Portable Wireless Device Research Articles

A new wireless device for bedside assessment of tip location of central venous access devices using intracavitary ECG: A retrospective study.

Intracavitary electrocardiography (IC-ECG) is a non-invasive method for intraprocedural tip location during central venous catheterization. Over the last 20 years, the IC-ECG method has been thoroughly investigated and many studies have confirmed its wide applicability and feasibility, as well as its great accuracy and safety in different populations of patients and in different types of central venous access devices (CVADs). This retrospective study presents a two-center experience with tip location of central venous catheters using IC-ECG, by means of a new wireless portable device. Tip location was performed during 983 insertions of CVADs at bedside, using this new device. Clinical conditions with limited or no applicability of IC-ECG were excluded. The feasibility of IC-ECG, as performed using the novel device, was 92.2%. This wireless device may play an important role in central venous catheterization at bedside, since it is pocket-sized, particularly easy to use, and easy to sanify after use.

A smart device of data acquisition with emergency safety features for laboratory furnaces

Laboratory furnaces or ovens are found in scientific, forensic, medicinal, and material processing labs. They provide precise temperature control and homogenous heating for samples in industrial and research applications but cannot measure the sample's interior temperature and most of them do not have the facility to store temperature data. Moreover, a furnace cannot promptly shut off if a sample or the furnace catches fire due to overheating or an electrical failure. To solve these problems, in this work, an intelligent, automated add-on device with wireless monitoring and emergency safety features to use with any commercial furnace or oven is presented. The device can individually measure furnace and sample temperatures at the same time using a two-channel K-type thermocouple unit. The system can store the real-time temperature data generated by the thermocouples, display it on LCD (Liquid Crystal Display) and give the facility to observe this real-time data from anywhere within an area of 800–1000-m radius using the portable wireless monitor device without the need for Wi-Fi or internet. Additionally, the device can quickly detect fire or overheating, immediately cuts the power automatically, activates a loud alarm and dials the user's phone itself using Global System for Mobile Communication (GSM) technology to warn them.

Wireless portable bioelectronic nose device for multiplex monitoring toward food freshness/spoilage

Monitoring food freshness/spoilage is important to ensure food quality and safety. Current methods of food quality monitoring are mostly time-consuming and labor intensive processes that require massive analytical equipment. In this study, we developed a portable bioelectronic nose (BE-nose) integrated with trace amine-associated receptor (TAAR) nanodiscs (NDs), allowing food quality monitoring via the detection of food spoilage indicators, including the biogenic amines cadaverine (CV) and putrescine (PT). The olfactory receptors TAAR13c and TAAR13d, which have specific affinities for CV and PT, were produced and successfully reconstituted in ND structures. TAAR13 NDs BE-nose-based side-gated field-effect transistor (SG-FET) system was constructed by utilizing a graphene micropattern (GM) into which two types of olfactory NDs (TAAR13c ND and TAAR13d ND) were introduced, and this system showed ultrahigh sensitivity for a limit of detection (LOD) of 1 fM for CV and PT. Moreover, the binding affinities between the TAAR13 NDs and the indicators were confirmed by a tryptophan fluorescence quenching assay and biosimulations, in which the specific binding site was confirmed. Gas-phase indicators were detected by the TAAR13 NDs BE-nose platform, and the LODs for CV and PT were confirmed to be 26.48 and 7.29 ppb, respectively. In addition, TAAR13 NDs BE-nose was fabricated with commercial gas sensors as a portable platform for the measurement of NH3 and H2S, multiplexed monitoring was achieved with similar performance, and the change ratio of the indicators was observed in a real sample. The integration of commercial gas sensors on a BE-nose enhanced the accuracy and reliability for the quality monitoring of real food samples. These results indicate that the portable TAAR13 NDs BE-nose can be used to monitor CV and PT over a wide range of concentrations, therefore, the electronic nose platform can be utilized for monitoring the freshness/spoilage step in various foods.

