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.
Biosignals of diverse body posture contain a key information about the physiological, kinesiological, and anatomical status of human body that can facilitate in early assessing the neurological maladies such as Parkinson's disease, multiple sclerosis, and other neurological disorders. Early detection and timely intervention of specific diagnosis for curing the disorders is considered as one of the effective step of diagnosis. Existing technologies of capturing human movements have the limitations of low latencies, bulky counterpart, cost‐intensive, and power consuming. A highly sensitive (≈440 mV N−1), confirmable, and flexible wearable gadget is introduced for addressing such complexities; moreover, the gadget is fabricated by recycling waste material. Subsequently, it is interfaced with various deep/machine learning algorithms for classification/prediction of different hand poses; particularly unsupervised k‐means clustering is used for observing discrete classes, and different supervised algorithms such as k‐nearest neighbor, support vector machine, deep neural network (DNN), and pattern recognition that provide the high degree of prediction accuracy (up to ≈98%) for classification of different postures. Thus, artificial intelligence‐aided wearable gadget can not only offer an unique solution for autonomously tracking various body movements but also assist in reducing waste materials, which is otherwise a threat to the environment.
All-organic piezoelectric mechanical energy harvesters display an excellent electrical output with higher sensitivity due to the superior electrode compatibility between active materials and organic electrodes in comparison to that of metal electrodes. Herein, a stretchable, breathable, and flexible all-organic piezoelectric nanogenerator, made up of PVDF nanofibers and δ-PVDF nanoparticles, fabricated through the electrospinning process in a single step, has been demonstrated for prospective machine learning applications. The δphase PVDF nanoparticles serve as efficient active piezoelectric and ferroelectric components with a piezoelectric coefficient of ∼13 pm/V. In terms of electrical response, a peak-to-peak ∼V OC of 4 V, I SC of 1.8 μA, and maximum power density of ∼1600 μW/m 2 were obtained. The fabricated device also exhibits excellent stretchability and air permeability, enabling the properties of robust wearable devices with a water vapor transmission rate of ∼250 g m −2 day −1 . Here, we have shown that a machine learning algorithm proposed for the different finger motion responses can predict with 94.6% accuracy. Thus, it could recognize different finger gestures efficiently with the highest possible accuracy and predict the possible source point. This feature could be advantageous for prospective health care and security purposes apart from the device and sensor applications.
The current–voltage characteristic curves of a perovskite solar cell (PSC) show a hysteretic effect. The effect is quantified as a hysteresis index (HI), which is a measure of the degree of hysteresis. The higher the value of the HI, the higher is the hysteretic effect. HI is determined by varying the thickness of the perovskite layer while keeping the interfacial layer constant and vice versa. It is observed that the value of the HI increases from 0.10 to 0.57 with increasing the thickness of the perovskite layer from 100 to 400 nm and saturates beyond 400 nm. In contrast, the value of HI is observed to remain constant at 0.57 over the entire variation of interfacial thickness. The observations are correlated with the number of trap‐states and ionic effects, inherently present in the perovskite and interface layers. The saturation of hysteretic effects beyond 400 nm is proposed to be due to the effective saturation of the screening effect produced by the mobile ions. The work highlights the combined role of trap‐states and ionic effects in controlling the hysteresis and provides an insight to optimize the PSCs to minimize the hysteretic effect, leading to a reliable power conversion efficiency.
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