Abstract
For personal and daily activities, it is highly desirable to collect energy from multiple sources, not only for charging personal electronics but also for charging devices that may in the future sense and transmit information for healthcare and biomedical applications. In particular, hybridization of triboelectric and piezoelectric energy-harvesting generators with lightweight components and relatively simple structures have shown promise in self-powered sensors. Here, we present a self-powered multifunctional sensor (SPMS) based on hybridization with a novel design of a piezoelectrically curved spacer that functions concurrently with a zigzag shaped triboelectric harvester for a human biomechanical monitoring device. The optimized SPMS had an open-circuit voltage (VOC) of 103 V, short-circuit current (ISC) of 302 µA, load of 100 kΩ, and maximum average power output of 38 mW under the operational processes of compression/deformation/touch/release. To maximize the new sensor’s usage as a gait sensor that can detect and monitor human motion characteristics in rehabilitation circumstances, the deep learning long short-term memory (LSTM) model was developed with an accuracy of the personal sequence gait SPMS signal recognition of 81.8%.
Highlights
The rapid development of modern society and the increasingly accelerated pace of life can affect human health
The signal sent by the self-powered multifunctional sensor (SPMS) device performs deep learning through long short-term memory (LSTM) and recognizes the relationship between different signals for actions to achieve the monitoring functions, such as stroke phase recognition [34], noise-robust automatic speech recognition(ASR) [35], and information retrieval [36]
We showed that this microstructure can improve the overall performance of the Triboelectric nanogenerators (TENGs) and increase the output
Summary
The rapid development of modern society and the increasingly accelerated pace of life can affect human health. Triboelectric nanogenerators (TENGs) have recently been developed and have unique advantages, namely, they have high output, are economical and light, and have a durable structure [11,12], which means they can harvest all kinds of extremely small quantities mechanical energy (in the range of nW–mW). LSTM is an alternative artificial recurrent neural network (RNN) architecture for identifying sequential data, and it is used in the field of deep learning [32] It has a feedback connection, unlike standard feedforward neural networks. The signal sent by the self-powered multifunctional sensor (SPMS) device performs deep learning through LSTM and recognizes the relationship between different signals for actions to achieve the monitoring functions, such as stroke phase recognition [34], noise-robust automatic speech recognition(ASR) [35], and information retrieval [36]
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