Rolling up of 2D nanosheets into 1D Nanoscrolls: Visible-Light-Activated chemiresistors based on surface modified indium selenide with enhanced sensitivity and stability

Two-dimensional (2D) materials with ultra-thin structure hold much promise for application in constructing gas sensors at room temperature. Unfortunately, the production capacity of ultra-thin 2D materials with high-quality is very limited, and the prepared sensors often suffer from slow/incomplete recovery and poor long-term stability. In this study, a novel gas sensor based on carbon/oxygen functional groups-modified indium selenide (C-InSe) nanoscrolls was demonstrated. First, large-quantity and ultra-thin InSe nanosheets were obtained through electrochemical exfoliation, and assembled into nanoscrolls with heat treatment. Compared with C-InSe nanosheets sensors, the nanoscrolls devices exhibited better response intensity (381% per ppm) and faster recovery speed (∼200 s) for NO2 determination under visible light irradiation. In addition, the sensors possessed excellent selectivity, repeatability and low limit of detection (LOD) of 0.43 ppb. The superior gas-sensing performance and ambient stability can be attributed to the tubular-like framework and surface functional groups of C-InSe, and the mechanisms were investigated through experiments, calculations and simulations. Finally, the sensing chip was integrated into a portable wireless device and fulfilled NO2 monitoring in tail gas samples. This work presents a promising strategy for development of 2D materials-based sensing platform with high performance, miniaturized size, long-term stability as well as low-power consumption.

A dual-band CPW-fed miniature planar antenna for S-, C-, WiMAX, WLAN, UWB, and X-band applications

A miniature planar antenna is a vital component of any portable wireless communication device. The antenna in portable devices should provide wide/multiple operating bands to cover a good number of narrowband services as a multi-band antenna not only reduces the number of antennas but also lessens the system complexity, cost, and device size. To operate over S-, C-, WiMAX, WLAN, UWB, and X-communication bands, in this paper, a dual-band CPW-fed antenna is presented. The anticipated antenna is made up of a vertical bow-tie-shaped patch and two asymmetric ground planes and etched on the same side of the single-sided standard substrate material. To generate two distinct operating bands, an inverted L-shaped parasitic element is inserted within the modified U-shaped coplanar ground plane. The antenna achieved dual operating bands of 3.24–8.29 GHz and 9.12–11.25 GHz in measurement which helps the proposed antenna to cover S-, C-, WiMAX, WLAN, 4G LTE, 5G sub-6 GHz, UWB, and X-communication bands. In the two operating bands, the antenna realized a peak gain of 4.33 dBi, and 4.80 dBi, the maximum radiation efficiency of 86.6%, and 72.6%, and exhibits symmetric radiation patterns. In the operating bands, the antenna also exhibits good time-domain behavior which helps it to transmit the signal with minimum distortion.

Self-adhesive, stretchable, and dry silver nanorods embedded polydimethylsiloxane biopotential electrodes for electrocardiography

Biopotential signals are extremely useful in assessing organ function and diagnosing diseases. This study proposes a highly flexible, conductive, antibacterial, and self-adhesive silver nanorods (AgNRs) embedded polydimethylsiloxane (PDMS) dry electrode for long-term electrocardiogram (ECG) monitoring with portable instrumentation. We use a unique glancing angle deposition method to fabricate AgNRs and embedded them in a biocompatible PDMS matrix. These electrodes do not cause skin irritation even after several hours of use and work efficiently without skin preparation. These AgNRs-PDMS electrodes have an electrical resistivity of 10−7 Ω m and a skin contact impedance of 93.9 ± 0.7 kΩ to 6.2 ± 3.7 kΩ for frequencies ranging from 40 Hz to 1 kHz, which is around 18% less than most conventional Ag/AgCl wet electrodes. The fabricated electrodes have a signal-to-noise ratio of 12 dB, comparable to that of a conventional Ag/AgCl electrode. A signal acquisition circuit is designed to detect ECG signals combined with proposed dry electrodes and a wireless monitoring device. Finally, real-time ECG signals are displayed on a mobile phone via an Android application. These AgNRs-PDMS dry electrodes, in combination with the portable wireless device, may be used in future clinical studies that require real-time and long-term ECG monitoring.

Graphene Field-Effect Transistor Based High-Performance Wireless Portable Device for H1N1 Detection

Graphene Field-Effect Transistor Based High-Performance Wireless Portable Device for H1N1 Detection

Multi-layered BTO/PVDF nanogenerator with highly enhanced performance induced by interlaminar electric field

The flexible piezoelectric nanogenerator (PNG) has been spotlighted as a promising candidate for sustainable power source in wireless portable device. Here, a multi-layered BaTiO3 (BTO) /poly(vinylidene fluoride) (PVDF) PNG has been designed by spin coating and layer by layer assembled method. Benefited from the composite strategy and interlaminar electric field effect, the typical four-layered BTO/PVDF nanogenerator contained with 20 wt% BTO nanoparticles represents excellent output performance with voltage of 14.22 V (about 6.49 times than that of 1 layered PNG), current of 2.42 μA, and maximum power of 11.55 μW. The dielectric constant and residual polarization of the PNG vary with the number of piezoelectric layers, which is suspected as the root cause for the enhancement of piezoelectric and output performance. In addition, 4000 pressing-releasing cycles test, finger tapping and foot striking measurements also demonstrated excellent stability of the synthesized BTO/PVDF PNG, showing their great potential in wireless sensing and wearable device applications.

