforts. Performance evaluation was performed on a 60s-epoch basis using signal and physiological indicators: the evaluation consisted in the comparison of airflow estimations from ADR and RIP to the nasal airflow, considered as reference. Results showed that 74% of the 60s-epoch ADR airflow estimation present a correlation coefficient with nasal airflow ≥ 70% compared to 64% for RIP. Relative errors for one-minute respiration rate and tidal volume estimation appeared to be relatively low which reflected the good feasibility of the adaptive ADR method for respiration monitoring during sleep.

P066: Ultrasound localization to resuscitate in arrest (ULTRA)

Introduction: There is increasing evidence supporting ultrasonography for the determination of optimal chest compression location during cardiac arrest. Radiological studies have demonstrated that in up to 1/3 of patients the aortic root or outflow tract is being compressed during standard CPR. Out-of-hospital-cardiac-arrests (OHCA) could benefit from cardiac localization, undertaken with scaled-down ultrasound equipment by which the largest fluid filled structure in the chest (the heart) is identified to guide optimal compression location. We intend to evaluate 1) where the left ventricle is in supine patients, 2) the accuracy and precision as well as 3) the feasibility and reliability of cardiac localization with a scaled down ultrasound device (bladder scanners). Methods: We are recruiting men and women over the age of 40. The scanning protocol involves using a bladder scanner on a 15-point grid over the subject's left chest and parasternal, midclavicular, and anterior axillary intercostal spaces 3-7. Detected volumes will be recorded, with the presumption that the intercostal space with the largest measured volume is centered over the heart. Echocardiography will then be used to confirm the bladder scanner accuracy and to better describe the patient's internal chest anatomy. Having assessed procedural feasibility on 3 pilot subjects, we are now recruiting 100 participants, with planned interim analysis at 50 participants for sample size reassessment. Maximal volume location frequencies from the echocardiograms will be described and assessed for variation utilizing the goodness-of-fit test. The proportion of agreement across the two modalities regarding the maximal volume location will also be examined. Results: Among the 3 volunteers (pilot study), the scanner identified fluid in 4-8 of 15 intercostal spaces. In each of the three pilot study patients, the maximal volume identified by the bladder scanner was found to be at the parasternal location of the 6th intercostal space. This was also the location of the mid left ventricular diameter on echocardiography. Conclusion: Our literature review and pilot study data support the premise that lay persons and emergency medical personnel may improve compressions (and thus outcomes) during OHCA by using a scaled-down ultrasound to identify the location of optimal compression. We are currently enrolling patients in our study.

Air velocity influences thermoregulation and endurance exercise capacity in the heat.

This study examined the effects of variations in air velocity on time to exhaustion and thermoregulatory and perceptual responses to exercise in a hot environment. Eight male volunteers completed stationary cycle exercise trials at 70% peak oxygen uptake until exhaustion in an environmental chamber maintained at 30 °C and 50% relative humidity. Four air velocity conditions, 30, 20, 10, and 0 km/h, were tested, and the headwind was directed at the frontal aspect of the subject by 2 industrial fans, with blade diameters of 1 m and 0.5 m, set in series and positioned 3 m from the subject's chest. Mean ± SD time to exhaustion was 90 ± 17, 73 ± 16, 58 ± 13, and 41 ± 10 min in 30-, 20-, 10-, and 0-km/h trials, respectively, and was different between all trials (P < 0.05). There were progressive elevations in the rate of core temperature rise, mean skin temperature, and perceived thermal sensation as airflow decreases (P < 0.05). Core temperature, heart rate, cutaneous vascular conductance, and perceived exertion were higher and evaporative heat loss was lower without airflow than at any given airflow (P < 0.05). Dry heat loss and plasma volume were similar between trials (P > 0.05). The present study demonstrated a progressive reduction in time to exhaustion as air velocity decreases. This response is associated with a faster rate of core temperature rise and a higher skin temperature and perceived thermal stress with decreasing airflow. Moreover, airflow greater than 10 km/h (2.8 m/s) might contribute to enhancing endurance exercise capacity and reducing thermoregulatory, cardiovascular, and perceptual strain during exercise in a hot environment.

An estimation of knee and ankle joint angles during extension phase of standing up motion performed using an inertial sensor.

