Multiple Human Subjects Research Articles

Traditional Chinese Medicine (TCM)-Inspired Fully Printed Soft Pressure Sensor Array with Self-Adaptive Pressurization for Highly Reliable Individualized Long-Term Pulse Diagnostics.

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

Continuous Ketone Monitoring via Wearable Microneedle Patch Platform.

Ketone bodies (KBs), especially β-hydroxybutyrate (BHB), have gained tremendous attention as potential biomarkers as their presence in bodily fluids is closely associated with health and wellness. While a variety of blood fingerstick test strips are available for self-testing of BHB, there are major needs for wearable devices capable of continuously tracking changing BHB concentrations. To address these needs, we present here the first demonstration of a wearable microneedle-based continuous ketone monitoring (CKM) in human interstitial fluid (ISF) and illustrate its ability to closely follow the intake of ketone drinks. To ensure highly stable and selective continuous detection of ISF BHB, the new enzymatic microneedle BHB sensor relies on a gold-coated platinum working electrode modified with a reagent layer containing toluidine blue O (TBO) redox mediator, β-hydroxybutyrate dehydrogenase (HBD) enzyme, a nicotinamide adenine dinucleotide (NAD+) cofactor, along with carbon nanotubes (CNTs), chitosan (Chit), and a poly(vinyl chloride) (PVC) outer protective layer. The skin-worn microneedle sensing device operates with a miniaturized electrochemical analyzer connected wirelessly to a mobile electronic device for capturing, processing, and displaying the data. Cytotoxicity and skin penetration studies indicate the absence of potential harmful effects. A pilot study involving multiple human subjects evaluated continuous BHB monitoring in human ISF, against gold standard BHB meter measurements, revealing the close correlation between the two methods. Such microneedle-based CKM offers considerable promise for dynamic BHB tracking toward the management of diabetic ketoacidosis and personal nutrition and wellness.

Dodecanogram (DDG): Advancing EEG technology with a high- frequency brain activity measurement device

EEG measures electric potential changes in the scalp. Even though it has been associated with human thoughts, there has been no direct evidence. The problem with EEG is that it measures variations in current or electric potential in the millisecond time domain, where muscle movement strongly affects the readings. The millisecond time domain is equivalent to the kHz resonance signal generated by any dielectric resonator, and every single cell membrane resonates in this time range. So, the measurement of EEG could come simply from the skin and not from the brain. Therefore, we have advanced EEG technology with the dodecanogram (DDG), which reveals 12 frequency bands or 12 discrete time regions where brain activities are most significant. We measure brain activity using a stream of pulses and a logic analyzer that counts ultra-short pulses needed to emulate brain scalp potential changes. We have created another version of DDG where, using an array of RLC (resistor-inductor-capacitor) resonators, we sense the ultra-low-power electromagnetic radiation from different locations on the brain's surface. Since we measure signals from Hz to THz, covering 12 orders of time ranges as a property of dielectric resonance, unlike EEG, there is a high probability that the DDG signal may truly originate from the brain. We have monitored DDG on an artificial organic brain replica 24/7 for over a year and on multiple human subjects, before and after meditation and concluded that most cognitive, perceptive, and emotional bursts occur around 200-700 nanoseconds, not milliseconds, as it was believed for 150 years of EEG era.

Robotic Optical Coherence Tomography of Human Subjects with Posture-Invariant Head and Eye Alignment in Six Degrees of Freedom.

Ophthalmic optical coherence tomography (OCT) has achieved remarkable clinical success but remains sequestered in ophthalmology specialty offices. Recently introduced robotic OCT systems seek to expand patient access but fall short of their full potential due to significant imaging workspace and motion planning restrictions. Here, we present a next-generation robotic OCT system capable of imaging in any head orientation or posture that is mechanically reachable. This system overcomes prior restrictions by eliminating fixed-base tracking components, extending robot reach, and planning alignment in six degrees of freedom. With this robotic system, we show repeatable subject imaging independent of posture (standing, seated, reclined, and supine) under widely varying head orientations for multiple human subjects. For each subject, we obtained a consistent view of the retina, including the fovea, retinal vasculature, and edge of the optic nerve head. We believe this robotic approach can extend OCT as an eye disease screening, diagnosis, and monitoring tool to previously unreached patient populations.

