Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review.

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer's physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual's physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

A new 3D, microfluidic-oriented, multi-functional, and highly stretchable soft wearable sensor.

Increasing demand for wearable devices has resulted in the development of soft sensors; however, an excellent soft sensor for measuring stretch, twist, and pressure simultaneously has not been proposed yet. This paper presents a novel, fully 3D, microfluidic-oriented, gel-based, and highly stretchable resistive soft sensor. The proposed sensor is multi-functional and could be used to measure stretch, twist, and pressure, which is the potential of using a fully 3D structure in the sensor. Unlike previous methods, in which almost all of them used EGaIn as the conductive material, in this case, we used a low-cost, safe (biocompatible), and ubiquitous conductive gel instead. To show the functionality of the proposed sensor, FEM simulations and a set of designed experiments were done, which show linear (99%), accurate (> 94.9%), and durable (tested for a whole of four hours) response of the proposed sensor. Then, the sensor was put through its paces on a female test subject's knee, elbow, and wrist to show the potential application of the sensor as a body motion sensor. Also, a fully 3D active foot insole was developed, fabricated, and evaluated to evaluate the pressure functionality of the sensor. The result shows good discrimination and pressure measurement for different foot sole areas. The proposed sensor has the potential to be used in real-world applications like rehabilitation, wearable devices, soft robotics, smart clothing, gait analysis, AR/VR, etc.

A 3D analytical ion transport model for ionic polymer metal composite actuators in large bending deformations.

Ionic polymer metal composites (IPMCs) are a kind of soft electroactive polymer composites. An IPMC strip commonly has a thin polymer membrane coated with a noble metal as electrodes on both sides. Whenever an electric voltage is applied to the IPMC, it bends and whenever it is deformed, a low voltage is measurable between its electrodes, hence IPMC is an actuator as well as a sensor. They are well known for their promising features like low density, lightness, high toughness and remarkable stimulus strain, also, they have the potential for low-voltage operation while exhibiting acceptable large bending deformation. In this paper, a three-dimensional (3D), dynamic and physics-based model is presented analytically and experimentally for IPMC actuators. The model combines the ion transport dynamics within the IPMC and the bending dynamics of it as a beam under an electrical stimulation. In particular, we present an analytical model to create a relation between the input voltage and the output tip displacement of an IPMC actuator for large bending deformations. Experimental results show that the proposed model captures well the tip displacement.

How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning.

Nowadays, artificial intelligence (AI) and machine learning (ML) are playing a tremendous role in all aspects of human life and they have the remarkable potential to solve many problems that classic sciences are unable to solve appropriately. Neuroscience and especially psychiatry is one of the most important fields that can use the potential of AI and ML. This study aims to develop an ML-based model to detect the relationship between resiliency and hope with the stress of COVID-19 by mediating the role of spiritual well-being. An online survey is conducted to assess the psychological responses of Iranian people during the Covid-19 outbreak in the period between March 15 and May 20, 2020, in Iran. The Iranian public was encouraged to take part in an online survey promoted by Internet ads, e-mails, forums, social networks, and short message service (SMS) programs. As a whole, 755 people participated in this study. Sociodemographic characteristics of the participants, The Resilience Scale, The Adult Hope Scale, Paloutzian & Ellison's Spiritual Wellbeing Scale, and Stress of Covid-19 Scale were used to gather data. The findings showed that spiritual well-being itself cannot predict stress of Covid-19 alone, and in fact, someone who has high spiritual well-being does not necessarily have a small amount of stress, and this variable, along with hope and resiliency, can be a good predictor of stress. Our extensive research indicated that traditional analytical and statistical methods are unable to correctly predict related Covid-19 outbreak factors, especially stress when benchmarked with our proposed ML-based model which can accurately capture the nonlinear relationships between the collected data variables.

A novel, low cost, and accessible method for rapid fabrication of the modifiable microfluidic devices.

As microfluidic chips are evolving to become a significant analysis tool toward POCT devices, it is crucial to make the cost and the time required for the fabrication process of these chips as low as possible. Because of the multidisciplinary nature of these systems and the collaboration of many different laboratories and organizations from vastly various fields with unequal types of equipment, it is essential to develop new techniques and materials to make the integration of disparate systems together more straightforward, accessible, and economical. In this paper, we present ethylene-vinyl acetate (EVA) as a new polymer-based material for the fabrication of different microfluidic chips, which brings new features and tools in fabrication, integration, and functionality of microfluidic systems. We put this material next to PDMS for comparison between various aspects of these materials. We have shown that besides the low-cost ability, ubiquitousness, geometrical modifiability, and ease of fabrication of EVA chips, due the lower hydrophobicity and lower terahertz (THz) absorption of EVA than PDMS, EVA chips, in comparison to PDMS counterparts, can work faster, have less number of channel blocking and can be used in THz biosensing application like metamaterial-based cancer detection. Finally, several devices are made using EVA to demonstrate the functionality and versatility of this material for the fabrication of microfluidic chips.