Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition.

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.

Jugular Venous Pulse Waveform Extraction From a Wearable Radio Frequency Sensor.

Many prevalent heart diseases can be indicated by the features of the jugular venous pulse (JVP), an efficacious indicator of right heart health. However, JVP dynamics are not widely utilized in clinical settings as its observation and sensing remain cumbersome. Non-invasive measures of cardiac behavior, including the JVP, are of growing interest to enable continuous and at-home monitoring of cardiac disorders. In this work, we propose a wearable near-field radio-frequency (RF) sensor affixed with a neck collar on the clavicle over the internal jugular vein to enable non-invasive JVP sensing. We employed a complex vector injection signal processing method to extract repeatable JVP waveform features in multiple postures. With a 21-subject human study, we demonstrated morphologically consistent JVP sensing with consistent a-, c-, and v-wave feature timings, benchmarked by synchronous electrocardiogram and phonocardiogram. Further, inter-postural experiments demonstrated the capability of the proposed system to quantify morphological changes to the JVP which are present in many cardiac disorders. The results of this work suggest the proposed near-field RF sensor is capable of non-invasive JVP monitoring, potentially enabling improved sensing in both clinical and ambulatory environments.

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor.

This work presents a study on users' attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user's baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively.

Furniture-Integrated Respiration Sensors by Notched Transmission Lines.

Non-invasive respiration sensors integrated into furniture can be invisible to the user and greatly enhance comfort and convenience to facilitate many applications. Current sensors often require user cooperation or fitting, which discourages frequent usage. We present a new respiration sensor integrated into a bed or a chair by modifying a radio-frequency (RF) coaxial cable structure with a designed notch. The lung motion is coupled to the electromagnetic leakage at the notch through near-field coherent sensing (NCS). The sensors, covered with fabrics and positioned under the abdomen and thorax, can capture the respiratory waveforms and derive the breath rate. The heart rate can also be evaluated in the same setup with proper filtering. The sensor design can tolerate large position variation to accommodate user uncertainties. Various voluntary exercises of normal, deep, fast, held and blocked breathing were measured under different postures of supine, recumbent and sitting by the carrier frequency range between 900MHz and 2.4GHz. The breath rate from 10 participants compare well with the synchronous commercial chest-belt sensors in all breathing routines.

Systolic Time Interval Extraction in Hypertensive and Hypotensive Pig Models Using Wearable Near-Field Radio-Frequency Sensors.

Screening and monitoring for cardiovascular diseases (CVDs) can be enabled by analyzing systolic time intervals (STIs). As CVDs have a strong causal correlation with hypertension, it is important to validate STI sensor accuracy in hypertensive hearts to ensure consistent performance in this prevalent cardiac disease state. This work presents STI extraction using a non-invasive near-field radio-frequency (RF) sensor during normotension, hypertension, and hypotension in a pig model. Waveform features of semilunar and atrioventricular valve dynamics during systole were extracted to derive isovolumic contraction time (ICT) and left ventricular ejection time (LVET), benchmarked by a phonocardiogram and aortic catheterization. Study-wide mean relative ICT and LVET errors were -4.4ms and -3.6ms, respectively, demonstrating high accuracy during both normal and abnormal systemic pressures.Clinical relevance- This work demonstrates accurate STI extraction with relative error less than 5 ms from a non-invasive near-field RF sensor during normotensive, hypotensive, and hypertensive systemic pressures, validating the sensor's accuracy as a screening tool during this disease state.

Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors.

OBJECTIVE: Respiratory disturbances during sleep are a prevalent health condition that affects a large adult population. The gold standard to evaluate sleep disorders including apnea is overnight polysomnography, which requires a trained technician for live monitoring and post-processing scoring. Currently, the disorder events can hardly be predicted using the respiratory waveforms preceding the events. The objective of this paper is to develop an autonomous system to detect and predict respiratory events reliably based on real-time covert sensing. METHODS: A bed-integrated radio-frequency (RF) sensor by near-field coherent sensing (NCS) was employed to retrieve continuous respiratory waveforms without user's awareness. Overnight recordings were collected from 27 patients in the Weill Cornell Center for Sleep Medicine. We extracted respiratory features to feed into the random-forest machine learning model for disorder detection and prediction. The technician annotation, derived from observation by polysomnography, was used as the ground truth during the supervised learning. RESULTS: Apneic event detection achieved a sensitivity and specificity up to 88.6% and 89.0% for k-fold validation, and 83.1% and 91.6% for subject-independent validation. Prediction of forthcoming apneic events could be made up to 90 s in advance. Apneic event prediction achieved a sensitivity and specificity up to 81.3% and 82.1% for k-fold validation, and 80.5% and 82.4% for subject-independent validation. The most important features for event detection and prediction can be assessed in the learning model. CONCLUSION: A bed-integrated RF sensor can covertly and reliably detect and predict apneic events. SIGNIFICANCE: Predictive warning of the sleep disorders in advance can intervene serious apnea, especially for infants, servicemen, and patients with chronic conditions.