Wireless and Multi‐Person Respiration Monitoring using Facemask‐Integrated Flexible Meta‐Antennas

AbstractRespiration monitoring of a large population is important in containing the spread of viral respiratory infections such as the coronavirus disease 2019 (COVID‐19). Current technologies, however, lack the ability in respiration monitoring of multiple human subjects in a long‐term, robust, and low‐cost manner. Herein, wireless respiration monitoring of multiple human subjects using facemask‐integrated flexible meta‐antennas is demonstrated. The flexible meta‐antenna has an architecture of multi‐layered anisotropic hole‐array, which is optimized by theory and simulations to achieve high performances including good antenna gain, robustness against body interferences, and high air permeability favorable for facemask integration. A person's respiration patterns and respiration rates are wirelessly obtained by the meta‐antenna integrated with a temperature‐sensor‐embedded chip. Respiration monitoring of multiple subjects in long range and long term during daily activities is simultaneously demonstrated. In addition, a real‐time data processing system is introduced in which a local server, a cloud server, and an application layer are implemented for the real‐time display of respiration patterns and automatic recognition of abnormal status. The design of flexible meta‐antennas may lead to a distinct class of physiological sensors over a large population for applications in pandemic control and personalized healthcare.

Multitarget Physical Activities Monitoring and Classification Using a V-Band FMCW Radar

This work proposes a radar-based physical activities monitoring solution capable of detecting, classifying, and counting the number of repetitions of multiple human subjects doing different exercises simultaneously in front of the radar, using only one monostatic radar. Four exercises were performed in the experiments: sit-ups, push-ups, squats, and jump rope which are very common exercises performed in and out of the gym without the need for expensive gym equipment. A 60-GHz frequency-modulated continuous-wave (FMCW) radar was used in the experiments. The extracted data from the radar were processed using MATLAB R2021b, and the features were extracted using range–Doppler frames and micro-Doppler (mD) analysis generating a spectrogram. By combining these two analyses, information about the range, time, Doppler, and radar cross section (RCS) can be extracted for each individual. To validate the feasibility and robustness of the work in realistic scenarios, static and moving clutter were added to some of the measurements.

Numerical Simulation of Mechanically Adaptive Bone Remodeling Around Teeth and Implants: A Comparison with Clinical Images

The results of numerical simulation of mechanically adaptive bone remodeling have been compared with clinical images. Cone beam computed tomography (CBCT) images of multiple human subjects were superimposed to obtain a continuous bone density spatial distribution map inside the mandible supporting the lateral incisor. Strain energy density in the bone under normal chewing and biting forces was computed using finite element analysis. A bone remodeling algorithm was implemented to compute the bone density distribution at equilibrium. Linear regression analysis was performed between the bone density computed by numerical simulation and that obtained from image analysis, for every trabecular bone element. The results exhibited close agreement with a coefficient of correlation of 0.8499. The bite forces were transmitted through tooth roots to the surrounding bone, thus stimulating high trabecular bone density near the roots. The bending and torsion moments on the sagittal section of the mandible resulted in lower bone density near the center than those towards the edge of the mandible. The results provide a new method to compare the results of adaptive bone remodeling simulation with experimental data, and also provide model parameters to predict the bone density distribution surrounding a dental implant that replaced the tooth.

Simultaneous Monitoring of Multiple People’s Vital Sign Leveraging a Single Phased-MIMO Radar

Vital sign monitoring plays a critical role in tracking the physiological state of people and enabling various health-related applications (e.g., recommending a change of lifestyle, examining the risk of diseases). Traditional approaches rely on hospitalization or body-attached instruments, which are costly and intrusive. However, in recent years there is an emergence of contact-less vital sign monitoring techniques that rely on radio frequency signals. Early studies with continuous wave radars/WiFi devices have shown good success in detecting the vital signs of a single individual, while simultaneous monitoring of the vital signs of multiple, closely spaced subjects remains a challenge. In this paper, using an off-the-shelf Texas Instrument automotive FMCW radar, we design and implement a time-division multiplexing (TDM) phased-MIMO radar sensing system that allows high-precision vital sign monitoring of multiple subjects. The proposed sensing system can steer the beam towards the desired directions with a micro-second delay. The steerable beam enables capturing the vital signs of multiple individuals at the same radial distance to the radar. The proposed system enables the formation of a virtual array with aperture longer than that of the physical array. A Capon beamformer is used at the receiver side to combine the data collected from different transmit and receive antenna pairs corresponding to the virtual array. As all those pairs provide independent information about the targets, their combination significantly boosts the receiver signal-to-noise ratio. Based on the designed TDM phased-MIMO radar, we develop a system to automatically localize multiple human subjects and estimate their vital signs. Extensive evaluations show that under two-subject scenarios, our system can achieve more than 98.06% accuracy for breathing rate (BR) and more than 82.89% accuracy for heartbeat rate (HR) estimation, at a subject-to-radar distance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.6}\;m$</tex-math></inline-formula> when the targets are facing the radar. The minimal subject-to-subject angle separation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30^\circ $</tex-math></inline-formula> at a subject-to-radar distance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.6}\;m$</tex-math></inline-formula> , corresponding to a close distance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{0.3}\;m$</tex-math></inline-formula> between two subjects, which outperforms the state-of-the-art.

