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.

Despite advantages and promising features of ionic polymer-metal composite (IPMC), this smart material has some drawbacks that should be eliminated as much as possible for improving the IPMC performance. One of the most important drawbacks is the back relaxation (BR). When a step input voltage is applied to the IPMC, it bends toward the anode. The BR is the unwanted and slow bending of the IPMC back toward the cathode. Several techniques based on using a feedback control loop have been proposed for eliminating the BR effect. However, these techniques are effective only when the bending displacement of the IPMC is small. In this paper, we propose a nonfeedback method for restraining the IPMC BR effect even in large bending displacement. The proposed technique is based on the fast reciprocating motions of free water molecules in Nafion membrane between the anode and the cathode. For this purpose, we have used a local Gaussian disturbance in a specifically patterned IPMC. We have described our idea conceptually and we have validated it by several experimental results.

Modeling and identification of dynamic behavior of ionic polymer metal composites as a widely used smart material is an inevitable work. In this article, unlike other conventional methods that modeled the tip displacement or uniform deformation of the ionic polymer metal composite, we have modeled the nonuniform deformation of the ionic polymer metal composite actuator. Final target of this method is estimation and prediction of a multi-criteria dynamic mathematical model. This dynamic mathematical model has been defined in such a way that all geometrical characteristics, shape, and curvature of it are consistent with the ionic polymer metal composite characteristics in all the time. This model can be useful to extract and predict many geometrical and even physical characteristics of the ionic polymer metal composite such as torque due to the ionic polymer metal composite weight, curvature, and bending angle. Experimental results demonstrate that the proposed method is capable to identify and predict the ionic polymer metal composite nonuniform deformation and curvature trajectory accurately even in a large deformation situation.

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.

Ionic polymer–metal composites are an emerging kind of electroactive polymer actuators, which can bend in response to a relatively low driving voltage. However, to enhance the actuation performance of ionic polymer–metal composites, some of their drawbacks should be considered. One of the most important drawbacks is “back relaxation.” The so-called back relaxation effect means, when a step input voltage is applied to the ionic polymer–metal composite, the conventional bending displacement toward the anode is followed by an unwanted and slow back relaxation toward the cathode. Control-based methods for restraining the ionic polymer–metal composite back relaxation effect are feedback-based schemes which apply significant constraints to dominant applications of ionic polymer–metal composite actuators especially in biomedical applications. In this article, we present an entirely scientific-based mathematical modeling to achieve a practical method for restraining the back relaxation effect in Nafion-based ionic polymer–metal composites, relying on creating a specific pattern on Pt layers of the ionic polymer–metal composites and applying a local Gaussian disturbance to this patterned ionic polymer–metal composites.