Physiological Features of Cardiac Ventricle and Valve Dynamics from Wearable Radio-Frequency Sensors.

Early detection of cardiovascular diseases via non-invasive, convenient, and continuous monitoring is crucial to reducing preventable deaths. This paper illustrates such monitoring using wearable near-field radio-frequency sensors to analyze ventricle and valve transients, which can be used as indicators of myriad cardiac disorders. We applied a novel vector injection signal processing method to improve timing consistency in ventricular contraction, ventricular relaxation, and valve opening extraction. The median relative timing error in valve opening detection was 14.7ms and 37.8ms for semilunar and atrioventricular valves, respectively, as benchmarked by the S1 and S2 heart sounds from a synchronous phonocardiogram. Clinical Relevance- No wearable sensor currently exists to conveniently and reliably evaluate ventricular and valvular dynamics, specifically valvular opening. Beyond extraction of the heart rate and its variation, the method in this paper has the potential to enable non-invasive measurements of detailed cardiac cycle timing features including valve openings, isovolumetric contraction/relaxation times, and ejection periods, improving the monitoring of patient health away from clinical healthcare centers.

Objective Scoring of Physiologically Induced Dyspnea by Non-Invasive RF Sensors.

OBJECTIVE: Dyspnea, also known as the patient's feeling of difficult or labored breathing, is one of the most common symptoms for respiratory disorders. Dyspnea is usually self-reported by patients using, for example, the Borg scale from 0 - 10, which is however subjective and problematic for those who refuse to cooperate or cannot communicate. The objective of this paper was to develop a learning-based model that can evaluate the correlation between the self-report Borg score and the respiratory metrics for dyspnea induced by exertion and increased airway resistance. METHODS: A non-invasive wearable radio-frequency sensor by near-field coherent sensing was employed to retrieve continuous respiratory data with user comfort and convenience. Self-report dyspnea scores and respiratory features were collected on 32 healthy participants going through various physical and breathing exercises. A machine learning model based on the decision tree and random forest then produced an objective dyspnea score. RESULTS: For unseen data as well as unseen participants, the objective dyspnea score can be in reasonable agreement with the self-report score, and the importance factor of each respiratory metrics can be assessed. CONCLUSION: An objective dyspnea score can potentially complement or substitute the self-report for physiologically induced dyspnea. SIGNIFICANCE: The method can potentially formulate a baseline for clinical dyspnea assessment and help caregivers track dyspnea continuously, especially for patients who cannot report themselves.

Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning.

Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild coughs are usually overlooked in the early stage, not only because they are barely noticeable by the person and the people around, but also because the present recording method is not comfortable, private, or reliable for long-term monitoring. In this paper, a wearable radio-frequency (RF) sensor is presented to recognize the mild cough signal directly from the local trachea vibration characteristics, and can isolate interferences from nearby people. The sensor operates at the ultra-high-frequency band, and can couple the RF energy to the upper respiratory track by the near field of the sensing antenna. The retrieved tissue vibration caused by the cough airflow burst can then be analyzed by a convolutional neural network trained on the frequency-time spectra. The sensing antenna design is analyzed for performance improvement. During the human study of 5 participants over 100 minutes of prescribed routines, the overall recognition ratio is above 90% and the false positive ratio during other routines is below 2.09%.

Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate.

Many health diagnostic systems demand noninvasive sensing of respiratory rate, respiratory volume, and heart rate with high user comfort. Previous methods often require multiple sensors, including skin-touch electrodes, tension belts, or nearby off-the-body readers, and hence are uncomfortable or inconvenient. This paper presents an over-clothing wearable radio-frequency sensor study, conducted on 20 healthy participants (14 females) performing voluntary breathing exercises in various postures. Two prototype sensors were placed on the participants, one close to the heart and the other below the xiphoid process to couple to the motion from heart, lungs and diaphragm, by the near-field coherent sensing principle. We can achieve a satisfactory correlation of our sensor with the reference devices for the three vital signs: heart rate (r = 0.95), respiratory rate (r = 0.93) and respiratory volume (r = 0.84). We also detected voluntary breath-hold periods with an accuracy of 96%. Further, the participants performed a breathing exercise by contracting abdomen inwards while holding breath, leading to paradoxical outward thorax motion under the isovolumetric condition, which was detected with an accuracy of 83%.

