Non-invasive Sensor Research Articles

Assessment of a non-invasive accelerometer for detecting cattle urination and defecation events

Nitrogen (N) excreted in the urine from cattle is the primary source of N leaching loss from grazed pasture systems. The objective of this study was to use non-invasive accelerometer sensors to detect the time and duration of urination and defecation events from grazing cattle. Two accelerometer sensors were attached to cows to detect the back arching of cows during urination and defecation events. Trials were conducted under outdoor grazing conditions in autumn and summer with a total of 160 urination recorded events. Changes in tilt angle of 8–22.5 degrees were observed during most urination and defecation events. Ensemble machine learning models to predict urination and defecation events were developed based on 18 selected features of a cow back arching event. The accelerometer urination event classifier had an F1 statistic of 0.80 (harmonic mean of precision (0.82) and sensitivity (0.78)) and the defecation event classifier had an F1 statistic of 0.73 (based on 408 h of data obtained 9a.m. to 3p.m. from 19 cows). Models to predict urination event duration were significant but had low performance partly due to the moderate association between the duration of back arching and the duration of the urination event (R2 of 0.29 and 0.47 for the autumn and summer trials, respectively). This demonstrates the potential for non-invasive accelerometer sensors to identify the urination characteristics of individual cows and provide individual farm information that could be used as an input parameter in farm decision support tools (e.g. urine-N excretion) and farm management decisions (e.g. culling/breeding).

Effects of Hallux Valgus Surgery on Balance and Gait in Middle Aged and Older Adults

Hallux valgus is associated with balance deficits, and has been implicated as an independent risk factor for falls in older adults. However, it is unknown what effect hallux valgus surgery has on static and dynamic (i.e., while walking) balance in older adults. We enrolled 13 middle-aged and older aged adults (mean age 54.3 ± 12.7 years, range 47 to 70) who underwent isolated hallux valgus surgery and followed them for 12 months. Preoperative and postoperative gait and balance performance was assessed using non-invasive body worn sensors with standardized and validated testing protocols. Visual analog scale (VAS) for pain and radiographic angles were also assessed. All subjects reported improvements in pain (VAS mean change -38.3 ± 10.3 mm), and all subjects demonstrated improvements in their hallux valgus angles and first/second intermetatarsal angles (mean change 16.3 ± 8.8°, and 5.5 ± 3.0°, respectively). While standing in full tandem, center of mass (COM) sway was improved upon by 59% at 1 year postoperative (p < .05, paired t-test). While most gait parameters demonstrated little change postoperatively, patients tended to spend less time in double support (p = .08, paired t-test), while gait variability increased by 55% (p = .03, paired t-test) and medial-lateral sway while walking increased by 43% (p = .08, paired t-test) 12 months postoperatively. Balance improved after hallux valgus surgery in our population, particularly when subjects were forced to rely on their operative foot for support (e.g., full tandem). Patients also seemed to walk with greater variability in stride velocity and with greater medial-lateral sway postoperatively, suggesting perhaps increased ambulatory confidence after successful hallux valgus surgery.

Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene

Objective. Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.

One-Dimensional Systemic Modeling of Thermal Sensors Based on Miniature Bead-Type Thermistors.

Accurate measurements of thermal properties is a major concern, for both scientists and the industry. The complexity and diversity of current and future demands (biomedical applications, HVAC, smart buildings, climate change adapted cities, etc.) require making the thermal characterization methods used in laboratory more accessible and portable, by miniaturizing, automating, and connecting them. Designing new materials with innovative thermal properties or studying the thermal properties of biological tissues often require the use of miniaturized and non-invasive sensors, capable of accurately measuring the thermal properties of small quantities of materials. In this context, miniature electro-thermal resistive sensors are particularly well suited, in both material science and biomedical instrumentation, both in vitro and in vivo. This paper presents a one-dimensional (1D) electro-thermal systemic modeling of miniature thermistor bead-type sensors. A Godunov-SPICE discretization scheme is introduced, which allows for very efficient modeling of the entire system (control and signal processing circuits, sensors, and materials to be characterized) in a single workspace. The present modeling is applied to the thermal characterization of different biocompatible liquids (glycerol, water, and glycerol–water mixtures) using a miniature bead-type thermistor. The numerical results are in very good agreement with the experimental ones, demonstrating the relevance of the present modeling. A new quasi-absolute thermal characterization method is then reported and discussed. The multi-physics modeling described in this paper could in the future greatly contribute to the development of new portable instrumental approaches.

Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system

A method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network.

