A Hybrid Microstructure Piezoresistive Sensor with Machine Learning Approach for Gesture Recognition
| Autor: | Olatunji Mumini Omisore, Xiuqi Cao, Lei Wang, Toluwanimi Oluwadara Akinyemi, Jing Chen, Yousef Al-Handarish, Yan Yan |
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| Rok vydání: | 2021 |
| Předmět: |
sensor applications
Technology QH301-705.5 Computer science QC1-999 Interface (computing) Wearable computer Machine learning computer.software_genre human–machine interface General Materials Science Biology (General) QD1-999 Instrumentation Fluid Flow and Transfer Processes low-cost electronics business.industry Physics Process Chemistry and Technology General Engineering surgical robotics Engineering (General). Civil engineering (General) Flexible electronics Computer Science Applications Support vector machine Chemistry machine learning Gesture recognition Multilayer perceptron Robot Artificial intelligence TA1-2040 tactile sensors business computer Tactile sensor |
| Zdroj: | Applied Sciences Volume 11 Issue 16 Applied Sciences, Vol 11, Iss 7264, p 7264 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11167264 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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