Non-invasive Sensor Research Articles

Scalability of cardiovascular intrinsic frequencies: Validations in preclinical models and non-invasive clinical studies

AimsCardiovascular intrinsic frequencies (IFs) are associated with cardiovascular health and disease, separately capturing the systolic and diastolic information contained in a single (uncalibrated) arterial waveform. Previous clinical investigations related to IF have been restricted to studying chronic conditions, and hence its applicability for acute cardiovascular diseases has not been explored. Studies of cardiovascular complications such as acute myocardial infarction are difficult to perform in humans due to the high-risk and invasive nature of such procedures. Although they can be performed in preclinical (animal) models, the corresponding interpretation of IF measures and how they ultimately translate to humans is unknown. Hence, we studied the scalability of IF across species and sensor platforms. Materials and methodsScaled values of the two intrinsic frequencies ω1 and ω2 (corresponding to systolic and diastolic dynamics, respectively) were extracted from carotid waveforms acquired either non-invasively (via tonometry, Vivio or iPhone) in humans or invasively in rabbits and rats. Key findingsThe scaled IF parameters for all species were found to fall within the same physiological ranges carrying similar statistical characteristics, even though body sizes and corresponding heart rates of the species were substantially different. Additionally, results demonstrated that all non-invasive sensor platforms were significantly correlated with each other for scaled IFs, suggesting that such analysis is device-agnostic and can be applied to upcoming wearable technologies. SignificanceUltimately, our results found that IFs are scalable across species, which is particularly valuable for the training of IF-based artificial intelligence systems using both preclinical and clinical data.

Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident’s house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.

A Pilot Study of Blood Pressure Monitoring After Cardiac Surgery Using a Wearable, Non-invasive Sensor.

Background: Continuous blood pressure (BP) measurement in intensive care units is based on arterial line (AL) transducers, sometimes associated with clinical complications. Our objective was to evaluate continuous BP measurements obtained from a non-invasive, wireless photoplethysmography (PPG)-based device using two distinct configurations (wristwatch and chest-patch monitors) compared to an AL.Methods: In this prospective evaluation study, comparison of the PPG-based devices to the AL was conducted in 10 patients immediately following cardiac surgery. Pulse rate (PR), systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) were recorded using both the AL and the PPG-based devices simultaneously for an average of 432 ± 290 min starting immediately after cardiac surgery. Bland-Altman plots and Pearson's correlations were used to assess the accuracy and degree of agreement between techniques.Results: A total of ~4,000 data points were included in the final analysis. AL measurements for PR, SBP, DBP and MAP were significantly (p < 0.001) and strongly correlated with both the wristwatch (r = 0.99, r = 0.94, r = 0.93 and r = 0.96, respectively) and the chest-patch (r = 0.99, r = 0.95, r = 0.93 and r = 0.95, respectively) monitors. Both configurations showed a marginal bias of <1 mmHg for BP measurements and <1 beat/min for PR [95% limits of agreement −3,3 beat/min; BP measurements: (−6)–(−10), 6–10 mmHg] compared to AL measurements.Conclusion: The PPG-based devices offer a high level of accuracy for cardiac-related parameters compared to an AL in post-cardiac surgery patients. Such devices could provide advanced monitoring capabilities in a variety of clinical settings, including immediate post-operative and intensive care unit settings.Clinical Trial Registration:www.clinicaltrials.gov, NCT03603860.

Descriptive electromyography signals analysis of equine longissimus dorsi, rectus abdominis and gluteus medius muscles during maneuvers used to activate the core

ABSTRACT Maneuvers to activate the equine's core can make a difference in their physical and psychic health. Although these activities are recommended and practiced, there is little research proving their effectiveness. This article aims to describe, through surface electromyography, the occurrences, durations and sequences activity of longissimus dorsi, rectus abdominis and gluteus medius during thoracolumbar flexion (TLF), lumbar and lumbosacral flexion (LLSF), global flexion (GF), which is the combination of TLF and LLSF, and tail traction (TT). Seven healthy adult horses of three different breeds performed five repetitions of these movements for five seconds (sec). Electromyographic activity was captured with non-invasive superficial sensors positioned in the skin regions covering these muscles. The sequence was performed once per animal, muscle activity captured by surface electromyography, data from two replicates of each animal were selected, analyzed on matLab software and data tabulation were described during each maneuver. These maneuvers provoked punctual and transient activation of muscles mentioned above, confirming the ability to activate equine core muscles. However, responses were not standardized, which means there were variations of occurrence, duration and sequence, suggesting that for practical application of those maneuvers it is necessary to perform more repetitions with longer durations to activate more muscles.