Field modulation in bilayer graphene band structure.

Using an external electric field, one can modulate the band gap of Bernal stacked bilayer graphene by breaking the A-[Formula: see text] symmetry. We analyze strain effects on the bilayer graphene using the extended Hückel theory and find that reduced interlayer distance results in higher band gap modulation, as expected. Furthermore, above about 2.5 Å interlayer distance, the band gap is direct, follows a convex relation with the electric field and saturates to a value determined by the interlayer distance. However, below about 2.5 Å, the band gap is indirect, the trend becomes concave and a threshold electric field is observed, which also depends on the stacking distance.

No-touch measurements of vital signs in small conscious animals.

Measuring the heartbeat and respiration of small conscious animals is important for assessing their health and behavior, but present techniques such as electrocardiogram (ECG), ultrasound, and auscultation rely on close skin contact with the animal. These methods can also require surface preparation, cause discomfort or stress to animals, and even require anesthetic administration, especially for birds, reptiles, and fish. Here, we show that radio frequency near-field coherent sensing (NCS) can provide a new solution to animal vital sign monitoring while ensuring minimal pain and distress. We first benchmarked NCS with synchronous ECG on an anesthetized rat. NCS was then applied to monitor a conscious hamster from outside its cage, and was further extended to a parakeet, Russian tortoise, and betta fish in a noninvasive manner. Our system can revolutionize vital sign monitoring of small conscious animals in their laboratory living quarters or natural habitats.

A Wearable RF Sensor for Monitoring Respiratory Patterns.

We present a non-invasive approach for continuous monitoring of respiration dynamics using a wearable radio-frequency (RF) sensor based on near-field coherent sensing. A continuous-wave RF signal at 1.8 GHz is generated by a software-defined radio, with both transmitter (Tx) and receiver (Rx) antennas placed close to the xiphoid process. The experimental prototype of the mobile sensor can modulate the internal organ motion in the near-field region of the Tx antenna and is then received by the nearby Rx antenna to be demodulated and sampled. Through peak detection, we have identified inhalation and exhalation peaks of each breath cycle to estimate the breath rate and the lung volume. The extracted respiratory parameters are compared with the conventional chest belts data for various simulated respiratory conditions including voluntary deep, fast-shallow and slow-shallow breathing. We also characterized simulated central sleep apneas, Cheyne-Stokes, Biot's, ataxic and coughing conditions. To accurately identify obstructive apnea, we presented a two-sensor approach that can capture paradoxical movement of thorax and abdomen. The on-line recognition of these respiratory patterns can be employed not only to continuously monitor patients with chronic respiratory disorders but also to provide real-time feedback for future therapeutic purposes.

Non-Faradaic electrochemical detection of protein interactions by integrated neuromorphic CMOS sensors.

Electronic detection of the binding event between biotinylated bovine serum albumen (BSA) and streptavidin is demonstrated with the chemoreceptive neuron MOS (CnuMOS) device. Differing from the ion-sensitive field-effect transistors (ISFET), CnuMOS, with the potential of the extended floating gate determined by both the sensing and control gates in a neuromorphic style, can provide protein detection without requiring analyte reference electrodes. In comparison with the microelectrode arrays, measurements are gathered through purely capacitive, non-Faradaic interactions across insulating interfaces. By using a (3-glycidoxypropyl)trimethoxysilane (3-GPS) self-assembled monolayer (SAM) as a simple covalent link for attaching proteins to a silicon dioxide sensing surface, a fully integrated, electrochemical detection platform is realized for protein interactions through monotone large-signal measurements or small-signal impedance spectroscopy. Calibration curves were created to coordinate the sensor response with ellipsometric measurements taken on witness samples. By monitoring the film thickness of streptavidin capture, a sensitivity of 25ng/cm2 or 2A of film thickness was demonstrated. With an improved noise floor the sensor can detect down to 2ng/(cm2mV) based on the calibration curve. AC measurements are shown to significantly reduce long-term sensor drift. Finally, a noise analysis of electrochemical data indicates 1/f(alpha) behavior with a noise floor beginning at approximately 1Hz.

