Machine Learning Enabled Intelligent Self-Powered Sensors for Next-Generation Electronic Devices

The quantum leap in artificial intelligence from the last decade makes it pervasive in almost all the domains of our daily life activities from economic activities to planning smart cities and from healthcare monitoring to smart agriculture. Machine learning is a way to provide intelligence to systems with the help of specialized algorithms. The integration of the machine learning algorithms with the devices/systems makes them independent, autonomously working which of course helps to reduce a lot of economic and human resources spending to manually carry out the work. Furthermore, triboelectric nanogenerators are one of the premier choices for fabricating self-powered sensors, referring to their remarkable features like a wide choice of materials, easy fabrication techniques, and the nonrequirement of any energy storage unit to operate. Here, we have proposed an effective strategy to integrate the machine learning algorithms with the triboelectric nanogenerators for fabricating the self-powered intelligent sensors, further employed for numerous next-generation applications such as early detection of diseases, predicting the properties of materials, and detecting abnormalities in the regular activities of the human body to a name of few.