Electronic Skin to Feel "Pain": Detecting "Prick" and "Hot" Pain Sensations.

Seebeck effect ZnO nanowire artificial pain feeling electronic skin piezoelectric effect pyroelectric effect tactile sensor

Journal

Soft robotics
ISSN: 2169-5180
Titre abrégé: Soft Robot
Pays: United States
ID NLM: 101623819

Informations de publication

Date de publication:
12 2019
Historique:
pubmed: 25 7 2019
medline: 25 7 2019
entrez: 24 7 2019
Statut: ppublish

Résumé

An artificial tactile system has attracted tremendous interest and intensive study, since it can be applied as a new functional interface between humans and electronic devices. Unfortunately, most previous works focused on improving the sensitivity of sensors. However, humans also respond to psychological feelings for sensations such as pain, softness, or roughness, which are important factors for interacting with others and objects. Here, we present an electronic skin concept that generates a "pain" warning signal, specifically, to sharp "prick" and "hot" sensations. To simplify the sensor structure for these two feelings, a single-body tactile sensor design is proposed. By exploiting "hot" feeling based on the Seebeck effect instead of the pyroelectric property, it is possible to distinguish points registering a "hot" feeling from those generating a "prick" feeling, which is based on the piezoelectric effect. The control of free carrier concentration in nanowire induced the appropriate level of Seebeck current, which enabled the sensor system to be more reliable. The first derivatives of the piezo and Seebeck output signals are the key factors for the signal processing of the "pain" feeling. The main idea can be applied to mimic other psychological tactile feelings.

Identifiants

pubmed: 31335257
doi: 10.1089/soro.2018.0049
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

745-759

Auteurs

Minkyung Sim (M)

Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Kyung Hwa Lee (KH)

IMEP-LAHC, Grenoble Institute of Technology(Minatec), Grenoble, France.

Kwon Sik Shin (KS)

Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Jeong Hee Shin (JH)

Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Ji-Woong Choi (JW)

Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Hongsoo Choi (H)

Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Cheil Moon (C)

Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Hyun Sik Kim (HS)

Department of Applied Physics and Material Science, California Institute of Technology, Pasadena, California.

Yuljae Cho (Y)

Department of Electrical Engineering Science, University of Oxford, Oxford, United Kingdom.

Seung Nam Cha (SN)

Department of Electrical Engineering Science, University of Oxford, Oxford, United Kingdom.

Jae Eun Jung (JE)

Department of Chemical Engineering and Material Science, Hongik University, Seoul, Korea.

Jung Inn Sohn (JI)

Department of Electrical Engineering Science, University of Oxford, Oxford, United Kingdom.

Jae Eun Jang (JE)

Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea.

Classifications MeSH