Long-term Monitoring Applications Research Articles

An Adaptive Energy Efficient MAC Protocol for RF Energy Harvesting WBANs

Continuous and remote health monitoring medical applications with heterogeneous requirements can be realized through wireless body area networks (WBANs). Energy harvesting is adopted to enable low-power health applications and long-term monitoring without battery replacement, which have drawn significant interest recently. Because energy harvesting WBANs are obviously different from battery-powered ones, network protocols should be designed accordingly to improve network performance. In this article, an efficient cross-layer media access control protocol is proposed for radio frequency powered energy harvesting WBANs. We redesigned the superframe structure, which can be rescheduled by the coordinator dynamically. A time switching (TS) strategy is used when sensors harvest energy from radio frequency signals broadcast by the coordinator, and a transmission power adjustment scheme is proposed for sensors based on the energy harvesting efficiency and the network environment. Energy efficiency can be effectively improved that more packets can be uploaded using limited energy. The length of the energy harvesting period is determined by the coordinator to balance the channel resources and energy requirements of sensors and further improve the network performance. Numerical simulation results show that our protocol can provide superior system performance for long-term periodic health monitoring applications.

Adhesive free, conformable and washable carbon nanotube fabric electrodes for biosensing

AbstractSkin-mounted wearable electronics are attractive for continuous health monitoring and human-machine interfacing. The commonly used pre-gelled rigid and bulky electrodes cause discomfort and are unsuitable for continuous long-term monitoring applications. Here, we design carbon nanotubes (CNTs)-based electrodes that can be fabricated using different textile manufacturing processes. We propose woven and braided electrode design using CNTs wrapped textile yarns which are highly conformable to skin and measure a high-fidelity electrocardiography (ECG) signal. The skin-electrode impedance analysis revealed size-dependent behavior. To demonstrate outstanding wearability, we designed a seamless knit electrode that can be worn as a bracelet. The designed CNT-based dry electrodes demonstrated record high signal-to-noise ratios and were very stable against motion artifacts. The durability test of the electrodes exhibited robustness to laundering and practicality for reusable and sustainable applications.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals

BackgroundSleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment. MethodIn this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM). ResultThe performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857. ConclusionsThe proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.

Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network.

Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and body. Flexible probes, which can deform and effectively adhere to the body, are a promising technology for long-term monitoring applications. However, owing to the flexible element array geometry, the reconstructed image becomes blurred and distorted. In this study, we propose a flexible probe U.S. imaging method based on element array geometry estimation from radio frequency (RF) data using a deep neural network (DNN). The input and output of the DNN are the RF data and parameters that determine the element array geometry, respectively. The DNN was first trained from scratch with simulation data and then fine-tuned with in vivo data. The DNN performance was evaluated according to the element position mean absolute error (MAE) and the reconstructed image quality. The reconstructed image quality was evaluated with peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM). In the test conducted with simulation data, the average element position MAE was 0.86 mm, and the average reconstructed image PSNR and MSSIM were 20.6 and 0.791, respectively. In the test conducted with in vivo data, the average element position MAE was 1.11 mm, and the average reconstructed image PSNR and MSSIM were 19.4 and 0.798, respectively. The average estimation time was 0.045 s. These results demonstrate the feasibility of the proposed method for long-term real-time monitoring using flexible probes.

Application of long-term passive acoustic monitoring to evaluate spatial and temporal patterns of multi-species acoustic assemblages of marine mammals in the western North Atlantic Ocean

Passive acoustic monitoring (PAM) is an efficient method for large-scale marine mammal monitoring. PAM technologies concurrently sample multiple soniferous marine mammal species, and when coupled with verified detectors, provide information that can be used to evaluate community composition. This analysis used data collected by an array of high-frequency PAM recorders at eight sites between 30°0’N and 42°0’N along the shelf-break in the western North Atlantic Ocean. Daily acoustic presence of 13 known marine mammal species and grouped unknown odontocetes was determined for all months between June 2016 and May 2019. Classification trees were generated from monthly summaries of daily detections to identify community composition dissimilarities. Detections of Gervais’s beaked whale (Mesoplodon europaeus) represented the root node and split sites by latitude, grouping the three sites south of 34°0’N and the five sites north of 38°0’N. Tree nodes were further divided by other odontocetes. The presence of mysticetes also varied by site and season. Distinctive communities were identified for each site, with odontocetes being resident and mysticetes more migratory. The root and secondary splits were all driven by beaked whale species, demonstrating the importance of identifying these whales to species instead of aggregating them as is common for visual survey data.

