Deep Learning Enabled Early Predicting Cardiovascular Status Using Highly Sensitive Piezoelectric Sensor of Solution‐Processable Nylon‐11

Abstract

Cardiovascular diseases are found as one of the major cause of deaths globally, these can be reduced substantially if early‐stage detection and intervention is possible. Regular monitoring of the arterial pulse is one of the possible solutions, however, existing technologies have put limitations, due instability in continuous monitoring, lack of information in real‐time recording of cardiovascular parameters and bulky instruments. A highly sensitive flexible piezoelectric sensor of nylon‐11 fabricated is introduced from simple solution processable technique. Which consists of a highly sensitive, flexible, conformable piezoelectric film, owing to its high mechanosensitivity (≈225 mV N−1) in the subtle pressure range (0.001–1 kPa), and fast responsivity (≈4 ms), it is tested for assessing risk factors of cardiovascular diseases based on arterial pulse data. It is integrated with the internet of things (IoT) via system on a chip to facilitate remote healthcare monitoring. Deep learning algorithms is further interfaced with sensor for early detect and predict cardiovascular risks, showing an accuracy of >94% for predicting cardiovascular status. This piezoelectric sensor equipped with artificial intelligence and IoT has potential for monitoring the risk analysis of the cardiovascular diseases, daily activities, and facilitate to early predict the anomalous physiological changes in the body.

Deep Learning Enabled Perceptive Wearable Sensor: An Interactive Gadget for Tracking Movement Disorder

“…The time of delay between the systolic and diastolic peak (Δ t ), RI , and AI r are found at 0.375 s, 4.72 m s −1 , and 0.52, respectively, which indicates the human health of a healthy person. [ 42 ]…”

Effect of the Fiber Diameter of Polyamide 11 Nanofibers on Their Internal Molecular Orientation and Properties

“…The obtained d 33 values were similar to those of PA11 films cast from a trifluoroacetic acid/acetone mixture and a formic acid/dichloromethane mixture (≈2 and 0.8 pm V -1 , respectively). [23,24]…”

Directional freezing-induced self-poled piezoelectric nylon 11 aerogels as high-performance mechanical energy harvesters

“…Piezoelectric materials capable of harvesting energy from mechanical vibrations have gained wide interest as a potential power solution, in light of the growing demand for wireless, wearable, lightweight and portable electronic devices. [1][2][3][4] Piezoelectric materials with a unique polar axis can undergo a transient deformation under mechanical stress, which induces an ordered dipole distribution. This develops a net dipole moment that can directly transduce mechanical energy into electrical energy.…”

“…In this regard, the easy processability, exibility, biocompatibility, and chemical robustness of piezoelectric polymers render them excellent candidates to be integrated into small and lightweight devices for energy harvesting applications. [10][11][12][13][14] Though polymers such as polyvinylidene uoride (PVDF) and their copolymers such as polyvinylidene uoride triuoroethylene (PVDF-TrFE) and poly(vinylidene uoride-co-hexauoropropene) (PVDF-HFP), 15,16 odd nylons, 1,2,[17][18][19][20] poly (lactic acid), 4 cellulose, 21,22 chitosan, 23 polyhydroxybutyrate, 24 etc., are known to exhibit the piezoelectric effect, most of the earlier studies were focussed on PVDF and their copolymers, as they exhibit superior piezoelectric performance. However, uorine-based systems are not environment-friendly and their low Curie and/or melting temperature limit their high-temperature applications.…”