Highly Stretchable Wearable Electrochemical Sensor Based on Ni-Co MOF Nanosheet-Decorated Ag/rGO/PU Fiber for Continuous Sweat Glucose Detection

A noninvasive fiber material-based wearable electrochemical sensor to continuously monitor the glucose level in sweat is highly desirable for smart fabrics for personal diabetes management. To achieve it, the key challenge is to construct fibers with high stretchability and excellent electrochemical performance. Herein, a highly stretchable Ni-Co metal-organic framework/Ag/reduced graphene oxide/polyurethane (Ni-Co MOF/Ag/rGO/PU) fiber-based wearable electrochemical sensor is fabricated for monitoring the glucose level in sweat continuously with high sensitivity and accuracy. The rGO/PU fiber was simply produced by an improved wet spinning technology, and the Ni-Co MOF nanosheet was coated on its surface to prepare the Ni-Co MOF/Ag/rGO/PU (NCGP) fiber electrode. The Ni-Co MOF has a large specific surface area and high catalytic activity, which enables the fiber sensors with good electrochemical performance with a high sensitivity of 425.9 μA·mM-1·cm-2 and a wide linear range of 10 μM-0.66 mM. More importantly, the NCGP fiber electrode also shows extremely high stretching and bending stability under mechanical deformation. Also, the NCGP fiber electrode has high selectivity and long-time storage stability. Moreover, the NCGP fiber-based three-electrode system was sewn with an absorbent fabric and fixed on a stretchable polydimethylsiloxane film substrate to form a nonenzymatic sweat glucose wearable sensor, which realized real-time monitoring of glucose in human sweat with high accuracy. This indicates that our designed NCGP fiber can be used as a wearable electrochemical sensor for the bio-diagnostics of body sweat.

High-Efficiency Large-Area Printed Multilayer Liquid Metal Wires for Stretchable Biomedical Sensors with Recyclability.

Stretchable conductors are essential for soft robots, wearable on-skin electronic technologies, and bioelectronics. The utilization of sophisticated stretchable conductors requires a new, simple, rapid, and large-scale printing process whose features include high stretchability, high precision, multilayers, and recyclability simultaneously for commercial wearable electronics. To address this need, an LM (liquid metal) wire was developed using a simple, rapid, and large-scale soft stamper-based printing process and employed to realize LM wire-based conductors and capacitors, which simultaneously offer high stretchability (>380%), high precision past 50 μm, and electromechanical response stability after stretching for up to an hour. Based on the excellent electromechanical responses, the LM wire-based capacitors, as strain sensors, attached to finger joints resulted in precise gesture detection. Meanwhile, a simple transparent wearable e-skin consisting of a 6 × 6 LM wire-based capacitor array without rigid parts successfully monitored a multi-point touch. At last, a portable noninvasive stretchable multilayer LM wire-based pulse sensor with recyclability is fabricated to monitor the patient's heartbeats. The experimental results reveal that the stretchable biomedical sensors have the potential to help patients to improve their life in healthcare, including replacement prosthetic devices, daily and sports activity tracking, continuous health monitoring, and others.

Fabrication of a Mesoporous Multimetallic Oxide-based Ion-Sensitive Field Effect Transistor for pH Sensing.

Sensitive and reliable noninvasive sensors are in demand to cope with an increasing need for robust working conditions and fast results. One of the leading potential technologies is field-effect transistor (FET)-based sensors to improve response time, sensitivity, and stability. Here, a sol–gel method fabricates an ion-sensitive field-effect transistor with a high current and output sensitivity for electrochemical sensing, solving binary device design, component regulating, and long-term stability, while maintaining the promoted sensitivity. Metal oxide-based devices with single and binary contents are fabricated and characterized for monitoring pH changes, with performance fitted to a Nernst–Poisson model. After detecting the performance, the result was compared with devices in different components and ratios to obtain excellent performance and high stability. In addition, these extended gate FETs with multimetallic oxide promise efficiency and stability optimization in terms of a flexible component design, demonstrating the feasibility of the novel sol–gel fabrication method to achieve efficient and reliable FET sensors.

CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain.

The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term ‘CyberEye’ to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable.

SenseMag: Enabling Low-Cost Traffic Monitoring Using Noninvasive Magnetic Sensing

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers' privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least $90\%$ vehicle type classification accuracy and less than 5\% vehicle length classification error.

Reduced Graphene Oxide-Coated Silica Nanospheres as Flexible Enzymatic Biosensors for Detection of Glucose in Sweat

Noninvasive flexible electrochemical sensors for body fluid analysis are subject to strict requirements for sensitivity and detection limit. In this work, a three-dimensional (3D) flexible biosenso...