The Biological Memory Effect in Microbial Fuel Cell Biosensors

Microbial fuel cells (MFCs) are electrochemical fuel cells that directly convert the chemical energy of organic compounds in biomass into electrical energy. Due to their self-sustainability, direct current output, and fast response, MFC biosensors have the potential for long-term environmental monitoring applications. For the first time, we report a biological memory effect (BME) in MFC biosensors during repeated toxin injections. The toxin response of the biosensors generally weakens over repeated toxin stimuli injection at low concentrations. Experimental results demonstrate that the current drop of the second and third toxin injection is only 48.88% and 28.13%, respectively, of the first toxin injection on average. To investigate this BME, an ordinary differential equation (ODE) model is established. By fitting ODE model parameters to the experimental results, the model successfully simulates the experiments and the BME. This ODE model has good potential to compensate for the BME with its predictive ability, and it may potentially correct inaccuracies that accrue during long-term environmental monitoring for MFC biosensors. The current research paves the way for implementing MFC biosensors for long-term environmental toxic chemical detection.

Stretchable Sponge Electrodes for Long-Term and Motion-Artifact-Tolerant Recording of High-Quality Electrophysiologic Signals.

Soft electronic devices and sensors have shown great potential for wearable and ambulatory electrophysiologic signal monitoring applications due to their light weight, ability to conform to human skin, and improved wearing comfort, and they may replace the conventional rigid electrodes and bulky recording devices widely used nowadays in clinical settings. Herein, we report an elastomeric sponge electrode that offers greatly reduced electrode–skin contact impedance, an improved signal-to-noise ratio (SNR), and is ideally suited for long-term and motion-artifact-tolerant recording of high-quality biopotential signals. The sponge electrode utilizes a porous polydimethylsiloxane sponge made from a sacrificial template of sugar cubes, and it is subsequently coated with a poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) conductive polymer using a simple dip-coating process. The sponge electrode contains numerous micropores that greatly increase the skin–electrode contact area and help lower the contact impedance by a factor of 5.25 or 6.7 compared to planar PEDOT:PSS electrodes or gold-standard Ag/AgCl electrodes, respectively. The lowering of contact impedance resulted in high-quality electrocardiogram (ECG) and electromyogram (EMG) recordings with improved SNR. Furthermore, the porous structure also allows the sponge electrode to hold significantly more conductive gel compared to conventional planar electrodes, thereby allowing them to be used for long recording sessions with minimal signal degradation. The conductive gel absorbed into the micropores also serves as a buffer layer to help mitigate motion artifacts, which is crucial for recording on ambulatory patients. Lastly, to demonstrate its feasibility and potential for clinical usage, we have shown that the sponge electrode can be used to monitor uterine contraction activities from a patient in labor. With its low-cost fabrication, softness, and ability to record high SNR biopotential signals, the sponge electrode is a promising platform for long-term wearable health monitoring applications.

