Smart textiles for personalized healthcare

Research Interests Security and privacy: Active authentication, biometrics template protection, biometrics recognition. Computer vision: Domain adaptation, dictionary learning, object recognition, activity recognition, shape representation. Machine learning: Dimensionality reduction, clustering, kernel methods, weakly-supervised learning. Signal/Image processing: Sparse representation, compressive sampling, synthetic aperture radar imaging, millimeter wave imaging.

Sparse and redundant representations for inverse problems and recognition

There is nothing more personal than healthcare. Health care must move from its current reactive and disease-centric system to a personalized, predictive, preventative and participatory model with a focus on disease prevention and health promotion. As the world marches into the era of Internet of Things (IoT) and 5G wireless, technology renovation enables industry to offer a more individually tailored approach to healthcare with more successful health outcomes, higher quality and lower costs. However, empowering the utility of IoT enabled technology in personalized health care is still significantly challenged by the shortage of cost-effective and wearable biomedical devices to continuously provide real-time, patient-generated health data. Textiles have been concomitant and playing a vital role in the long history of human civilization. In this talk, I will introduce our current research on smart textiles for biomedical monitoring and personalized diagnosis, textile for therapy, and textile power generation as an energy solution for the future wearable medical devices.

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Smart Textiles for Personalized Health Care

MXenes have received increasing attention due to their two-dimensional layered structure, high conductivity, hydrophilicity, and large specific surface area. Because of these distinctive advantages, MXenes are considered as very competitive pressure-sensitive materials in applications of flexible piezoresistive sensors. This work reviews the preparation methods, basic properties, and assembly methods of MXenes and their recent developments in piezoresistive sensor applications. The recent developments of MXene-based flexible piezoresistive sensors can be categorized into one-dimensional fibrous, two-dimensional planar, and three-dimensional sensors according to their various structures. The trends of multifunctional integration of MXene-based pressure sensors are also summarized. Finally, we end this review by describing the opportunities and challenges for MXene-based pressure sensors and the great prospects of MXenes in the field of pressure sensor applications.

Ti3C2Tx MXene-Based Flexible Piezoresistive Physical Sensors.

Flexible sensors have received tremendous attention over the last several decades due to their outstanding properties, such as high stretchability, excellent biocompatibility, great conformability, and low cost. The main fatal aspect of flexible sensors is that the flexible material has poor conductivity, resulting in obtained flexible sensors possessing poor sensitivity. Many kinds of nanomaterials with excellent conductivity have been integrated with flexible materials to improve the conductivity of flexible sensors, such as carbon nanomaterials and metal nanomaterials. In terms of molding, 3D printing serves as an ideal technology for producing flexible sensors because it can generate previously unattainable geometric structures compared with traditional fabrications. Understanding the features of flexible materials, nanomaterials, and 3D printing technology, and assessing the possibility of their fabrication are therefore critical for the development and application of flexible sensors. Herein, we review the recent literature on flexible materials and nanomaterials, and the fabrication strategy of flexible sensors, with a particular focus on stretchable and self-healing properties. Then, we discuss the application of flexible sensors, such as biomedical devices, wearable devices, and soft robots. Finally, we provide key challenges and opportunities that lie ahead for flexible sensors.

Emerging flexible sensors based on nanomaterials: recent status and applications

Wearable sensor technologies, especially continuous monitoring of various human health conditions, are attracting increased attention. However, current rigid sensors present obvious drawbacks, like lower durability and poor comfort. Here, a strategy is proposed to efficiently yield wearable sensors using cotton fabric as an essential component, and conductive materials conformally coat onto the cotton fibers, leading to a highly electrically conductive interconnecting network. To improve the conductivity and durability of conductive coatings, a topographical modification approach is developed with genus-3 and genus-5 structures, and topological genus structures enable cage metallic seeds on the surface of substrates. A textile-based capacitive sensor with flexible, comfortable, and durable properties has been demonstrated. High sensitivity and convenience of signal collection have been achieved by the excellent electrical conductivity of this sensor. Based on results of deep investigation on capacitance, effects of distance and angles between two conductive fabrics contribute to the capacitive sensitivity. In addition, the textile-based capacitive sensor has successfully been used for real-time monitoring human breathing, speaking, blinking, and joint motions during physical rehabilitation exercises.