[Purpose] Motion capture system is difficult to use in daily life. The aim of this study was to propose an estimation model for knee and ankle joint angle measurements and locate body center of gravity (COG) of the extension phase during standing-up motion. [Subjects and Methods] Seven healthy male volunteers were enrolled. An estimation model was proposed for the knee and ankle joint angle measurements by combining the angle and acceleration of the trunk, based on readings from the inertial sensor attachment on the subject’s chest, during the extension phase. Joint angles and COG position were compared to those obtained by a motion capture system. [Results] The joint angles and COG position demonstrated high correlation coefficients which represent strong correlation between the proposed model and the motion capture system. The proposed model could estimate the joint angle during extension phase, with a maximum error of 4.58 degrees, as well as COG position in the horizontal and vertical directions with maximum errors of 4.48 cm and 3.19 cm, respectively. [Conclusion] The proposed system could be used instead of motion capture system to estimate knee and ankle joint angles; however, the estimation of the COG position was insufficient because of lacked accuracy.

Perioperative hair removal in the 21st century: Utilizing an innovative vacuum-assisted technology to safely expedite hair removal before surgery

Perioperative hair removal using clippers requires lengthy cleanup to remove loose hairs contaminating the operative field. We compared the amount of hair debris and associated microbiologic contamination produced during clipping of surgical sites using standard surgical clippers (SSC) or clippers fitted with a vacuum-assisted hair collection device (SCVAD). Trained nurses conducted bilateral hair clipping of the chest and groin of 18 male subjects using SSC or SCVAD. Before and during clipping, measurements of particulate matter and bacterial contamination were evaluated on settling plates placed next to each subject's chest and groin. Skin condition after clipping and total clipping/cleanup times were compared between SSC and SCVAD. The microbial burden recovered from residual hair during cleanup in the SSC group was 3.9 log10 CFU and 4.6 log10 CFU from respective, chest, and groin areas. Use of the SCVAD resulted in a significant (P < .001) reduction in both residual hair and microbial contamination within the operative field compared with SSC. Use of SCVAD resulted in significant (P < .001) reduction in total time required to clip and clean up residual hair contaminating the operative field compared with standard practice (ie, SSC), eliminating the need to physically remove dispersed hairs, which can harbor a significant microbial burden, from within the operative field.

A BREATHING SYNCHRONIZATION STRATEGY FOR THE NON-INVASIVE VENTILATOR SYSTEM

The study presents a method to achieve a subject-ventilator synchronic pressure supply for a non-invasive ventilator (NIV) used for obstructive sleep apnea syndrome (OSAS), solely through controlling the blower instead of using expiratory relief valves. The controller used air flow signal to distinguish inspiratory and expiratory phases, and used a fast auto-forecast (AF) algorithm to predict the current inspiratory duration based on respiration periods distinguished previously. Then in a patient's late inspiration the mechanical expiratory state was triggered to release pressure in advance. Afterward, when the beginning of a patient's actual expiration was detected, a timer was used to trigger the end-expiratory state. As the index of patient-ventilator synchrony, subject's chest movement was detected by electrical impedance signal in experiments. The experimental results indicated that the previous state transition based on the prediction of inspiratory duration can eliminate the pressure overshoot spike, and then avoid excessive expiratory pressure to subjects. The fast auto-forecast with end expiratory positive airway pressure (AF-EEPAP) controlling strategy presented in this paper provided a mild pressure curve, which is consistent with the subject's respiratory physiology, and thus improved respiratory synchrony between subject and ventilator.

Electrode Characteristics of Non-contact Electrocardiographic Measurement

The ability to take electrocardiographic measurements while performing our daily activities has become the people-choice for modern age vital sign sensing. Currently, wet and dry ECG electrodes are known to pose threats like inflammations, allergic reactions, and metal poisoning due to their direct skin interaction. Therefore, the main goal in this work is to implement a very small ECG sensor system with a capacitive coupling, which is able to detect electrical signals of heart at a distance without the conductive gel. The aim of this paper is to design, implement, and characterize the contactless ECG electrodes. Under a careful consideration of factors that affect a capacitive electrode functional integrity, several different sizes of ECG electrodes were designed and tested with a pilot ECG device. A very small cotton-insulated copper tape electrode (<TEX>$2.324cm^2$</TEX>) was finally attained that could detect and measure bioelectric signal at about 500 um of distance from the subject's chest.