Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications.

The alteration of the hydrostatic pressure gradient in the human body has been associated with changes in human physiology, including abnormal blood flow, syncope, and visual impairment. The focus of this study was to evaluate changes in the resonant frequency of a wearable electromagnetic resonant skin patch sensor during simulated physiological changes observed in aerospace applications. Simulated microgravity was induced in eight healthy human participants (n = 8), and the implementation of lower body negative pressure (LBNP) countermeasures was induced in four healthy human participants (n = 4). The average shift in resonant frequency was -13.76 ± 6.49 MHz for simulated microgravity with a shift in intracranial pressure (ICP) of 9.53 ± 1.32 mmHg, and a shift of 8.80 ± 5.2097 MHz for LBNP with a shift in ICP of approximately -5.83 ± 2.76 mmHg. The constructed regression model to explain the variance in shifts in ICP using the shifts in resonant frequency (R2 = 0.97) resulted in a root mean square error of 1.24. This work demonstrates a strong correlation between sensor signal response and shifts in ICP. Furthermore, this study establishes a foundation for future work integrating wearable sensors with alert systems and countermeasure recommendations for pilots and astronauts.

A Flexible Near-Field Biosensor for Multisite Arterial Blood Flow Detection.

Modern wearable devices show promising results in terms of detecting vital bodily signs from the wrist. However, there remains a considerable need for a device that can conform to the human body's variable geometry to accurately detect those vital signs and to understand health better. Flexible radio frequency (RF) resonators are well poised to address this need by providing conformable bio-interfaces suitable for different anatomical locations. In this work, we develop a compact wearable RF biosensor that detects multisite hemodynamic events due to pulsatile blood flow through noninvasive tissue-electromagnetic (EM) field interaction. The sensor consists of a skin patch spiral resonator and a wearable transceiver. During resonance, the resonator establishes a strong capacitive coupling with layered dielectric tissues due to impedance matching. Therefore, any variation in the dielectric properties within the near-field of the coupled system will result in field perturbation. This perturbation also results in RF carrier modulation, transduced via a demodulator in the transceiver unit. The main elements of the transceiver consist of a direct digital synthesizer for RF carrier generation and a demodulator unit comprised of a resistive bridge coupled with an envelope detector, a filter, and an amplifier. In this work, we build and study the sensor at the radial artery, thorax, carotid artery, and supraorbital locations of a healthy human subject, which hold clinical significance in evaluating cardiovascular health. The carrier frequency is tuned at the resonance of the spiral resonator, which is 34.5 ± 1.5 MHz. The resulting transient waveforms from the demodulator indicate the presence of hemodynamic events, i.e., systolic upstroke, systolic peak, dicrotic notch, and diastolic downstroke. The preliminary results also confirm the sensor's ability to detect multisite blood flow events noninvasively on a single wearable platform.

Spatial Detection of the Shafts of Fractured Femur for Image-Guided Robotic Surgery.

Femur fractures due to traumatic forces often require surgical intervention. Such surgeries require alignment of the femur in the presence of large muscular forces up to 500 N. Currently, orthopedic surgeons perform this alignment manually before fixation, leading to extra soft tissue damage and inaccurate alignment. One of the limitations of femoral fracture surgery is the limited vision and two-dimensional nature of X-ray images, which typically guide the surgeon in diagnosing the position of the femur. Other limitations include the lack of precise intraoperative planning and the process of trial-and-error alignment. To alleviate the issues discussed, we develop a marker-based approach for detecting the position of femur fragments using two X-ray images. The relative spatial position of the femur fragments plays a key role in guiding an innovative robotic system, named Robossis, for femur fracture alignment surgeries. Using the derived three-dimensional data, we simulate pre-programmed movements to visualize the proposed steps of the alignment method, while the bone fragments are attached to the robot. Ultimately, Robossis aims to improve the accuracy of femur alignment, which results in improved patient outcomes.

Passive Self Resonant Skin Patch Sensor to Monitor Cardiac Intraventricular Stroke Volume Using Electromagnetic Properties of Blood.

This paper focuses on the development of a passive, lightweight skin patch sensor that can measure fluid volume changes in the heart in a non-invasive, point-of-care setting. The wearable sensor is an electromagnetic, self-resonant sensor configured into a specific pattern to formulate its three passive elements (resistance, capacitance, and inductance). In an animal model, a bladder was inserted into the left ventricle (LV) of a bovine heart, and fluid was injected using a syringe to simulate stoke volume (SV). In a human study, to assess the dynamic fluid volume changes of the heart in real time, the sensor frequency response was obtained from a participant in a 30° head-up tilt (HUT), 10° HUT, supine, and 10° head-down tilt positions over time. In the animal model, an 80-mL fluid volume change in the LV resulted in a downward frequency shift of 80.16 kHz. In the human study, there was a patterned frequency shift over time which correlated with ventricular volume changes in the heart during the cardiac cycle. Statistical analysis showed a linear correlation [Formula: see text] and 0.87 between the frequency shifts and fluid volume changes in the LV of the bovine heart and human participant, respectively. In addition, the patch sensor detected heart rate in a continuous manner with a 0.179% relative error compared to electrocardiography. These results provide promising data regarding the ability of the patch sensor to be a potential technology for SV monitoring in a non-invasive, continuous, and non-clinical setting.