Investigation of graphene-coated Ag/AgCl electrode performance in surface electromyography measurement

Conventional silver-silver chloride (Ag/AgCl) electrodes are widely used for recording surface electromyography (sEMG) with a conductive gel. However, for long-term sEMG recording, the gel has some disadvantages that cause high impedance. Therefore, the dry electrodes have been alternatively purposed to overcome these disadvantages. Recently, the nanomaterial-based dry electrodes have been developed for long term electrophysiological signal recording. In the present study, we aimed to develop a graphene-coated Ag/AgCl electrode for long-term recording. We transferred single layer graphene (SLG) on the Ag/AgCl electrode surface by using chemical vapor deposition and confirmed this process by Raman scattering spectroscopy and scanning electron microscopy. We then compared the graphene-coated Ag/AgCl and conventional Ag/AgCl electrodes by evaluating median motor nerve conduction studies (mNCS) and their impedance. The charge transfer resistance (Rct) for the Ag/AgCl electrode (4170 Ω) was much higher than graphene-coated Ag/AgCl electrode (Rct = 24.6 Ω). For median mNCS measurements without gel, the graphene-coated Ag/AgCl electrode provided a better amplitude of distal and proximal compound muscle action potential (28.3 mV and 25.8 mV, respectively) than the Ag/AgCl electrode (21.8 mV and 20.9 mV, respectively). Consequently, the present study suggests promising results in terms of the usability of graphene-coated Ag/AgCl electrodes for long-term monitoring and wearable systems applications of sEMG. In future studies, we aim to investigate clinical applicability of graphene-coated sEMG electrodes that include extended clinical settings and larger study population.

(Invited) Carbon Nanotube Thin-Film Devices for Fully-Flexible Electronics

Since flexible electronic devices can come in close contact with soft human tissue, they are suitable as wearable sensor applications for long-term and non-invasive monitoring of biological signals in a wide range of fields such as medical/health care, sports, and so on. Previous flexible sensor systems require signal processing with either a remote terminal or rigid IC chips placed on the sensor device due to the lack of high-performance flexible analog-frontend circuit (AFE). The remote system may suffer from an incorporation of external noise. The use of rigid IC chips degrades the flexibility and durability of the wearable sensor. To realize a fully flexible sensor system, an AFE including amplifiers, filters, analog-digital converters is needed to be integrated as well as sensors monolithically on a flexible substrate. In this study, we have realized fully-flexible AFE with carbon nanotubes. The key circuit is a novel operational amplifier (op-amp) with a high gain and low operating voltage. The AFE can be operated at low-voltage and exhibits excellent stability and robustness against variability.

Structural displacement estimation by fusing vision camera and accelerometer using hybrid computer vision algorithm and adaptive multi-rate Kalman filter

Structural displacement monitoring is essential because displacement can provide critical information regarding the health condition of civil structures. However, the precise estimation of structural displacement remains a challenge. This paper describes a displacement estimation technique that fuses asynchronous acceleration and vision measurements at different sampling rates. A hybrid computer vision (CV) algorithm and an adaptive multi-rate Kalman filter are integrated to efficiently estimate high-sampling displacement from low-sampling vision measurement and high-sampling acceleration measurement. An initial calibration algorithm is proposed to automatically determine active pixels and two scale factors required in the hybrid CV algorithm without any prior knowledge or ad-hoc thresholding. The proposed technique was experimentally validated and high-sampling displacements were accurately estimated in real-time with less than 1.5 mm error, indicating the potential of the proposed technique for practical applications in long-term continuous structural displacement monitoring.

AutoLoc: Autonomous Sensor Location Configuration via Cross Modal Sensing.

Internet-of-Things (IoT) systems have become pervasive for smart homes. In recent years, many of these IoT sensing systems are developed to enable in-home long-term monitoring applications, such as personalized services in smart homes, elderly/patient monitoring, etc. However, these systems often require complicated and expensive installation processes, which are some of the main concerns affecting users' adoption of smart home systems. In this work, we focus on floor vibration-based occupant monitoring systems, which enables non-intrusive in-home continuous occupant monitoring, such as patient step tracking and gait analysis. However, to enable these applications, the system would require known locations of vibration sensors placed in the environment. Current practice relies on manually input of location, which makes the installation labor-intensive, time consuming, and expensive. On the other hand, without known location of vibration sensors, the output of the system does not have intuitive physical meaning and is incomprehensive to users, which limits the systems' usability. We present AutoLoc, a scheme to estimate the location of the vibration sensors in a two-dimensional space in the view of a nearby camera, which has spatial physical meaning. AutoLoc utilizes occupants' walking events captured by both vibration sensors and the co-located camera to estimate the vibration sensors' location in the camera view. First, AutoLoc detects and localizes the occupant's footsteps in the vision data. Then, it associates the time and location of the event to the floor vibration data. Next, the extracted vibration data of the given event from multiple vibration sensors are used to estimate the sensors' locations in the camera view coordinates. We conducted real-world experiments and achieved up to 0.07 meters localization accuracy.

