Development of artificial haptic model for human tactile sense using machine learning

Shimoe, Hikaru; Matsumura, Kohei; Noma, Hiroyasu; Sohgawa, Masayuki; Okuyama, Masanori

doi:10.1109/icsens.2017.8234173

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…These Supervised Learning Algorithms (SLAs) require the trained dataset to be labeled, whereas, unSupervised Learning Algorithms (un-SLAs) do not require parameter tuning or data labeling [22] . Chan-Maestas and Sofge [23] performed tactile perception based on K-Means Clustering (KMC); and Shimoe et al [24] developed artificial haptic models with Neural Network (NN) technology.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based multi-modal information perception for soft robotic hands

Huang

Lin

et al. 2020

Tinshhua Sci. Technol.

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This paper focuses on multi-modal Information Perception (IP) for Soft Robotic Hands (SRHs) using Machine Learning (ML) algorithms. A flexible Optical Fiber-based Curvature Sensor (OFCS) is fabricated, consisting of a Light-Emitting Diode (LED), photosensitive detector, and optical fiber. Bending the roughened optical fiber generates lower light intensity, which reflecting the curvature of the soft finger. Together with the curvature and pressure information, multi-modal IP is performed to improve the recognition accuracy. Recognitions of gesture, object shape, size, and weight are implemented with multiple ML approaches, including the Supervised Learning Algorithms (SLAs) of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and the unSupervised Learning Algorithm (un-SLA) of K-Means Clustering (KMC). Moreover, Optical Sensor Information (OSI), Pressure Sensor Information (PSI), and Double-Sensor Information (DSI) are adopted to compare the recognition accuracies. The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective. The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations. Moreover, DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.

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Section: Introductionmentioning

confidence: 99%