Abstract
Human motion state recognition technology based on flexible, wearable sensor devices has been widely applied in the fields of human–computer interaction and health monitoring. In this study, a new type of flexible capacitive pressure sensor is designed and applied to the recognition of human motion state. The electrode layers use multi-walled carbon nanotubes (MWCNTs) as conductive materials, and polydimethylsiloxane (PDMS) with microstructures is embedded in the surface as a flexible substrate. A composite film of barium titanate (BaTiO3) with a high dielectric constant and low dielectric loss and PDMS is used as the intermediate dielectric layer. The sensor has the advantages of high sensitivity (2.39 kPa−1), wide pressure range (0–120 kPa), low pressure resolution (6.8 Pa), fast response time (16 ms), fast recovery time (8 ms), lower hysteresis, and stability. The human body motion state recognition system is designed based on a multi-layer back propagation neural network, which can collect, process, and recognize the sensor signals of different motion states (sitting, standing, walking, and running). The results indicate that the overall recognition rate of the system for the human motion state reaches 94%. This proves the feasibility of the human motion state recognition system based on the flexible wearable sensor. Furthermore, the system has high application potential in the field of wearable motion detection.
Highlights
Human motion state recognition technology has been widely applied in different fields such as medical treatment [1,2], human–computer interaction [3,4], and home life [5,6]
Posts and Telecommunications proposed an architecture of time-varying long short-term memory recurrent neural networks (TV-LSTMs) for human action recognition, which adopted convolutional neural networks (CNNs) as the feature generator, and obtained good recognition results
The multi-walled carbon nanotubes (MWCNTs) were dispersed in anhydrous ethanol (AET) and stirred manually for 10 min
Summary
Human motion state recognition technology has been widely applied in different fields such as medical treatment [1,2], human–computer interaction [3,4], and home life [5,6]. Bilen et al [8] of the University of Edinburgh combined motion pictures and dynamic optical flow with CNNs and summarized the video as an image, which can realize end-to-end video action recognition This vision-based recognition mode has the advantages of strong real-time performance, simple data acquisition, and good continuity, most of the studies focus on algorithm design with a long system development cycle. The capacitive flexible pressure sensor composed of this dielectric layer had a sensitivity of 0.55 kPa−1 in a pressure range of 0–0.2 kPa. The pressure detection limit was as low as 3 Pa, and the response time was less than 1 s. The sensor can effectively monitor human physiological activity information, such as breathing signals and the state of joint motion, and can significantly contribute to the field of wearable motion detection
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