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Textile-Based Capacitive Sensor for Physical Rehabilitation via Surface Topological Modification.

In this paper, an electromagnetic sensor which can operate simultaneously in capacitive and inductive modalities with sensitivities to permittivity, conductivity, and permeability is developed, and a novel measurement strategy is proposed accordingly. The sensor is composed of two planar spiral coils with a track width of 4 mm, which promotes its capacitive mode. The capacitive coupling is measured in common mode, while the inductive coupling is measured in differential mode. In capacitive mode, the sensor is sensitive to changes in permittivity, i.e., the dielectric material distribution; while in inductive mode, it is sensitive to magnetically permeable material and electrically conductive material. Furthermore, it is demonstrated that the sensor can simultaneously measure dielectric and conductive materials. This novel sensing element has been designed and implemented. Experimental results verified its effectiveness in dual modality measurement.

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A Novel Dual Modality Sensor With Sensitivities to Permittivity, Conductivity, and Permeability

In eddy current testing, lift-off variations between sensor and sample plate affect sensor inductance and consequently inferred thickness. In this paper, a novel combined inductive and capacitive sensor is designed in which the output of the capacitive sensor can be used to infer the lift-off. An algorithm is proposed to combine the inferred lift-off and the inductance measurement for predicting thickness. The effect of lift-off variations is significantly reduced using this combined sensor approach. Compared to the multi-frequency thickness measurements in our previous work, this only requires a single frequency measurement for each of the capacitive and inductive modalities, leading to a relaxation of the instrument and measurement requirements.

Accurate measurements of plate thickness with variable lift-off using a combined inductive and capacitive sensor

In eddy current testing, the trajectory of the impedance data due to a defect is presented as a Lissajous curve (LC) in the complex plane. This paper proposes a novel analytical model for describing a LC. Further, a new feature extraction method is implemented which automatically computes four geometric features (amplitude, width, angle and symmetry) from Lissajous figures. In addition, six machine learning-based classifiers are used for automatic defect identification based on these features. High detection rates are achieved for both the simulated and experimental data, which demonstrates the flexibility of the analytical model and the validity of the methodology.

A novel feature extraction method of eddy current testing for defect detection based on machine learning

Weld inspection is significant in manufacturing to improve productivity and ensure safety. During the welding process, steel microstructures experience complex transformations depending on welding conditions such as heat input, welding speed, component size and temperature, etc. Examining weld microstructures can reveal valuable information on its metallurgical, mechanical and electromagnetic properties. Electromagnetic (EM) testing is of great practical interest to characterise the weld microstructures in a non-destructive and expedient manner. In this paper, an experimental scanning method using a cup-ferrite enclosed T-R sensor has been devised to image the EM properties of a cross-section of an X70 steel submerged arc welding (SAW) specimen. These images show good correlation with the hardness testing and metallurgical information of the specimen. An approximate linear relationship was found between the EM signal and the hardness of the SAW of X70 steel weld. The scanning method can serve as a complementary tool for hardness testing without the need for sophisticated surface preparation.

Jorge R. Salas Avila

Papers

Textile-Based Capacitive Sensor for Physical Rehabilitation via Surface Topological Modification.

A Novel Dual Modality Sensor With Sensitivities to Permittivity, Conductivity, and Permeability

Accurate measurements of plate thickness with variable lift-off using a combined inductive and capacitive sensor

A novel feature extraction method of eddy current testing for defect detection based on machine learning

Imaging x70 weld cross-section using electromagnetic testing