TY - GEN
T1 - Palpation Robot System - Reproduction Method by Deep Neural Network of Skin Palpation Judgment Focusing on Softness Classification
AU - Kato, Fumihiro
AU - Adachi, Takeya
AU - Handa, Takumi
AU - Kamishima, Kaito
AU - Iwata, Hiroyasu
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JST [Moonshot R&D][Grant Number JPMJMS2031], AMED [Practical Research Project for Allergic Diseases and Immunology][Grant Number 22677379], JSPS Kakenhi [Grant Number JP22K16268][Grant Number JP22K18220], and the Scientific Research Fund of the Ministry of Health, Labour and Welfare, Japan [21FE2001]. Our special gratitude goes to Dr. Susumu Tachi from the University of Tokyo for his contribution to haptic sensing, as well as Drs. Toyoko Inazumi, Hiroki Arakawa, Yuka Shintani, and Yuki Kobayashi from the KKR Tachikawa Hospital, Drs. Shuhei Nishimoto, Sayuka Arakawa, and Akihiro Miyagawa from the Kawasaki Municipal Hospital, Drs. Yuichi Kurihara, Ryo Tanaka, Tomohiro Suzuki, and Takayoshi Sakuta from the Hiratsuka City Hospital, Drs. Emiko Watanabe-Okada, Ryohei Asakura, and Chiaki Takahashi from the Saiseikai Yokohamashi Tobu Hospital for their valuable contribution to the palpation analysis. This work was also supported by Waseda University Global Robot Academia Institute and Waseda University Green Computing Systems Research Organization.
Funding Information:
This work was supported by JST [Moonshot R&D][Grant Number JPMJMS2031], AMED [Practical Research Project for Allergic Diseases and Immunology][Grant Number 22677379], JSPS Kakenhi [Grant Number JP22K16268][Grant Number JP22K18220], and the Scientific Research Fund of the Ministry of Health, Labour and Welfare, Japan [21FE2001]. Our special gratitude goes to Dr. Susumu Tachi from the University of Tokyo for his contribution to haptic sensing, as well as Drs. Toyoko Inazumi, Hiroki Arakawa, Yuka Shintani, and Yuki Kobayashi from the KKR Tachikawa Hospital, Drs. Shuhei Nishimoto, Sayuka Arakawa, and Akihiro Miyagawa from the Kawasaki Municipal Hospital, Drs. Yuichi Kurihara, Ryo Tanaka, Tomohiro Suzuki, and Takayoshi Sakuta from the Hiratsuka City Hospital, Drs. Emiko Watanabe-Okada, Ryohei Asakura, and Chiaki Takahashi from the Saiseikai Yokohamashi Tobu Hospital for their valuable contribution to the palpation analysis. This work was also supported by Waseda University Global Robot Academia Institute and Waseda University Green Computing Systems Research Organization.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In recent years, the spread of infectious diseases, such as COVID-19, has increased the need for medical examinations to avoid contact between doctors and patients. Most treatments, especially dermatology, require palpation, and its impact is significant. In this study, we aimed to reproduce the judgment of the softness and surface textures of diseased parts, which is important to dermatologists for determining the condition, using a simple robot device. Five levels of softness and three types of surface textures labeled with 14 types of materials were obtained from interviews with dermatologists. To acquire a haptic response from materials during pushing, 1) a single-rod probe with a haptic sensor using a linear actuator and 2) a dual-rod type configuration to obtain vibration propagation was constructed. Frequency-analyzed images were produced from the obtained waveforms of force and acceleration. A total of 343 images from 13 materials were used for transfer learning and were classified using AlexNet. The classification accuracy of the single-rod probe was 93.0%, and that of the dual-probe configuration was 95.2%. The classification accuracy was improved using the dual probe configuration than the single one; the softness classification accuracy was improved from 93.8% (single-rod) to 95.7% (dual-rod configuration). The surface texture classification accuracy was improved from 91.9% (single-rod) to 92.8% (dual-rod configuration), respectively. Therefore, the proposed method enables the reproduction of the judgment of five-level softness and three types of surface texture judgment by dermatologists.
AB - In recent years, the spread of infectious diseases, such as COVID-19, has increased the need for medical examinations to avoid contact between doctors and patients. Most treatments, especially dermatology, require palpation, and its impact is significant. In this study, we aimed to reproduce the judgment of the softness and surface textures of diseased parts, which is important to dermatologists for determining the condition, using a simple robot device. Five levels of softness and three types of surface textures labeled with 14 types of materials were obtained from interviews with dermatologists. To acquire a haptic response from materials during pushing, 1) a single-rod probe with a haptic sensor using a linear actuator and 2) a dual-rod type configuration to obtain vibration propagation was constructed. Frequency-analyzed images were produced from the obtained waveforms of force and acceleration. A total of 343 images from 13 materials were used for transfer learning and were classified using AlexNet. The classification accuracy of the single-rod probe was 93.0%, and that of the dual-probe configuration was 95.2%. The classification accuracy was improved using the dual probe configuration than the single one; the softness classification accuracy was improved from 93.8% (single-rod) to 95.7% (dual-rod configuration). The surface texture classification accuracy was improved from 91.9% (single-rod) to 92.8% (dual-rod configuration), respectively. Therefore, the proposed method enables the reproduction of the judgment of five-level softness and three types of surface texture judgment by dermatologists.
KW - dermatology
KW - machine learning
KW - palpation
KW - robotic medical system
KW - telexistence
UR - http://www.scopus.com/inward/record.url?scp=85143789372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143789372&partnerID=8YFLogxK
U2 - 10.1109/ISMCR56534.2022.9950593
DO - 10.1109/ISMCR56534.2022.9950593
M3 - Conference contribution
AN - SCOPUS:85143789372
T3 - Proceedings - International Symposium on Measurement and Control in Robotics: Robotics and Virtual Tools for a New Era, ISMCR 2022
BT - Proceedings - International Symposium on Measurement and Control in Robotics
A2 - Taqvi, Zafar
A2 - Fuchter, Simone Keller
A2 - Filho, Geraldo Gurgel
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th International Symposium on Measurement and Control in Robotics, ISMCR 2022
Y2 - 28 September 2022 through 30 September 2022
ER -