TiO2 anchored carbon fibers as non-invasive electrochemical sensor platform for the cortisol detection

An electrochemical immunosensor integrated on carbon yarns for non-invasive detection of cortisol is reported. The carbon yarns were integrated with TiO2 nanostructures for immobilizing cortisol specific antibodies. While the integration of TiO2 nanostructure was characterized using XRD and FE-SEM, the biorecognition of cortisol by antibodies modified yarns was investigated using voltammetric techniques (CV and DPV). The carbon yarn based immunosensor showed good sensitivity and is responsive over a broad-range of cortisol concentration (10 fg to 1 μg/mL). The efficacy of the cortisol sensing platform towards cortisol estimation in human sweat was evaluated and validated using commercially available chemiluminescence assay. The results demonstrated the excellent sensing performance of the TiO2 carbon fiber based flexible immunosensor system and its great potential in developing non-invasive POC sensor for human sweat cortisol detection.

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72–0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.

Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement

Many sports have a high risk of climatic ailments, such as hypothermia, hyperthermia, and heatstroke. The measurement of a sportsperson's body core temperature (Tc) may have an impact on their performances and it assists them to avoid injuries as well. To avoid complications like electrolyte imbalances or infections, it's essential to precisely measure the core body temperature during targeted temperature control when spontaneous circulation has returned. Previous approaches on the other hand, are intrusive and difficult to use. The usual technique, an oesophageal thermometer, was compared to a disposable non-invasive temperature sensor that used the heat flux methodology. This research indicates that, non-invasive disposable sensors used to measure core body temperature are very reliable when used for targeted temperature control after overcoming a cardiac arrest successfully. The non-invasive method of temperature measurement has somewhat greater accuracy than the invasive approach. The results of this study must be confirmed by more clinical research with various sensor types to figure out if the bounds of agreement could be increased. This will ensure that the findings are accurate based on core temperature.

Brain Light-Tissue Interaction Modelling: Towards a non-invasive sensor for Traumatic Brain Injury.

Traumatic brain injury (TBI) is one of the leading causes of death worldwide, yet there is no systematic approach to monitor TBI non-invasively. The main motivation of this work is to create new knowledge relating to light brain interaction using a Monte Carlo Model, which could aid in the development of non-invasive optical sensors for the continuous assessment of TBI. To this aim, a multilayer model tissue-model of adult human head was developed and explored at the near-infrared optical wavelength. Investigation reveals that maximum light (40-50%) is absorbed in the skull and the minimum light is absorbed in the subarachnoid space (0-1%). It was found that the absorbance of light decreases with increasing source-detector separation up to 3cm where light travels through the subarachnoid space, after which the absorbance increases with the increasing separation. Such information will be helpful towards the modelling of neurocritical brain tissue followed by the sensor development.

Association of Longitudinal Sleep and Next-day Indoor Mobility Measured via Passive Sensors among Community-dwelling Older Adults.

Previous studies have shown there is a relationship between sleep and mobility in older adults by collecting and analysing self-reported data from surveys and questionnaires, or by using objective measures from polysomnography or actigraphy. However, these methods have limitations for long-term monitoring, especially for community-dwelling adults. In this paper, we investigate the association between sleep and indoor mobility using longitudinal data collected over a period of about 12 months for older adults (65 years or older) living at home in Australia. The data was collected objectively and continuously using non-invasive and passive sensors. First, we explored whether sleep and indoor mobility are different across gender and age groups (70s, 80s, and 90s). Second, we investigate the association of sleep and next-day indoor mobility through a stepwise multivariate regression. We found that males and females have significant differences in mobility, time in bed, total time in sleep, number and duration of awakenings and sleep efficiency. Additionally, mobility and all sleep measures significantly vary across the three age groups, except for sleep onset latency between 80s and 90s. Our findings show that sleep efficiency and total sleep time are the key sleep measures affecting next-day mobility, while sleep onset latency has the least effect.Clinical relevance - Our study contributes to a better understanding of the sleep patterns of older adults and how they affect their physical functioning.

