Core-spun carbon nanotube yarn supercapacitors for wearable electronic textiles

Linear (fiber or yarn) supercapacitors have demonstrated remarkable cyclic electrochemical performance as power source for wearable electronic textiles. The challenges are, first, to scale up the linear supercapacitors to a length that is suitable for textile manufacturing while their electrochemical performance is maintained or preferably further improved and, second, to develop practical, continuous production technology for these linear supercapacitors. Here, we present a core/sheath structured carbon nanotube yarn architecture and a method for one-step continuous spinning of the core/sheath yarn that can be made into long linear supercapacitors. In the core/sheath structured yarn, the carbon nanotubes form a thin surface layer around a highly conductive metal filament core, which serves as current collector so that charges produced on the active materials along the length of the supercapacitor are transported efficiently, resulting in significant improvement in electrochemical performance and scale up of the supercapacitor length. The long, strong, and flexible threadlike supercapacitor is suitable for production of large-size fabrics for wearable electronic applications.

Electrospun granular hollow SnO2 nanofibers hydrogen gas sensors operating at low temperatures

In this paper, we present H2 gas sensors based on hollow and filled, well-aligned electrospun SnO2 nanofibers, operating at a low temperature of 150 C. SnO2 nanofibers with diameters ranging from 80 to 400 nm have been successfully synthesized in which the diameter of the nanofibers can be controlled by adjusting the concentration of polyacrylonitrile in the solution for electrospinning. The presence of this polymer results in the formation of granular walls for the nanofibers. We discussed the correlation between nanofibers morphology, structure, oxygen vacancy contents and the gas sensing performances. X-ray photoelectron spectroscopy analysis revealed that the granular hollow SnO2 nanofibers, which show the highest responses, contain a significant number of oxygen vacancies, which are favorable for gas sensor operating at low temperatures. © 2014 American Chemical Society.

High-performance multifunctional graphene yarns: toward wearable all-carbon energy storage textiles

The successful commercialization of smart wearable garments is hindered by the lack of fully integrated carbon-based energy storage devices into smart wearables. Since electrodes are the active components that determine the performance of energy storage systems, it is important to rationally design and engineer hierarchical architectures atboth the nano- and macroscale that can enjoy all of the necessary requirements for a perfect electrode. Here we demonstrate a large-scale flexible fabrication of highly porous high-performance multifunctional graphene oxide (GO) and rGO fibers and yarns by taking advantage of the intrinsic soft self-assembly behavior of ultralarge graphene oxide liquid crystalline dispersions. The produced yarns, which are the only practical form of these architectures for real-life device applications, were found to be mechanically robust (Young's modulus in excess of 29 GPa) and exhibited high native electrical conductivity (2508 ± 632 S m(-1)) and exceptionally high specific surface area (2605 m(2) g(-1) before reduction and 2210 m(2) g(-1) after reduction). Furthermore, the highly porous nature of these architectures enabled us to translate the superior electrochemical properties of individual graphene sheets into practical everyday use devices with complex geometrical architectures. The as-prepared final architectures exhibited an open network structure with a continuous ion transport network, resulting in unrivaled charge storage capacity (409 F g(-1) at 1 A g(-1)) and rate capability (56 F g(-1) at 100 A g(-1)) while maintaining their strong flexible nature.

Placing sensors for area coverage in a complex environment by a team of robots

Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an "entrance" to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40-50% of the ROI, whereas BTD covers it in full. BTD involves significantly (80%) less robot moves and messages than LRV.

Individualized arrhythmia detection with ECG signals from wearable devices

Low cost pervasive electrocardiogram (ECG) monitors is changing how sinus arrhythmia are diagnosed among patients with mild symptoms. With the large amount of data generated from long-term monitoring, come new data science and analytical challenges. Although traditional rule-based detection algorithms still work on relatively short clinical quality ECG, they are not optimal for pervasive signals collected from wearable devices - they don't adapt to individual difference and assume accurate identification of ECG fiducial points. To overcome these short-comings of the rule-based methods, this paper introduces an arrhythmia detection approach for low quality pervasive ECG signals. To achieve the robustness needed, two techniques were applied. First, a set of ECG features with minimal reliance on fiducial point identification were selected. Next, the features were normalized using robust statistics to factors out baseline individual differences and clinically irrelevant temporal drift that is common in pervasive ECG. The proposed method was evaluated using pervasive ECG signals we collected, in combination with clinician validated ECG signals from Physiobank. Empirical evaluation confirms accuracy improvements of the proposed approach over the traditional clinical rules.