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device.

Emotion recognition is of great importance for artificial intelligence, robots, and medicine etc. Although many techniques have been developed for emotion recognition, with certain successes, they rely heavily on complicated and expensive equipment. Skin potential (SP) has been recognized to be correlated with human emotions for a long time, but has been largely ignored due to the lack of systematic research. In this paper, we propose a single SP-signal-based method for emotion recognition. Firstly, we developed a portable wireless device to measure the SP signal between the middle finger and left wrist. Then, a video induction experiment was designed to stimulate four kinds of typical emotion (happiness, sadness, anger, fear) in 26 subjects. Based on the device and video induction, we obtained a dataset consisting of 397 emotion samples. We extracted 29 features from each of the emotion samples and used eight well-established algorithms to classify the four emotions based on these features. Experimental results show that the gradient-boosting decision tree (GBDT), logistic regression (LR) and random forest (RF) algorithms achieved the highest accuracy of 75%. The obtained accuracy is similar to, or even better than, that of other methods using multiple physiological signals. Our research demonstrates the feasibility of the SP signal’s integration into existing physiological signals for emotion recognition.

Accuracy of the SpineNav3DTM Ultrasound Technology in Estimating the Epidural Space Depth for Epidural and Spinal Insertion in Pregnant Obese Patients

Introduction: Ultrasound facilitates neuraxial puncture in obese parturients. Unfortunately, the widespread adoption of neuraxial ultrasound may be limited by the lack of technical expertise or the limited availability of the equipment. A wireless portable ultrasound device (Accuro, Rivanna Medical, Charlottesville, VA) with automated pattern recognition software (SpineNav3DTM technology) has been introduced to obtain the automated real-time identification of interspaces and epidural depth. The primary objective of this study was to assess the accuracy of the SpineNav3DTM ultrasound technology (Accuro) in estimating the epidural space depth compared to the standard ultrasound examination in pregnant obese patients. The secondary aim was to compare the ultrasound measurements with the measured needle depth during epidural and spinal insertion. Methods: The study was conducted at S Gerardo Hospital, Monza, Italy from March 2021 to April 2021. Obese laboring women requesting epidural analgesia or undergoing elective cesarean delivery under spinal anesthesia were recruited. All the subjects had their lumbar area scanned for the measurements of the depth of the epidural space by the SpineNav3DTM ultrasound technology and by the standard US and then both compared with the needle insertion depth in a double-blind fashion. Results: Forty-eight women were enrolled in the study. There was agreement (±0.25 cm) between the epidural depth (in cm) measured with the Accuro, versus the standard ultrasound. There was a significant difference between the mean depth of epidural space s measured by Accuro or Standard US and needle insertion depth (P Conclusions: The handheld ultrasound system with 3D spine navigation technology can automatically identify and measure the epidural depth with the same accuracy as the standard ultrasounds in obese pregnant women.

Recognition of the layout of colored yarns in yarn-dyed fabrics

The layout of colored yarns in yarn-dyed fabrics is a significant part of designing and production in the textile industry, which is still analyzed manually at present. Existing methods based on image processing have some limitations in accuracy and stability. Therefore, an automatic method is proposed to recognize the layout of colored yarns and some other basic fabric structure parameters: the fabric density and weave pattern. First, a large dataset with fabric structure parameters is constructed. The fabric images are captured by a wireless portable device. Then the yarns and floats are accurately located using a novel multi-task and multi-scale convolutional neural network. Finally, a density-based color clustering algorithm is proposed to recognize the layout of colored yarns. The results of extensive experiments show that the proposed method can automatically identify the basic structure parameters with high effectiveness and robustness.

A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern

The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry. Traditional methods based on image processing have some limitations on accuracy and stability. In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition. First, a new big fabric dataset is established by a portable wireless device. The dataset contains wide kinds of fabrics and detailed fabric structure parameters. Then, a novel multi-task and multi-scale convolutional neural network (MTMSnet) is proposed to predict the location maps of yarns and floats. By adopting the multi-task structure, the MTMSnet can better learn the related features between yarns and floats. Finally, the weave pattern and basic weave repeat are recognized by combining the yarn and float location maps. Extensive experimental results on various kinds of fabrics indicate that the proposed method achieves high accuracy and quality in weave pattern recognition.