Ultrasound-Guided Assistive Robots for Scoliosis Assessment With Optimization-Based Control and Variable Impedance

Assistive robots for healthcare have witnessed a growing demand over the past decades. In this letter, we investigate the development of an optimization-based control framework with variable impedance for an assistive robot to perform ultrasound-guided scoliosis assessment. The conventional procedure for scoliosis assessment using ultrasound imaging typically requires a medical practitioner to slide an ultrasound probe along a patient’s back while maintaining a certain magnitude of the contact force. To automate such a procedure, we need to consider multiple objectives, such as contact force, position, orientation, energy, posture, etc. To coordinate different objectives, we propose to formulate the control framework as a quadratic programming problem with each objective weighted by a tunable task priority, subject to a set of equality and inequality constraints. As the procedure requires the robot to establish a constant contact force with the patient during scanning, we incorporate variable impedance regulation of the end-effector to enhance safety and stability during the physical human-robot interaction; The variable impedance gains are then retrieved by learning from medical expert’s demonstrations. The proposed methodology is evaluated with a robotic system performing autonomous scoliosis assessment with multiple human subjects involved. The effectiveness of our approach is verified by the coronal spinal images obtained with the robot.

Human Tracking in Top-View Fisheye Images: Analysis of Familiar Similarity Measures via HOG and against Various Color Spaces.

The purpose of this paper is to find the best way to track human subjects in fisheye images by considering the most common similarity measures in the function of various color spaces as well as the HOG. To this end, we have relied on videos taken by a fisheye camera wherein multiple human subjects were recorded walking simultaneously, in random directions. Using an existing deep-learning method for the detection of persons in fisheye images, bounding boxes are extracted each containing information related to a single person. Consequently, each bounding box can be described by color features, usually color histograms; with the HOG relying on object shapes and contours. These descriptors do not inform the same features and they need to be evaluated in the context of tracking in top-view fisheye images. With this in perspective, a distance is computed to compare similarities between the detected bounding boxes of two consecutive frames. To do so, we are proposing a rate function () in order to compare and evaluate together the six different color spaces and six distances, and with the HOG. This function links inter-distance (i.e., the distance between the images of the same person throughout the frames of the video) with intra-distance (i.e., the distance between images of different people throughout the frames). It enables ascertaining a given feature descriptor (color or HOG) mapped to a corresponding similarity function and hence deciding the most reliable one to compute the similarity or the difference between two segmented persons. All these comparisons lead to some interesting results, as explained in the later part of the article.

Human Activity Classification Based on Micro-Doppler Signatures Separation

Human activity classification based on micro-Doppler (m-D) signatures finds applications in surveillance, search and rescue operations, and healthcare. In this article, we propose a new approach for human activity classification. This approach deals with the situations of reduced limb movements that could be due to the presence of injury or an individual carrying objects. It applies a preprocessing step to separate human m-D signals of the limbs from the Doppler signal corresponding to the torso. The separated m-D signal is input to a two-layer convolutional principal component analysis network (CPCAN) for feature extraction and motion classification. The CPCAN comprises a simple network architecture for efficient training and implementation, and it automatically learns the highly discriminative features. Experiments involving multiple human subjects performing different activities show a high classification accuracy associated with small arm motions.

Multitarget Vital Signs Measurement With Chest Motion Imaging Based on MIMO Radar

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

A coarse-to-fine detection and localization method for multiple human subjects under through-wall condition using a new telescopic SIMO UWB radar

Radar-based life detecting technology has been researched more and more in recent years and applied widely in several areas. However, few researches on multi-subjects detecting and locating have been reported. Even these researches are mainly based on imaging technology with two shortcomings, namely the interference of ghost and difficult to focus the target’s position. A new coarse-to-fine approach for detecting and locating of human targets using single-input multiple-output (SIMO) radar under penetration conditions is proposed. The approach realized automatic recognition and localization of human subjects without any prior information, such as target count. Firstly, an improved censored mean level detector constant false alarm rate (CMLD-CFAR) by adding a feature to distinguish vital sign from background is proposed in each inner-channel to coarsely determine the targets’ time-of-arrival (TOA) distances. Secondly, the real targets count is finely determined by calculating the inter-channel Pearson correlation coefficient of signals from different channels. Then, a back projection (BP) is presented to calculate the targets’ 2-D locations. In the experiments of single and multi-target locating, all targets were identified correctly and localized near their actual positions with errors within 0.3 m. With the distinctive telescopic structure of the proposed radar, the azimuth resolution of locating is adjustable. Compare to current radar system for radar multi-target locating, like multiple-input multiple-output (MIMO) system, the proposed system can improve accuracy and automation of localization while decline the cost and complexity. It has potential to promote practical application of locating trapped human targets in post-disaster rescue and intelligent homeware fields.