Breathable and Flexible Piezoelectric ZnO@PVDF Fibrous Nanogenerator for Wearable Applications.

A novel breathable piezoelectric membrane has been developed by growing zinc oxide (ZnO) nanorods on the surface of electrospun poly(vinylidene fluoride) (PVDF) nanofibers using a low-temperature hydrothermal method. Significant improvement in the piezoelectric response of the PVDF membrane was achieved without compromising breathability and flexibility. PVDF is one of the most frequently used piezoelectric polymers due to its high durability and reasonable piezoelectric coefficient values. However, further enhancement of its piezoelectric response is highly desirable for sensor and energy-harvester applications. Previous studies have demonstrated that piezoelectric ceramic and polymer composites can have remarkable piezoelectric properties and flexibility. However, devices made of such composites lack breathability and some present health risks in wearable applications for containing heavy metals. Unlike other piezoelectric ceramics, ZnO is non-toxic material and has been widely used in many applications including cosmetics. The fabrication of ZnO@PVDF porous electrospun membrane involves a simple low-temperature ZnO growth in aqueous solution, which does not weaken the polarization of PVDF created during electrospinning in the high electric field.

HIT or miss? A comprehensive contemporary investigation of laboratory tests for heparin induced thrombocytopenia.

Heparin induced thrombocytopenia (HIT) is a rare but potentially fatal complication of heparin therapy, which in a proportion of patients causes platelet activation and thrombosis. Initial clinical assessment of the likelihood of HIT is facilitated by laboratory testing to confirm or exclude HIT. This prospective investigation was performed over an 18-month period, and has involved testing of over 300 test samples from over 100 consecutive patients. Clinical assessment by 4T score was supplemented by laboratory tests that comprised both immunological [lateral flow ('STiC'), chemiluminescence (AcuStar; HIT-IgG(PF4-H)), ELISA (Asserachrom HPIA IgG)] and functional assays [SRA, platelet aggregation using whole blood ('Multiplate') and platelet rich plasma ('LTA')]. We observed both false positive and false negative test findings with most assays. Overall, the whole blood aggregation method provided a reasonable alternative to SRA for identifying functional HIT. STiC, AcuStar and ELISA procedures were fairly comparable in terms of screening for HIT, although STiC and AcuStar both yielded false negatives, albeit also resulting in fewer false positives than ELISA. The 4T score had less utility in our patient cohort than we were expecting, although there was an association with the likelihood of HIT. Nevertheless, we accept that our observations are based on limited test numbers. In conclusion, no single approach (clinical or laboratory) was associated with optimal sensitivity or specificity of HIT exclusion or identification, and thus, a combination of clinical evaluation and laboratory testing will best ensure the accuracy of diagnosis.

Laboratory Testing Protocols for Heparin-Induced Thrombocytopenia (HIT) Testing.

Heparin-induced thrombocytopenia (HIT) represents a significant high morbidity complication of heparin therapy. The clinicopathological diagnosis of HIT remains challenging for many reasons; thus, laboratory testing represents an important component of an accurate diagnosis. Although there are many assays available to assess HIT, these essentially fall into two categories-(a) immunological assays, and (b) functional assays. The current chapter presents protocols for several HIT assays, being those that are most commonly performed in laboratory practice and have the widest geographic distribution. These comprise a manual lateral flow-based system (STiC), a fully automated latex immunoturbidimetric assay, a fully automated chemiluminescent assay (CLIA), light transmission aggregation (LTA), and whole blood aggregation (Multiplate).

Laboratory Testing for Von Willebrand Factor Multimers.

Von Willebrand disease (VWD) is reportedly the most common inherited bleeding disorder and can also arise as an acquired syndrome (AVWS). These disorders develop due to defects and/or deficiency of the plasma protein von Willebrand factor (VWF). Laboratory testing for the VWF-related disorders requires assessment of both VWF level and VWF activity, the latter requiring multiple assays because of the many functions carried out by VWF to help prevent bleeding. As an additional step, an evaluation of VWF structural features by multimer analysis is useful in selective investigations. The current paper therefore describes a protocol for assessment of VWF multimers by gel electrophoresis, thus enabling identification of protein bands that represent differently sized multimers. The sample protocol described in this chapter is the methodology developed by Sebia.

A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability.

BACKGROUND: Computationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability. NEW METHOD: Here we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for "fastDTW" in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings. RESULTS: Our method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA+k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude. COMPARISON WITH EXISTING METHOD(S): In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability. CONCLUSIONS: We have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.