Object classification system using temperature variation of smart finger device via machine learning

Heon Ick Park; Tae Jin Cho; In Geol Choi; Min Suk Rhee; Youngsu Cha

doi:10.1016/j.sna.2023.114338

Object classification system using temperature variation of smart finger device via machine learning

Heon Ick Park
, Tae Jin Cho
, In Geol Choi
, Min Suk Rhee
, Youngsu Cha^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

In this study, we proposed smart finger devices (SFDs) for an object classification system using unimodal temperature sensors. Each SFD comprised a module with a flexible thermoelectric device (TED) and a resistance temperature detector (RTD) sensor embedded in a silicone finger cot mounted on a robot gripper. The stored Peltier heat on the TED of the SFD was transferred to the object when the robot gripper grasped it. The RTD sensor data obtained through a one-dimensional convolutional neural network (1D-CNN) distinguished materials with similar thermal conductivities. Through two preprocessing steps, the sensor data were fed into the designed classifier to identify ten selected objects. Finally, our configured classifier performed real-time recognition using unimodal temperature sensors.

Original language	English
Article number	114338
Journal	Sensors and Actuators, A: Physical
Volume	356
DOIs	https://doi.org/10.1016/j.sna.2023.114338
Publication status	Published - 2023 Jun 16

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation by the Korea government ( Ministry of Science and ICT , MSIT) (grant NRF-2022R1A4A3033775 ). The authors would like to thank Kyungmin Lee for him help with drawing schematic.

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

Machine learning
Object detection
Temperature sensing

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Instrumentation
Condensed Matter Physics
Surfaces, Coatings and Films
Metals and Alloys
Electrical and Electronic Engineering

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10.1016/j.sna.2023.114338

Cite this

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title = "Object classification system using temperature variation of smart finger device via machine learning",

abstract = "In this study, we proposed smart finger devices (SFDs) for an object classification system using unimodal temperature sensors. Each SFD comprised a module with a flexible thermoelectric device (TED) and a resistance temperature detector (RTD) sensor embedded in a silicone finger cot mounted on a robot gripper. The stored Peltier heat on the TED of the SFD was transferred to the object when the robot gripper grasped it. The RTD sensor data obtained through a one-dimensional convolutional neural network (1D-CNN) distinguished materials with similar thermal conductivities. Through two preprocessing steps, the sensor data were fed into the designed classifier to identify ten selected objects. Finally, our configured classifier performed real-time recognition using unimodal temperature sensors.",

keywords = "Machine learning, Object detection, Temperature sensing",

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doi = "10.1016/j.sna.2023.114338",

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AU - Park, Heon Ick

AU - Cho, Tae Jin

AU - Choi, In Geol

AU - Rhee, Min Suk

AU - Cha, Youngsu

N1 - Funding Information: This work was supported by the National Research Foundation by the Korea government ( Ministry of Science and ICT , MSIT) (grant NRF-2022R1A4A3033775 ). The authors would like to thank Kyungmin Lee for him help with drawing schematic. Publisher Copyright: © 2023 Elsevier B.V.

PY - 2023/6/16

Y1 - 2023/6/16

N2 - In this study, we proposed smart finger devices (SFDs) for an object classification system using unimodal temperature sensors. Each SFD comprised a module with a flexible thermoelectric device (TED) and a resistance temperature detector (RTD) sensor embedded in a silicone finger cot mounted on a robot gripper. The stored Peltier heat on the TED of the SFD was transferred to the object when the robot gripper grasped it. The RTD sensor data obtained through a one-dimensional convolutional neural network (1D-CNN) distinguished materials with similar thermal conductivities. Through two preprocessing steps, the sensor data were fed into the designed classifier to identify ten selected objects. Finally, our configured classifier performed real-time recognition using unimodal temperature sensors.

AB - In this study, we proposed smart finger devices (SFDs) for an object classification system using unimodal temperature sensors. Each SFD comprised a module with a flexible thermoelectric device (TED) and a resistance temperature detector (RTD) sensor embedded in a silicone finger cot mounted on a robot gripper. The stored Peltier heat on the TED of the SFD was transferred to the object when the robot gripper grasped it. The RTD sensor data obtained through a one-dimensional convolutional neural network (1D-CNN) distinguished materials with similar thermal conductivities. Through two preprocessing steps, the sensor data were fed into the designed classifier to identify ten selected objects. Finally, our configured classifier performed real-time recognition using unimodal temperature sensors.

KW - Machine learning

KW - Object detection

KW - Temperature sensing

UR - https://www.scopus.com/pages/publications/85151780168

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DO - 10.1016/j.sna.2023.114338

M3 - Article

AN - SCOPUS:85151780168

SN - 0924-4247

VL - 356

JO - Sensors and Actuators, A: Physical

JF - Sensors and Actuators, A: Physical

M1 - 114338

ER -

Object classification system using temperature variation of smart finger device via machine learning

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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