Next-Generation Wearable Wireless EEG Recorder: The future of accessible neural applications, from mental health monitoring to a noninvasive brain–computer interface

EEG recording has been widely used in a variety of applications, including sleep studies, mental health monitoring, brain–computer interfaces (BCIs), etc. However, mainstream clinical recording systems are still tethered with wet electrodes that require special preparation, limiting the setup to well-equipped labs and preventing the collection of longitudinal data. In this article, we summarize the special characteristics of EEG signals and review key circuit design techniques in EEG signal acquisition and data telemetry. With careful system optimization, a wireless distributed recording setup is possible for nonintrusive EEG data collection, which will enable long-term mental health monitoring and other important applications.

Beluga whale (Delphinapterus leucas) acoustic foraging behavior and applications for long term monitoring.

Cook Inlet, Alaska, is home to an endangered and declining population of 279 belugas (Delphinapterus leucas). Recovery efforts highlight a paucity of basic ecological knowledge, impeding the correct assessment of threats and the development of recovery actions. In particular, information on diet and foraging habitat is very limited for this population. Passive acoustic monitoring has proven to be an efficient approach to monitor beluga distribution and seasonal occurrence. Identifying acoustic foraging behavior could help address the current gap in information on diet and foraging habitat. To address this conservation challenge, eight belugas from a comparative, healthy population in Bristol Bay, Alaska, were instrumented with a multi-sensor tag (DTAG), a satellite tag, and a stomach temperature transmitter in August 2014 and May 2016. DTAG deployments provided 129.6 hours of data including foraging and social behavioral states. A total of 68 echolocation click trains ending in terminal buzzes were identified during successful prey chasing and capture, as well as during social interactions. Of these, 37 click trains were successfully processed to measure inter-click intervals (ICI) and ICI trend in their buzzing section. Terminal buzzes with short ICI (minimum ICI <8.98 ms) and consistently decreasing ICI trend (ICI increment range <1.49 ms) were exclusively associated with feeding behavior. This dual metric was applied to acoustic data from one acoustic mooring within the Cook Inlet beluga critical habitat as an example of the application of detecting feeding in long-term passive acoustic monitoring data. This approach allowed description of the relationship between beluga presence, feeding occurrence, and the timing of spawning runs by different species of anadromous fish. Results reflected a clear preference for the Susitna River delta during eulachon (Thaleichthys pacificus), Chinook (Oncorhynchus tshawytscha), pink (Oncorhynchus gorbuscha), and coho (Oncorhynchus kisutch) salmon spawning run periods, with increased feeding occurrence at the peak of the Chinook and pink salmon runs.

Clustering and Feature Analysis of Smartphone Data for Depression Monitoring.

Modern advancements have allowed society to be at the most innovative stages of technology which involves the possibility of multimodal data collection. Dartmouth dataset is a rich dataset collected over 10 weeks from 60 participants. The dataset includes different types of data but this paper focuses on 10 different smartphone sensor data and a Patient Health Questionnaire (PHQ) 9 survey that monitors the severity of depression. This paper extracts key features from smartphone data to identify depression. A multi-view bi-clustering (MVBC) algorithm is applied to categorize homogeneous behaviour subgroups. MVBC takes multiple views of sensing data as input. The algorithm inputs three views: average, trend, and location views. MVBC categorizes the subjects to low, medium and high PHQ-9 scores. Real-world data collection may have fewer sensors, allowing for less features to be extracted. This creates a focus on prioritization of features. In this body of work, minimum redundancy maximum relevance (mRMR) is applied to the sensing features to prioritize the features that better distinguish the different groups. The resulting MVBC are compared to literature to support the categorized clusters. Decision Tree (DT) 10-fold cross validation shows that our method can classify individuals into the correct subgroups using a reduced number of features to achieve an overall accuracy of 94.7±1.62%. Achieving high accuracies with reduced features allows for focus on low power analysis and edge computing applications for long-term mental health monitoring using a smartphone.