Graphene and graphitic derivative filled polymer composites as potential sensors

Graphite and numerous graphitic-derived micro- and nano-particles have gained importance in current materials science research. These two-dimensional sheets of sp(2)-hybridized carbon atoms remarkably influence the properties of polymers. Graphene mono-layers, graphene oxides, graphite oxides, exfoliated graphite, and other related materials are derived from a parental graphite structure. In this review, we focus primarily on the role of these fillers in regulating the electrical and sensing properties of polymer composites. It has been demonstrated that the addition of an optimized mixture of graphene and or its derivatives to various polymers produces a record-high enhancement of the electrical conductivity and achieved semiconducting characteristics at small filler loading, making it suitable for sensor manufacture. Promising sensing characteristics are observed in graphite-derived composite films compared with those of micro-sized composites and the properties are explained mainly based on the filler volume fraction, nature and rate of dispersion and the filler polymer interactions at the interface. In short, this critical review aims to provide a thorough understanding of the recent advances in the area of graphitic-based polymer composites in advanced electronics. Future perspectives in this rapidly developing field are also discussed.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.

Knitted strain sensor textiles of highly conductive all-polymeric fibers

A scaled-up fiber wet-spinning production of electrically conductive and highly stretchable PU/PEDOT:PSS fibers is demonstrated for the first time. The PU/PEDOT:PSS fibers possess the mechanical properties appropriate for knitting various textile structures. The knitted textiles exhibit strain sensing properties that were dependent upon the number of PU/PEDOT:PSS fibers used in knitting. The knitted textiles show sensitivity (as measured by the gauge factor) that increases with the number of PU/PEDOT:PSS fibers deployed. A highly stable sensor response was observed when four PU/PEDOT:PSS fibers were co-knitted with a commercial Spandex yarn. The knitted textile sensor can distinguish different magnitudes of applied strain with cyclically repeatable sensor responses at applied strains of up to 160%. When used in conjunction with a commercial wireless transmitter, the knitted textile responded well to the magnitude of bending deformations, demonstrating potential for remote strain sensing applications. The feasibility of an all-polymeric knitted textile wearable strain sensor was demonstrated in a knee sleeve prototype with application in personal training and rehabilitation following injury.

Predictive data mining for converged internet of things: a mobile health perspective

Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks

Validity of wearable technology to measure peak impact during high-intensity treadmill running

The purpose of this study was to identify the validity of an upper-body mounted accelerometer to measure peak acceleration during high-intensity treadmill running. A twelve camera motion analysis (MA) system was used as the criterion measure with markers placed on and close to the accelerometer. Ten peak impacts per participant were compared (n = 390). All accelerometer values were significantly different between the MA unit and T6 reflective marker’s acceleration data. Smoothing accelerometer data at 8 and 6 Hz provides an acceptable indirect measure of peak impact acceleration performed during high-intensity running. Consequently, smoothing algorithms should be incorporated into the commercially available software that the devices are supplied with.

Design and fabrication of a capacitance based wearable pressure sensor using e-textiles

This paper addresses the methods used for the design and fabrication of a capacitance based wearable pressure sensor fabricated using neoprene and (SAC) plated Nylon Fabric. The experimental set up for the pressure sensor is comprised of a shielded grid of sensing modules, a 555 timer based transduction circuitry, and an Arduino board measuring the frequency of signal to a corresponding pressure. The fundamental design parameters addressed during the development of the pressure sensor presented in this paper are based on size, simplicity, cost, adaptability, and scalability. The design approach adopted in this paper results in a sensor module that is less obtrusive, has a thinner and flexible profile, and its sensitivity is easily scalable for ‘smart’ product applications across industries associated to sports performance, ergonomics, rehabilitation, etc.