Blood pressure wave propagation—a multisensor setup for cerebral autoregulation studies

Objective. Cerebral autoregulation is critically important to maintain proper brain perfusion and supply the brain with oxygenated blood. Non-invasive measures of blood pressure (BP) are critical in assessing cerebral autoregulation. Wave propagation velocity may be a useful technique to estimate BP but the effect of the location of the sensors on the readings has not been thoroughly examined. In this paper, we were interested in studying whether the propagation velocity of a pressure wave in the direction from the heart to the brain may differ compared with propagation from the heart to the periphery, as well as across different physiological tasks and/or health conditions. Using non-invasive sensors simultaneously placed at different locations of the human body allows for the study of how the propagation velocity of the pressure wave, based on pulse transit time (PTT), varies across different directions. Approach. We present a multi-sensor BP wave propagation measurement setup intended for cerebral autoregulation studies. The presented sensor setup consists of three sensors, one placed on each of the neck, chest and finger, allowing simultaneous measurement of changes in BP propagation velocity towards the brain and to the periphery. We show how commonly tested physiological tasks affect the relative changes of PTT and correlations with BP. Main results. We observed that during maximal blow, valsalva and breath hold breathing tasks, the relative changes of PTT were higher when PTT was measured in the direction from the heart to the brain than from the heart to the peripherals. In contrast, during a deep breathing task, the relative change in PTT from the heart to the brain was lower. In addition, we present a short literature review of the PTT methods used in brain research. Significance. These preliminary data suggest that the physiological task and direction of PTT measurement may affect relative PTT changes. The presented three-sensor setup provides an easy and neuroimaging compatible method for cerebral autoregulation studies by allowing measurement of BP wave propagation velocity towards the brain versus towards the periphery.

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment.

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.

Non-invasive measurement of oil-water two-phase flow in vertical pipe using ultrasonic Doppler sensor and gamma ray densitometer

Oil–water two-phase flow experiments were conducted in a vertical pipe to study the liquid–liquid flow measurement using a non-invasive ultrasound Doppler flow sensor and a gamma-ray densitometer. Tap water and a dyed mineral oil are used as test the fluids. A novel Doppler effect strategy is used to estimate the mixture flow velocity in a vertical pipe based on flow velocity measured by the ultrasound sensor and the shear flow velocity profile model. Drift-flux flow model was used in conjunction phase fractions to predict the superficial velocities of oil and water. The results indicate that the proposed method estimated oil-continuous flow and water-continuous flow have average relative errors of 5.2% and 4.5%; and superficial phase flow velocities of oil and of water have average relative errors of 4.5% and 5.9% respectively. These results demonstrate the potential for using ultrasonic Doppler sensor combined with gamma densitometer for oil–water measurement.

Development of Non-Invasive Impedance Sensors for Real Time Monitoring of Bacteria Growth

Mucosal surface in the gastrointestinal track and oral cavity are colonized by more than 700 types of microorganism. The bacteria interaction is depended on the location of its formation and dynamics. Streptococcus mutans, is known pathogenic bacteria present in oral microbiome, which metabolizes sucrose and other carbohydrates to produce lactic acid and lowers the pH 5.5 or less, which causes caries and periodontal diseases. Therefore, study of growth and dynamics of Sm bacteria is critical to understand oral cavity formation. Fluorescent in-situ hybridization probe (FISH) with confocal laser scanning microscopy (CLSM) is widely used for monitoring of biofilm with spatial resolution. However, these sophisticated instruments are not economically viable for multiple replication or high throughput applications. Here, we have used single frequency impedance spectroscopy as fast and non-invasive technique to monitor real time growth of Sm biofilm and kinetics of its formation.Real time monitoring of Sm biofilm was performed by the continuous recording of impedance while Sm bacteria was grown on glass substrate in BMM with 30 mM sucrose feeding. The cell-sensors impedance has been reported in the arbitrary unit as normalized impedance called cell-index (CI). Sensors were first tested in phosphate buffer solution (PBS) and then changed to BMM as background signal. After stabilization of sensors, streptococcus mutane was injected. There is increase of CI after Sm injection from zero to 0.3 at 10kHz frequency then decreased ~ 0.2. The decrease of CI at 10 kHz is lactic acid release by the Sm bacteria which causes decrease in solution resistance. The CI value at 1 Hz is increased from zero to ~ 1 after bacteria inoculation. The increase in CI at lower frequency is due to growth of Sm bacteria on the electrodes surface which causes increase of capacitance. After 56 hrs of growth of Sm, surfactant sodium dodecyl sulphate (SDS, CMC) was injected for investigation of sensitivity of impedancemetry sensors on destruction of bacteria biofilm. There is decrease in CI value at 1 Hz suggests that Sm biofilm matrix was partially collapsed. So, this study suggests that, high frequency impedance-based sensor can be used as solution resistance sensors and low frequency impedance based sensor can be used as capacitive based sensor for monitoring of biofilm growth.