Long-Term Performance of Distributed Optical Fiber Sensors Embedded in Reinforced Concrete Beams under Sustained Deflection and Cyclic Loading.

This paper explores the performance of distributed optical fiber sensors based on Rayleigh backscattering for the monitoring of strains in reinforced concrete elements subjected to different types of long-term external loading. In particular, the reliability and accuracy of robust fiber optic cables with an inner steel tube and an external protective polymeric cladding were investigated through a series of laboratory experiments involving large-scale reinforced concrete beams subjected to either sustained deflection or cyclic loading for 96 days. The unmatched spatial resolution of the strain measurements provided by the sensors allows for a level of detail that leads to new insights in the understanding of the structural behavior of reinforced concrete specimens. Moreover, the accuracy and stability of the sensors enabled the monitoring of subtle strain variations, both in the short-term due to changes of the external load and in the long-term due to time-dependent effects such as creep. Moreover, a comparison with Digital Image Correlation measurements revealed that the strain measurements and the calculation of deflection and crack widths derived thereof remain accurate over time. Therefore, the study concluded that this type of fiber optic has great potential to be used in real long-term monitoring applications in reinforced concrete structures.

A Multichannel Strain Measurement Technique for Nanomodified Smart Cement-Based Sensors in Reinforced Concrete Structures.

Nanomodified smart cement-based sensors are an emerging self-sensing technology for the structural health monitoring (SHM) of reinforced concrete (RC) structures. To date, several literature works demonstrated their strain-sensing capabilities, which make them suited for damage detection and localization. Despite the most recent technological improvements, a tailored measurement technique allowing feasible field implementations of smart cement-based sensors to concrete structures is still missing. In this regard, this paper proposes a multichannel measurement technique for retrieving strains from smart cement-based sensors embedded in RC structures using a distributed biphasic input. The experiments performed for its validation include the investigation on an RC beam with seven embedded sensors subjected to different types of static loading and a long-term monitoring application on an RC plate. Results demonstrate that the proposed technique is effective for retrieving time-stable simultaneous strain measurements from smart cement-based sensors, as well as for aiding the identification of the changes in their electrical outputs due to the influence of environmental effects variable over time. Accordingly, the proposed multichannel strain measurement technique represents a promising approach for performing feasible field implementations of smart cement-based sensors to concrete structures.

An Anti-Fatigue Design Strategy for 3D Ribbon-Shaped Flexible Electronics.

Three-dimensional (3D) flexible electronics represent an emerging area of intensive attention in recent years, owing to their broad-ranging applications in wearable electronics, flexible robots, tissue/cell scaffolds, among others. The widely adopted 3D conductive mesostructures in the functional device systems would inevitably undergo repetitive out-of-plane compressions during practical operations, and thus, anti-fatigue design strategies are of great significance to improve the reliability of 3D flexible electronics. Previous studies mainly focused on the fatigue failure behavior of planar ribbon-shaped geometries, while anti-fatigue design strategies and predictive failure criteria addressing 3D ribbon-shaped mesostructures are still lacking. This work demonstrates an anti-fatigue strategy to significantly prolong the fatigue life of 3D ribbon-shaped flexible electronics by switching the metal-dominated failure to desired polymer-dominated failure. Combined in situ measurements and computational studies allow the establishment of a failure criterion capable of accurately predicting fatigue lives under out-of-plane compressions, thereby providing useful guidelines for the design of anti-fatigue mesostructures with diverse 3D geometries. Two mechanically reliable 3D devices, including a resistance-type vibration sensor and a janus sensor capable of decoupled temperature measurements, serve as two demonstrative examples to highlight potential applications in long-term health monitoring and human-like robotic perception, respectively.

