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Cui Z, Wang W, Xia H, Wang C, Tu J, Ji S, Tan JMR, Liu Z, Zhang F, Li W, Lv Z, Li Z, Guo W, Koh NY, Ng KB, Feng X, Zheng Y, Chen X. Freestanding and Scalable Force-Softness Bimodal Sensor Arrays for Haptic Body-Feature Identification. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2207016. [PMID: 36134530 DOI: 10.1002/adma.202207016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Tactile technologies that can identify human body features are valuable in clinical diagnosis and human-machine interactions. Previously, cutting-edge tactile platforms have been able to identify structured non-living objects; however, identification of human body features remains challenging mainly because of the irregular contour and heterogeneous spatial distribution of softness. Here, freestanding and scalable tactile platforms of force-softness bimodal sensor arrays are developed, enabling tactile gloves to identify body features using machine-learning methods. The bimodal sensors are engineered by adding a protrusion on a piezoresistive pressure sensor, endowing the resistance signals with combined information of pressure and the softness of samples. The simple design enables 112 bimodal sensors to be integrated into a thin, conformal, and stretchable tactile glove, allowing the tactile information to be digitalized while hand skills are performed on the human body. The tactile glove shows high accuracy (98%) in identifying four body features of a real person, and four organ models (healthy and pathological) inside an abdominal simulator, demonstrating identification of body features of the bimodal tactile platforms and showing their potential use in future healthcare and robotics.
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Affiliation(s)
- Zequn Cui
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Wensong Wang
- School of Electrical & Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Huarong Xia
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Changxian Wang
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Jiaqi Tu
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- Institute of Flexible Electronics Technology of THU, Zhejiang, Jiaxing, 314000, China
| | - Shaobo Ji
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Joel Ming Rui Tan
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhihua Liu
- Institute of Materials Research and Engineering, the Agency for Science, Technology and Research, 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Feilong Zhang
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Wenlong Li
- Institute of Materials Research and Engineering, the Agency for Science, Technology and Research, 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Zhisheng Lv
- Institute of Materials Research and Engineering, the Agency for Science, Technology and Research, 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Zheng Li
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Wei Guo
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Nien Yue Koh
- Lee Kong Chian School of Medicine, Novena Campus, Nanyang Technological University, 11 Mandalay Road, Singapore, 308232, Singapore
| | - Kian Bee Ng
- Lee Kong Chian School of Medicine, Novena Campus, Nanyang Technological University, 11 Mandalay Road, Singapore, 308232, Singapore
| | - Xue Feng
- Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing, 100190, China
| | - Yuanjin Zheng
- School of Electrical & Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Xiaodong Chen
- Innovative Center for Flexible Devices (iFLEX) & Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- Institute of Materials Research and Engineering, the Agency for Science, Technology and Research, 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
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Spille JL, Grunwald M, Martin S, Mueller SM. The suppression of spontaneous face touch and resulting consequences on memory performance of high and low self-touching individuals. Sci Rep 2022; 12:8637. [PMID: 35606459 PMCID: PMC9125538 DOI: 10.1038/s41598-022-12044-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/29/2022] [Indexed: 01/18/2023] Open
Abstract
Spontaneous touching of one's own face (sFST) is an everyday behavior that occurs primarily in cognitively and emotionally demanding situations, regardless of a persons' age or gender. Recently, sFST have sparked scientific interest since they are associated with self-inoculation and transmission of respiratory diseases. Several studies addressed the need to reduce sFST behaviors without discussing the underlying functions of this spontaneous behavior. In addition, the question of why this behavior occurs very frequently in some individuals (high self-touching individuals, HT) but less frequently in others (low self-touching individuals, LT) has not yet been addressed. For the first time, we distinguished between HT and LT and investigated the behavioral consequences of sFST suppression in these two groups. For this purpose, we examined performance outcomes of 49 participants depending on sFST behaviors during a haptic working memory task. In addition, we assessed personality traits of HT and LT using the Freiburg Personality Inventory (FPI-R). The results of our study reveal that suppressing sFST in HT is negatively related to memory performance outcomes. Moreover, HT show tendencies to differ from LT in certain personality traits. Our results highlight the relevance of distinguishing between HT and LT in future studies of sFST.
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Affiliation(s)
- Jente L Spille
- Haptic Research Laboratory, Paul Flechsig Institute - Centre of Neuropathology and Brain Research, University of Leipzig, 04103, Leipzig, Germany
| | - Martin Grunwald
- Haptic Research Laboratory, Paul Flechsig Institute - Centre of Neuropathology and Brain Research, University of Leipzig, 04103, Leipzig, Germany
| | - Sven Martin
- Haptic Research Laboratory, Paul Flechsig Institute - Centre of Neuropathology and Brain Research, University of Leipzig, 04103, Leipzig, Germany
| | - Stephanie M Mueller
- Haptic Research Laboratory, Paul Flechsig Institute - Centre of Neuropathology and Brain Research, University of Leipzig, 04103, Leipzig, Germany.
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Cabibihan JJ, El-Noamany A, Ahmed AMRM, Ang MH. Guidelines for Robot-to-Human Handshake From the Movement Nuances in Human-to-Human Handshake. Front Robot AI 2022; 9:758519. [PMID: 35419414 PMCID: PMC8996188 DOI: 10.3389/frobt.2022.758519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
The handshake is the most acceptable gesture of greeting in many cultures throughout many centuries. To date, robotic arms are not capable of fully replicating this typical human gesture. Using multiple sensors that detect contact forces and displacements, we characterized the movements that occured during handshakes. A typical human-to-human handshake took around 3.63 s (SD = 0.45 s) to perform. It can be divided into three phases: reaching (M = 0.92 s, SD = 0.45 s), contact (M = 1.96 s, SD = 0.46 s), and return (M = 0.75 s, SD = 0.12 s). The handshake was further investigated to understand its subtle movements. Using a multiphase jerk minimization model, a smooth human-to-human handshake can be modelled with fifth or fourth degree polynomials at the reaching and return phases, and a sinusoidal function with exponential decay at the contact phase. We show that the contact phase (1.96 s) can be further divided according to the following subphases: preshake (0.06 s), main shake (1.31 s), postshake (0.06 s), and a period of no movement (0.52 s) just before both hands are retracted. We compared these to the existing handshake models that were proposed for physical human-robot interaction (pHRI). From our findings in human-to-human handshakes, we proposed guidelines for a more natural handshake movement between humanoid robots and their human partners.
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Affiliation(s)
- John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Ahmed El-Noamany
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | | | - Marcelo H. Ang
- Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore
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Abstract
AbstractFor some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.
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