Estimation of petroleum contents in bituminous soil using compact submersible radio frequency sensor based on artificial neural network

AbstractThis paper presents a novel miniaturized noninvasive submersible compact planar sensor to estimate petroleum contents for various industrial and remote sensing applications. The proposed sensor is based on a microstrip technique wherein a complementary split‐ring resonator is used to sense samples under interrogation. The proposed compact sensor works at a resonating frequency of 8.87 GHz with quite high sensitivity. The designed sensor can accurately detect the shift in resonant frequency for corresponding change in soil samples and different level of petroleum contents. Furthermore, the sensor gives accurate permittivity extraction of various soil samples sample using an innovative automated neural network‐based algorithm. The experimental results show that the proposed sensor can detect materials contents with good repeatability, which can be used to detect the presence of petroleum constituents in transportation pipelines and mining exploration.

Point-Of-Care Capillary Refill Technology Improves Accuracy of Peripheral Perfusion Assessment.

Background: Peripheral perfusion assessment is used routinely at the bedside by measuring the capillary refill time (CRT). Recent clinical trials have shown evidence to its ability to recognize conditions with decreased end organ perfusion as well as guiding therapeutic interventions in sepsis. However, the current standard of physician assessment at the bedside has shown large variability. New technology can improve the precision and repeatability of CRT affecting translation of previous high impact research.Methods: This was a prospective, observational study in the intensive care unit and emergency department at a quaternary care hospital using a non-invasive finger sensor for CRT. The device CRT was compared to the gold standard of trained research personnel assessment of CRT as well as to providers clinically caring for the patient.Results: Pearson correlations coefficients were performed across 89 pairs of measurements. The Pearson correlation for the device CRT compared to research personnel CRT was 0.693. The Pearson correlation for the provider CRT compared to research personnel CRT was 0.359.Conclusions: New point-of-care technology shows great promise in the ability to improve peripheral perfusion assessment performed at the bedside in the emergency department triage and during active resuscitation. This standardized approach allows for better translation of prior research that is limited by the subjectivity of manual visual assessment of CRT.

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, very few approaches with ground robots have served as moving platforms for carrying non-invasive sensors to deliver field maps that help growers in decision making. The goal of this work is to demonstrate the capability of the VineScout (developed in the context of a H2020 EU project), a ground robot designed to assess and map vineyard water status using thermal infrared radiometry in commercial vineyards. The trials were carried out in Douro Superior (Portugal) under different irrigation treatments during seasons 2019 and 2020. Grapevines of Vitis vinifera L. Touriga Nacional were monitored at different timings of the day using leaf water potential (Ψl) as reference indicators of plant water status. Grapevines’ canopy temperature (Tc) values, recorded with an infrared radiometer, as well as data acquired with an environmental sensor (Tair, RH, and AP) and NDVI measurements collected with a multispectral sensor were automatically saved in the computer of the autonomous robot to assess and map the spatial variability of a commercial vineyard water status. Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.57 in the morning time and a r2cv of 0.42 in the midday. The root mean square error of cross-validation (RMSEcv) was 0.191 MPa and 0.139 MPa at morning and midday, respectively. Spatial–temporal variation maps were developed at two different times of the day to illustrate the capability to monitor the grapevine water status in order to reduce the consumption of water, implementing appropriate irrigation strategies and increase the efficiency in the real time vineyard management. The promising outcomes gathered with the VineScout using different sensors based on thermography, multispectral imaging and environmental data disclose the need for further studies considering new variables related with the plant water status, and more grapevine cultivars, seasons and locations to improve the accuracy, robustness and reliability of the predictive models, in the context of precision and sustainable viticulture.

