A Hybrid Microstructure Piezoresistive Sensor with Machine Learning Approach for Gesture Recognition

触觉传感器 人工智能 计算机科学 压阻效应 机器人 灵活性(工程) 接口(物质) 机器学习 模式识别(心理学) 工程类 电气工程 数学 统计 最大气泡压力法 气泡 并行计算
作者
Yousef Al-Handarish,Olatunji Mumini Omisore,Jing Chen,Xiuqi Cao,Toluwanimi Oluwadara Akinyemi,Yan Yan,Lei Wang
出处
期刊:Applied sciences [Multidisciplinary Digital Publishing Institute]
卷期号:11 (16): 7264-7264 被引量:6
标识
DOI:10.3390/app11167264
摘要

Developments in flexible electronics have adopted various approaches which have enhanced the applicability of human–machine interface fields. Recently, microstructural integration and hybrid functional materials were designed for realizing human somatosensory. Nonetheless, designing tactile sensors with smart structures using facile and low-cost fabrication processes remains challenging. Furthermore, using the sensors for recognizing stimuli and feedback applications remains poorly validated. In this study, a highly flexible piezoresistive tactile sensor was developed by homogeneously dispersing carbon black (CB) in a microstructure porous sugar/PDMS-based sponge. Owning to its high flexibility and softness, the sensor can be mounted on human or robotic systems for different clinical applications. We validated the applicability of the proposed sensor by applying it to recognizing grasp and release forces in an open setting and to classifying hand motions that surgeons apply on the master interface of a robotic system during intravascular catheterization. For this purpose, we implemented the long short-term memory (LSTM)-dense classification model and five traditional machine learning methods, namely, support vector machine, multilayer perceptron, decision tree, and k-nearest neighbor. The models were used to classify the different hand gestures obtained in an open-setting experiment. Amongst all, the LSTM-dense method yielded the highest overall recognition accuracy (87.38%). Nevertheless, the performance of the other models was in a similar range, showing that our sensor structure can be applied in intelligence sensing or tactile feedback systems. Secondly, the sensor prototype was applied to analyze the motions made while manipulating an interventional robot. We analyzed the displacement and velocity of the master interface during typical axial (push/pull) and radial operations with the robot. The results obtained show that the sensor is capable of recording unique patterns during different operations. Thus, a combination of the flexible wearable sensors and machine learning could yield a future generation of flexible materials and artificial intelligence of things (AIoT) devices.
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