Nanomaterials-patterned flexible electrodes for wearable health monitoring: a review.

Electrodes fabricated on a flexible substrate are a revolutionary development in wearable health monitoring due to their lightweight, breathability, comfort, and flexibility to conform to the curvilinear body shape. Different metallic thin-film and plastic-based substrates lack comfort for long-term monitoring applications. However, the insulating nature of different polymer, fiber, and textile substrates requires the deposition of conductive materials to render interactive functionality to substrates. Besides, the high porosity and flexibility of fiber and textile substrates pose a great challenge for the homogenous deposition of active materials. Printing is an excellent process to produce a flexible conductive textile electrode for wearable health monitoring applications due to its low cost and scalability. This article critically reviews the current state of the art of different textile architectures as a substrate for the deposition of conductive nanomaterials. Furthermore, recent progress in various printing processes of nanomaterials, challenges of printing nanomaterials on textiles, and their health monitoring applications are described systematically.Graphical

A 4-μW Analog Front End Achieving 2.4 NEF for Long-Term ECG Monitoring.

An ultra-low-power low-noise analog front end (AFE) is presented in this work, aiming for long-term ECG with clear P-waves for clinical diagnose. The chopper amplifier with passive noise filter and PWM based offset cancellation results in an input-reference noise of 0.39 μVrms, which shows 3.7X noise improvements among the state-of-the-art designs. With the digital offset cancellation by the pulse-width modulation wave, the AFE achieves a low input-referred dc offset of 0.4 μV among 9 tested chips and a low drift under 30 μV. A dynamic scale ADC with low-power comparator strategy prevents the instability and signal-loss, achieving an SFDR of 71.6 dB. The proposed AFE achieves a noise-efficient-factor (NEF) of 2.4 with a power consumption of 4 μW. The fabricated chip is demonstrated in a miniature prototype for long-term ECG monitoring application, presenting a clear ECG waveform with visible P-wave. The simultaneously ECG recording with a medical grade 12-lead ECG Holter shows the effective acquisition of the prototype, proofing the better noise performance.

An Inkjet-Printed PEDOT:PSS-Based Stretchable Conductor for Wearable Health Monitoring Device Applications.

A stretchable conductor is one of the key components in soft electronics that allows the seamless integration of electronic devices and sensors on elastic substrates. Its unique advantages of mechanical flexibility and stretchability have enabled a variety of wearable bioelectronic devices that can conformably adapt to curved skin surfaces for long-term health monitoring applications. Here, we report a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)-based stretchable polymer blend that can be patterned using an inkjet printing process while exhibiting low sheet resistance and accommodating large mechanical deformations. We have systematically studied the effect of various types of polar solvent additives that can help induce phase separation of PEDOT and PSS grains and change the conformation of a PEDOT chain, thereby improving the electrical property of the film by facilitating charge hopping along the percolating PEDOT network. The optimal ink formulation is achieved by adding 5 wt % ethylene glycol into a pristine PEDOT:PSS aqueous solution, which results in a sheet resistance of as low as 58 Ω/□. Elasticity can also be achieved by blending the above solution with the soft polymer poly(ethylene oxide) (PEO). Thin films of PEDOT:PSS/PEO polymer blends patterned by inkjet printing exhibits a low sheet resistance of 84 Ω/□ and can resist up to 50% tensile strain with minimal changes in electrical performance. With its good conductivity and elasticity, we have further demonstrated the use of the polymer blend as stretchable interconnects and stretchable dry electrodes on a thin polydimethylsiloxane (PDMS) substrate for photoplethysmography (PPG) and electrocardiography (ECG) recording applications. This work shows the potential of using a printed stretchable conducting polymer in low-cost wearable sensor patches for smart health applications.