Compact Size, and Highly Sensitive, Microwave Sensor for Non-Invasive Measurement of Blood Glucose Level

None-invasive blood glucose measurement enables effective diagnosis and treatment of diabetic patients. The existing microwave sensors suffer from low sensitivity and large size. This article presents a highly sensitive and compact size non-invasive microwave sensor for real-time blood glucose measurements. We have developed a new sensing technique using directly connected Branch Line Coupler and Split Ring Resonators. This technique significantly improves the sensitivity by mitigating the limited coupling between transmission lines and resonators. In addition, using Split Ring Resonators reduces the sensor size due to capacitive and inductive effects, which are loaded to the Branch Line Coupler. The proposed sensing function is based on shifting the transmission notch frequency in coupled arm of the Branch Line Coupler, which is caused by loading the Split Ring Resonators with varying glucose concentration. A prototype sensor is fabricated and successfully tested using several glucose concentrations in deionized water. Experimental results demonstrate 0.72 MHz/mgdL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> measurement sensitivity, which is higher in comparison with available sensors in PCB technology. The prototype sensor size is 3.5×3.5×0.16 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> .

Robotics-based vineyard water potential monitoring at high resolution

The purpose of this research is deploying a proximal sensing solution using non-invasive and cost-effective sensors onboard an Autonomous Ground Vehicle (AGV) as a feasible way for building high-resolution maps of water potential in vineyards. The final objective is offering growers a practical system to make decisions about water management, especially for arid climatic conditions. The monitoring AGV was entirely developed within this research context, and as a result, it is a machine specifically designed to endure off-road conditions and harsh environments. The autonomous vehicle served as a massive, non-invasive, and on-the-go data collector robotic platform. The sensors used for measuring the relevant field variables were two spectral reflectance sensors (SRS), an infrared radiometer, and an on-board weather sensor. The collected data were displayed on comprehensible grid maps using the Local Tangent Plane (LTP) coordinate system. The proposed model has a coefficient of determination R2 of 0.69, and results from combining six parameters: the canopy and air temperatures (as the temperature difference), the relative humidity, the altitude difference, the Normalized Difference Vegetation Index (NDVI), and the Photochemical Reflectance Index (PRI). The strongest relationships found in this study were between the temperature difference and PRI, with an R2 of 0.75, and the temperature difference with the leaf water potential with an R2 of 0.61. The practical use of these high-resolution maps includes irrigation scheduling and harvest zoning for sorting grape quality, with a further use as inputs to complex artificial intelligence algorithms considering larger areas or complementing airborne data. Future improvements to make the models more robust and versatile will entail considering additional variables, locations, or grapevine cultivars, and even other crops grown in vertical trellis systems.

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.

It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

In-silico investigation towards the non-invasive optical detection of blood lactate

This paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood.

Increased Rotatory Laxity after Anterolateral Ligament Lesion in Anterior Cruciate Ligament- (ACL-) Deficient Knees: A Cadaveric Study with Noninvasive Inertial Sensors.

The anterolateral ligament (ALL) has been suggested as an important secondary knee restrain on the dynamic laxity in anterior cruciate ligament- (ACL-) deficient knees. Nevertheless, its kinematical contribution to the pivot-shift (PS) phenomenon has not been clearly and objectively defined, and noninvasive sensor technology could give a crucial contribution in this direction. The aim of the present study was to quantify in vitro the PS phenomenon in order to investigate the differences between an ACL-deficient knee and an ACL+ALL-deficient knee. Ten fresh-frozen paired human cadaveric knees (n = 20) were included in this controlled laboratory study. Intact, ACL-deficient, and ACL+ALL-deficient knees were subjected to a manual PS test quantified by a noninvasive triaxial accelerometer (KiRA, OrthoKey). Kinematic data (i.e., posterior acceleration of the tibial lateral compartment) were recorded and compared among the three statuses. Pairwise Student's t-test was used to compare the single groups (p < 0.05). Intact knees, ACL-deficient knees, and ACL+ALL-deficient knees showed an acceleration of 5.3 ± 2.1 m/s2, 6.3 ± 2.3 m/s2, and 7.8 ± 2.1 m/s2, respectively. Combined sectioning of ACL and ALL resulted in a statistically significant acceleration increase compared to both the intact state (p < 0.01) and the ACL-deficient state (p < 0.01). The acceleration increase determined by isolated ACL resection compared to the intact state was not statistically significant (p > 0.05). The ALL sectioning increased the rotatory laxity during the PS after ACL sectioning as measured through a user-friendly, noninvasive triaxial accelerometer.