A realized on-demand cross-layer connection strategy for wireless self-organization link based on WLAN

For massive data communication, the wireless network is often needed to support data transmission in complex environment. In this case, a portable, inexpensive and efficient wireless device is especially helpful. In order to realize this function, we propose a wireless self-organized connection strategy in this paper. We focus on a multihop wireless network with single relay at each hop. One of the challenges over this system is to set up the communication link optimally and automatically, which is also the main contribution of this paper. So we designed an On-Demand Cross-Layer Connection Strategy (ODCLCS) based on WLAN to achieve the data transmission between source and destination point. This strategy crosses application layer, transport layer, network layer and MAC layer. By using active scanning in MAC layer, proper nodes can be found and connected. The automatic routing method can get benefit from active scanning message, automatically allocate IP address for each node. At last, we develop a link maintenance function which works after the link is established. The link maintenance function using heartbeats communication mechanism to find link errors. Moreover, the link maintenance message is very short which can be neglected compared with the data stream. The ODCLCS is designed for Wireless Self-organized Link (WSOL) system. WSOL system aims to establish a stable and user-friendly communication link by using ODCLCS and other necessary programs. All programs are properly written and debugged repeatedly by us, which can work well on testbed devices. Performance evaluation using NS-3 simulator shows the characteristic of WSOL. The WSOL program for testbed is based on cfg80211 and mac80211 in Linux Kernel. And some API based on libnl library is invoked. The testing data provided verify the good performance of ODCLCS in reality.

A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device

The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient's home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.

Pacifier Biosensor: Toward Noninvasive Saliva Biomarker Monitoring.

Wearable sensors for noninvasive monitoring of physiological parameters is a growing technology in the clinical field. Especially in neonates, the development of portable and nonharmful monitoring devices is urgently needed because they cannot provide any feedback about discomfort or health complaints. However, in infant monitoring, only wearable sensors measuring physical parameters for vital signs have been developed. Here, we describe the first chemical wearable sensor for newborn monitoring. This fully integrated pacifier operates as a portable wireless device toward noninvasive chemical monitoring in the infant's saliva. The infant's mouth movements on the pacifier result in efficient saliva pumping and promote unidirectional flow from the mouth to the electrochemical chamber. The integrated electrochemical detection chamber, containing the enzymatic biosensor, is located outside of the oral cavity. The capabilities of the platform were studied for glucose detection in diabetic adults and compared to their blood levels with good correlation, demonstrating the sensor's good performance. This baby-friendly device integrates saliva sampling with electrochemical sensing, along with miniaturized wireless electronics on a single pacifier platform. Such integration simplifies the infant's health monitoring in a real-time and selective fashion, representing the first wearable sensor focusing on chemical saliva sensing in newborns. This initial demonstration of glucose monitoring introduces new possibilities for metabolites monitoring in infants and neonates using saliva as a noninvasive sample.

Optimal Wireless Charging Inclusive of Intellectual Routing Based on SARSA Learning in Renewable Wireless Sensor Networks

The next generation’s sensor nodes will be more intelligent, energy conservative, and perpetual lifetime in the set-up of wireless sensor networks (WSNs). These sensors nodes are facing the overwhelming challenge of energy consumption which gradually decreases the lifetime of overall network. The wireless power transfer (WPT) is one of the most emerging technologies of energy harvesting that deploys at the heart of sensor nodes for efficient lifetime solution. A wireless portable charging device (WPCD) is drifting inside the WSN to recharge all the nodes which are questing for the eternal life. In this paper, we aspire to optimize a multi-objective function for charging trail of WPCD, and self-learning algorithm for data routing jointly. We formulated that the objective functions can optimize the fair energy consumption as well as maximize the routing efficiency of WPCD. The fundamental challenge of the problem is, to integrate the novel path for WPCD by applying the Nodal A* algorithm. We proposed a novel method of sensor node’s training for intellectual data transmission by using of clustering and reinforcement learning (SARSA) defined as clustering SARSA (C-SARSA) along with an optimal solution of objective functions. The whole mechanism outperforms in terms of trade-off between energy consumption and stability (fair energy consumption among all nodes) of the WSN; moreover, it prolongs the lifetime of the WSN. The simulated results demonstrate that our proposed method did better than compared literature in terms of energy consumption, stability, and lifetime of the WSN.