Robotics Meets Cosmetic Dermatology: Development of a Novel Vision-Guided System for Skin Photo-Rejuvenation

In this article, we present a novel robotic system for skin photo-rejuvenation, which uniformly delivers laser thermal stimulation to a subject&#x2019;s facial skin tissue. The robotized treatment is performed by a customized manipulator instrumented with a cosmetic laser generator, a depth sensor, and a thermal camera. To plan the optimal trajectory of the manipulator, our system computes the surface model of the face and segments it into seven regions, then automatically fills these regions with uniform laser irradiation. Finally, we report the detailed results of experiments on multiple human subjects to validate the performance of the system. To the best of the author&#x2019;s knowledge, this is the first time that facial skin rejuvenation has been automated by robot manipulators.

Multiskin Color Segmentation Through Morphological Model Refinement

This study used dynamic maps for skin color detection and applied a morphological closing operator to identify supplementary pixels in the image space to update the color map. Dual constraints were considered in the refinement system to prevent overestimation, which are the centroid of the skin color region in image space and dynamic range in color model space. As a result, the proposed system enables us to update the skin color map continuously. This paper presents a simple, rapid, and effective method for single skin color detection. This paper proposes skin color detection for images involving multiskin color regions (i.e., multiple human subjects of different races) by using the AdaBoost face detection mechanism. The experimental results reveal that the proposed method is highly robust for detection of diverse skin colors and simultaneous occurrences of multiskin color regions.

Motion-Tolerant Non-Contact Heart-Rate Measurements from Radar Sensor Fusion.

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 s of radar measurements from multiple human subjects.

Multitarget Respiration Detection With Adaptive Digital Beamforming Technique Based on SIMO Radar

The Doppler radar has been widely used in respiration detection. However, most of the existing microwave respiration detection works are intended for either a single human subject in front of the radar or multiple subjects with known positions. In this article, a system based on single-input–multiple-output (SIMO) continuous-wave (CW) radar equipped with adaptive digital beamforming (ADBF) technique is presented to detect the respiration of multiple human subjects at unknown positions simultaneously. A solution based on the modified Capon (m-Capon) direction-of-arrival (DOA) estimation and linear constraint minimal variance (LCMV) ADBF is proposed to automatically find the angles of human subjects. By forming spatially distributed beams toward the subjects of interest, human respiration can be remotely obtained. Furthermore, for the detection of each person’s respiration, nulls are also generated at the angles of nearby interfering subjects, which results in high target discrimination capability when multiple human subjects are close to each other. The experimental results show good detection accuracy compared with the reference sensor, which verifies the effectiveness of the developed system for multitarget respiration detection.

Fine-Grained Activity of Daily Living (ADL) Recognition Through Heterogeneous Sensing Systems With Complementary Spatiotemporal Characteristics

Non-intrusive monitoring of fine-grained activities of daily living (ADL) enables various smart healthcare applications. For example, ADL pattern analysis for older adults at risk can be used to assess their loss of safety or independence. Prior work in the area of ADL recognition has focused on coarse-grained ADL recognition at the activity level (e.g., cooking, cleaning, sleeping), and/or course-grained (hourly or minutely) activity duration segmentation. It also typically relies on a high-density deployment of a variety of sensors. Finer-grained (sub-second-level and event-based) ADL recognition, could provide more detailed ADL information, which is crucial for enabling the assessment of patients' activity patterns and potential changes in behavior. To achieve this fine-grained ADL monitoring, we present a heterogeneous multi-modal cyber-physical system, where we use 1) distributed vibration sensors to capture the action-induced structural vibrations and their spatial characteristics for information aggregation, and 2) single point electrical sensor to capture appliance usage with high temporal resolution. To evaluate our system, we conducted real-world experiments with multiple human subjects to demonstrate the complementary information from these two sensing modalities. Our system achieved an average 90% accuracy in recognizing activities, which is up to 2.6x higher than baseline systems considering each state-of-the-art sensing modality separately.

Through the Wall Human Subject Localization and Respiration Rate Detection Using Multichannel Doppler Radar

In this article, through-the-wall human subject localization and respiration rate detection using a multi-channel Doppler radar is proposed. The proposed methodology is used to detect respiration signal of the subject when the environment between the radar and the subject is wood, glass or any other material which resembles real-life situations. A major achievement is that angular position of multiple human subjects and their respiration rates are estimated using this technique. The proposed system also removes the null point detection problem which is present in single channel CW radar system for vital sign detection. The noise level is reduced using cross-correlation between received signals from different channels. Also, the received signals from different channels are added in phase to improve the detection. The system is compact enough to be carried on a micro-drone. This work finds application in real-life situations such as earthquake, medical-care, building collapse and patient monitoring.