An ultrasensitive reusable aptasensor for noninvasive diabetic retinopathy diagnosis target on tear biomarker

A sensitive, non-invasive and specific tear biomarker sensor for diabetic retinopathy (DR) could be of great significance in clinical practice. Vascular endothelial growth factor (VEGF) was targeted as a tear biomarker for DR. In principle, we used hybridization chain reaction (HCR) and CeO2 nanoparticles as a cascade signal amplification strategy to construct a reusable aptasensor in search of VEGF. The report probes H1 and H2 modified with medium CeO2 nanoparticles were added in and HCR reaction. The first analysis method for a quantitative detection of VEGF was obtained using CeO2 nanoparticles to catalyze H2O2 to generate electrical signals. The fabricated biosensor exhibited an ultrasensitive detection of VEGF down to 0.27 fg mL−1, response range from 1 fg mL−1 to 0.1 ng mL−1. Subsequently the aptasensor carried out a second repeated assembly to obtain a second signal by strand displacement reaction (SDR). The ratio of the former and latter signals was used to carry out a second analysis method for quantifying VEGF. The second analysis method had detection limits as low as 7.39 fg mL-1, ranging from 10 fg mL−1 to 0.1 ng mL−1. The aptasensor could maintain above 99 % signal in five days and shows the acceptable reproducibility. For a practical tear biomarker detection, the analysis of the tears was carried out simultaneously using ELISA and the aptasensor which showed excellent precision. This non-invasive screening method provides a potential tool for the early diagnosis and monitoring of DR and may improve screening success among high-risk patients with diabetes.

Real-time human core temperature estimation methods and their application in the occupational field: A systematic review

BackgroundThe estimation of body core temperature (CT) is a central issue in the study of the human thermoregulation response in the occupational field. The objective of this systematic review was to identify the real-time CT estimation methods based on physiological measurements acquired through wearable devices. MethodsAccording to the eligibility criteria, the articles published until 24th March 2020 were selected from Scopus, Pubmed and Web of Science and the potential risk of bias was assessed. Results16 studies were included, which enrolled a total of 353 above all young males, with tests performed mainly in the laboratory under warm/hot environmental conditions. Several CT estimation methods were found, for which performance was evaluated with different approaches. ConclusionPromising solutions appear to be emerging from the literature to enable real-time CT estimation with a small number of wearable and non-invasive sensors for application in the occupational field.

IFidgetCube: Tangible Fidgeting Interfaces (TFIs) to Monitor and Improve Mental Wellbeing

The ability to unobtrusively measure mental wellbeing states using non-invasive sensors has the potential to greatly improve mental wellbeing by alleviating the effects of high stress levels. Multiple sensors, such as electrodermal activity, heart rate and accelerometers, embedded within tangible devices pave the way to continuously and non-invasively monitor wellbeing in real-world environments. On the other hand, fidgeting tools enable repetitive interaction methods that may help to tap into individual's psychological need to feel occupied and engaged; hence potentially reducing stress. In this article, we present the design, implementation, and deployment of Tangible Fidgeting Interfaces (TFIs) in the form of computerised iFidgetCubes. iFidgetCubes embed non-invasive sensors along with fidgeting mechanisms to aid relaxation and ease restlessness. We take advantage of our labeling techniques at the point of collection to implement multiple subject-independent deep learning classifiers to infer wellbeing. The obtained performance demonstrates that these new forms of tangible interfaces combined with deep learning classifiers have the potential to accurately infer wellbeing in addition to providing fidgeting tools.

Emfit Bed Sensor Activity Shows Strong Agreement with Wrist Actigraphy for the Assessment of Sleep in the Home Setting.

PurposeWrist-worn actigraphy via research-grade devices, a well-established approach to the assessment of rest-activity, is limited by poor compliance, battery life, and lack of direct evidence for time spent physically in the bed. A non-invasive bed sensor (Emfit) may provide advantages over actigraphy for long-term sleep assessment in the home. This study compared sleep-wake measurements between this sensor and a validated actigraph.Patients and MethodsThirty healthy subjects (6 to 54 years) underwent simultaneous monitoring with both devices for 14 days and filled out a daily sleep diary. Parameters included bed entry time, sleep start, sleep end, bed exit time, rest interval duration, and wake after sleep onset (WASO). The agreement between the two devices was measured using Bland–Altman plots and inter-class correlation coefficients (ICC). In addition, sensitivity, specificity, and accuracy were obtained from epoch-by-epoch comparisons of Emfit and actigraphy.ResultsFifteen percent of the subjects reported that wearing the actigraph was a burden. None reported that using the bed sensor was a burden. The minimal detectable change between Emfit and actigraphy was 11 minutes for bed entry time, 14 minutes for sleep start, 14 minutes for sleep end, 10 minutes for bed exit time, 20 minutes for rest interval duration, and 110 minutes for WASO. Inter-class correlation coefficients revealed an excellent agreement for all sleep parameters (ICC=0.99, 95% CI 98–99) except for WASO (ICC=0.46, 95% CI 0.33–0.56). Sensitivity, specificity, and accuracy were 0.62, 0.93, and 0.88, respectively. Kappa correlation analysis revealed a moderate correlation between the two devices (κ=0.55, p<0.0001).ConclusionEmfit is an acceptable alternative to actigraphy for the estimation of bed entry time, sleep start, sleep end, bed exit time, and rest interval duration. However, WASO estimates are poorly correlated between the two devices. Emfit may offer methodological advantages in situations where actigraphy is challenging to implement.