Portable brain-computer interface based on novel convolutional neural network

Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear uncomfortable EEG hats. These procedures usually create a challenge for subjects. In the present study, we propose a portable EEG signal acquisition system. This study consists of two main parts: 1) A novel, portable dry-electrode and wireless brain–computer interface is designed. The EEG signal acquisition board is based on 24 bit, analog-to-digital converters chip and wireless microprocessor unit. The wireless portable brain computer interface device acquires an EEG signal comfortably, and the EEG signals are transmitted to a personal computer via Bluetooth. 2) A convolutional neural network (CNN) classification algorithm is implemented to classify the motor imagery (MI) experiment using novel feature 3-dimension input. The time dimension was reshaped to represent the first and second dimension, and the frequency band was used as the third dimension. Specifically, frequency domain representations of EEG signals obtained via wavelet package decomposition (WPD) are obtained to train CNN. The classification performance in terms of the value of kappa is 0.564 for the proposed method. The t-test results show that the performance improvement of CNN over other selected state-of-the-art methods is statistically significant. Our results show that the proposed design is reliable in measuring EEG signals, and the 3D CNN provides better classification performance than other method for MI experiments.

A model of pain behaviors in freely moving rats generated by controllable electrical stimulation of the peripheral nerve

BackgroundNeuropathic pain patients have described experiencing unprovoked, intermittent pain attacks with shooting, stabbing, and burning qualities. Rodent models used in previous literature usually only involve acute exposure, and/or are unable to manipulate the stimulation intensity in vivo by the experimenter during an experiment. New methodThis paper describes a method to induce controllable pain behaviors in rodents using a wireless portable electronic device that can be manipulated within the course of an experiment. A stimulating electrode was implanted at the L5 spinal nerve location in Sprague-Dawley rats and our custom-built wireless stimulating device was attached to deliver variable stimulation in freely moving animals (50 Hz, 0.5 V; 100 Hz, 1 V). ResultsImplantation itself did not induce hypersensitivity as measured by the mechanical paw withdrawal threshold test. Observation of pain behaviors (paw elevation and licking) indicated that high stimulation intensity yielded a significant increase in pain behaviors. Even further, high intensity stimulation resulted in a behavioral “wind-up” of pain behaviors that persisted into the resting period when no stimulation was applied. Comparison with existing methods and conclusionsThis method can be used to study pain behaviors in a controllable way in freely moving rodents in comparison to existing models that are acute and/or are unable to manipulate the stimulation intensity in vivo.

A portable wireless device based on oximetry for sleep apnea detection

Obstructive sleep apnea is a highly prevalent sleep related breathing disorder and polysomnography is the gold standard exam for diagnosis. Despite providing results with high accuracy this multi-parametric test is expensive, time consuming and does not fit with the new tendency in health care that is changing the focus to prevention and wellness. Home health care is seen as a possible way to address this problematic by using minimal invasive devices, providing low cost of diagnosis and higher accessibility. To address this, a portable and automated sleep apnea detector was designed and evaluated. The device uses one SpO2 sensor and the analysis is based on the connection between oxygen saturation and apnea events. The measured signals are received in a field-programmable gate array that checks for errors and implements the communication protocols of two wireless transmitters. Two solutions were implemented for processing the data: one based on a smartphone (due to availability and low cost) and another based on a personal computer (for a higher computation capability). The algorithms were implemented in Java, for the smartphone, and in Python, for the computer. Both implementations have a graphical user interface to simplify the device operation. The algorithms were tested using a database consisting of 70 patients with the SpO2 signal collected in a Hospital. The algorithm performance achieved an average accuracy, sensitivity and specificity of 87.5, 79.5 and 90.8% respectively.

Compact Uniplanar Multi Feed Multi Band ACS Monopole Antenna Loaded With Multiple Radiating Branches for Portable Wireless Devices

This research article presents, a compact 0.19 λ x 0.32 λ size ACS fed printed monopole wideband antenna loaded with multiple radiating branches suitable for LTE2300/WiBro, 5 GHz WLAN and WiMAX applications. The proposed triple band uniplanar antenna encompasses of C shaped strip, L shaped strip, rectangular shaped strip and a lateral ground plane. All the radiating strips and ground plane are etched on the 26 × 15 m size low cost FR4 epoxy substrate. This designed geometry evoked three independent reonances at 2.3 GHz, 3.5 GHz and 5.5 GHz with precise impedance matching over each operating band. The reflection coefficient ( ) response of the presented antenna demonstrates three distinct resonant modes associated with -10 dB bandwidths are about 2.24-2.40 GHz, 3.38-3.83 GHz and 5.0-6.25 GHz respectively. From the study, it is also observed that the proposed design works perfect with microstrip as well as CPW feedings. Hence the designed Multi Feed Multi Band (MFMB) antenna can be easily deployed in to any portable wireless device that works for 2.3/3.5/ 5 GHz frequency bands.