Sap Flow Disruption in Grapevine Is the Early Signal Predicting the Structural, Functional, and Genetic Responses to Esca Disease.

Fungal species involved in Esca cause the formation of grapevine wood necroses. It results in the deterioration of vascular network transport capacity and the disturbance of the physiological processes, leading to gradual or sudden grapevine death. Herein, for two consecutive growing seasons, a detailed analysis of the structural (wood necrosis and leaf discoloration) and physiological parameters related to the water use of healthy and esca-symptomatic grapevines was conducted. Measurements were carried out on 17-year-old grapevines that expressed, or not, Esca-leaf symptoms in a vineyard of the Bordeaux region (France). Whole-plant transpiration was recorded continuously from pre-veraison to harvest, using noninvasive sap flow sensors. Whole-plant transpiration was systematically about 40–50% lower in Esca-diseased grapevines compared with controls, and this difference can be observed around 2 weeks before the first Esca-foliar symptoms appeared in the vineyard. Unlike grapevine sap flow disruption, structural (e.g., leaf discolorations), functional (e.g., stomatal conductance, photosynthetic activity, phenolic compounds), and genetic (e.g., expression of leaf-targeted genes) plant responses were only significantly impacted by Esca at the onset and during leaf symptoms development. We conclude that sap flow dynamic, which was related to a high level of a white-rot necrosis, provides a useful tool to predict plant disorders due to Esca-grapevine disease.

Pattern Recognition of Interturn Short Circuit Fault in a Synchronous Generator Using Magnetic Flux

This article provides a novel approach for discriminating the interturn short circuit (ITSC) fault in a salient pole synchronous generator (SPSG) based on the use of noninvasive sensors. A stray magnetic field is used as a signal that can provide valuable data regarding the health condition of the SPSG. Generally, the captured signal under an ITSC fault is examined using a classical signal processing tool based on fast Fourier transforms (FFTs). The amplitude of the side-bands to the frequency spectrum is increased by ITSC, eccentricity, and broken damper-bar faults. Therefore, determining the type of fault is not possible by analyzing the FFT side-bands. A unique feature is introduced using a short-time Fourier transform to identify the ITSC in its early stage. The proposed feature can locate and recognize the ITSC fault with high accuracy in either the no-load or the full-load operation of the SPSG. This novel methodology is verified by applying an ITSC fault to a 100 kVA custom-made SPSG.

Wearable Intracranial Pressure Monitoring Sensor for Infants.

Monitoring of intracranial pressure (ICP) is important for patients at risk of raised ICP, which may indicate developing diseases in brains that can lead to brain damage or even death. Monitoring ICP can be invaluable in the management of patients suffering from brain injury or hydrocephalus. To date, invasive measurements are still the standard method for monitoring ICP; however, these methods can not only cause bleeding or infection but are also very inconvenient to use, particularly for infants. Currently, none of the non-invasive methods can provide sufficient accuracy and ease of use while allowing continuous monitoring in routine clinical use at low cost. Here, we have developed a wearable, non-invasive ICP sensor that can be used like a band-aid. For the fabrication of the ICP sensor, a novel freeze casting method was developed to encapsulate the liquid metal microstructures within thin and flexible polymers. The final thickness of the ICP sensor demonstrated is 500 µm and can be further reduced. Three different designs of ICP sensors were tested under various pressure actuation conditions as well as different temperature environments, where the measured pressure changes were stable with the largest stability coefficient of variation being only CV = 0.0206. In addition, the sensor output values showed an extremely high linear correlation (R2 > 0.9990) with the applied pressures.

Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review

The number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors.