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Xu Q, Yang D, Li M, Ren X, Yuan X, Tang L, Wang X, Liu S, Yang M, Liu Y, Yang M. Design and Verification of Piano Playing Assisted Hand Exoskeleton Robot. Biomimetics (Basel) 2024; 9:385. [PMID: 39056826 PMCID: PMC11274512 DOI: 10.3390/biomimetics9070385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton's motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton's ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety.
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Affiliation(s)
- Qiujian Xu
- School of Arts and Design, Yanshan University, Qinhuangdao 066004, China; (Q.X.); (X.Y.); (S.L.); (M.Y.)
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
| | - Dan Yang
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
| | - Meihui Li
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
| | - Xiubo Ren
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
| | - Xinran Yuan
- School of Arts and Design, Yanshan University, Qinhuangdao 066004, China; (Q.X.); (X.Y.); (S.L.); (M.Y.)
| | - Lijun Tang
- Department of Medical Assistant, Mount Eagle Univesity, Winston Salem, NC 27106, USA;
| | - Xiaoyu Wang
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
| | - Siqi Liu
- School of Arts and Design, Yanshan University, Qinhuangdao 066004, China; (Q.X.); (X.Y.); (S.L.); (M.Y.)
| | - Miaomiao Yang
- School of Arts and Design, Yanshan University, Qinhuangdao 066004, China; (Q.X.); (X.Y.); (S.L.); (M.Y.)
| | - Yintong Liu
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
| | - Mingyi Yang
- YSU & DCU Joint Research Centre for the Arts, Music College, Daegu Catholic University, Daegu 38430, Republic of Korea; (D.Y.); (M.L.); (X.R.); (X.W.); (Y.L.)
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Zhang M, Shi Y, Ge H, Sun G, Lian Z, Lu Y. High-Performance Four-Channel Tactile Sensor for Measuring the Magnitude and Orientation of Forces. SENSORS (BASEL, SWITZERLAND) 2024; 24:2808. [PMID: 38732914 PMCID: PMC11086079 DOI: 10.3390/s24092808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
Flexible sensors have gained popularity in recent years. This study proposes a novel structure of a resistive four-channel tactile sensor capable of distinguishing the magnitude and direction of normal forces acting on its sensing surface. The sensor uses EcoflexTM00-30 as the substrate and EGaIn alloy as the conductive filler, featuring four mutually perpendicular and curved channels to enhance the sensor's dynamic responsiveness. Experiments and simulations show that the sensor has a large dynamic range (31.25-100 mΩ), high precision (deviation of repeated pressing below 0.1%), linearity (R2 above 0.97), fast response/recovery time (0.2 s/0.15 s), and robust stability (with fluctuations below 0.9%). This work uses an underactuated robotic hand equipped with a four-channel tactile sensor to grasp various objects. The sensor data collected effectively predicts the shapes of the objects grasped. Furthermore, the four-channel tactile sensor proposed in this work may be employed in smart wearables, medical diagnostics, and other industries.
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Affiliation(s)
| | - Yong Shi
- School of Mechanical Engineering, Heilongjiang University, Harbin 150001, China; (M.Z.); (H.G.); (G.S.); (Z.L.); (Y.L.)
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Ades C, Abd MA, Hutchinson DT, Tognoli E, Du E, Wei J, Engeberg ED. Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration. Biomimetics (Basel) 2024; 9:78. [PMID: 38392124 PMCID: PMC10886511 DOI: 10.3390/biomimetics9020078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.
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Affiliation(s)
- Craig Ades
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Moaed A Abd
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | | | - Emmanuelle Tognoli
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - E Du
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Jianning Wei
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Erik D Engeberg
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
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Li F, Bai Y, Zhao M, Fu T, Men Y, Song R. Research on Robot Screwing Skill Method Based on Demonstration Learning. SENSORS (BASEL, SWITZERLAND) 2023; 24:21. [PMID: 38202883 PMCID: PMC10780978 DOI: 10.3390/s24010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024]
Abstract
A robot screwing skill learning framework based on teaching-learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation. This framework includes task-based teaching, learning, and summarization. We teach a robot to twist and gather the operation's trajectories, define the obstacles with potential functions, and counter the twisting of the robot using a skill-learning-based dynamic movement primitive (DMP) and Gaussian mixture model-Gaussian mixture regression (GMM-GMR). The hole-finding and screwing stages of the process are modeled. In order to verify the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios, obstacle avoidance trends and tightening experiments were conducted. Obstacle avoidance and tightening experiments were conducted on the robot tightening platform for bolts, plastic bottle caps, and faucets. The robot successfully avoided obstacles and completed the twisting task, verifying the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios.
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Affiliation(s)
- Fengming Li
- The School of Information and Engineering, Shandong Jianzhu University, Jinan 250101, China;
| | - Yunfeng Bai
- The School of Control Science and Engineering, Shandong University, Jinan 250061, China; (Y.B.); (M.Z.); (Y.M.)
| | - Man Zhao
- The School of Control Science and Engineering, Shandong University, Jinan 250061, China; (Y.B.); (M.Z.); (Y.M.)
| | - Tianyu Fu
- The School of Control Science and Engineering, Shandong University, Jinan 250061, China; (Y.B.); (M.Z.); (Y.M.)
| | - Yu Men
- The School of Control Science and Engineering, Shandong University, Jinan 250061, China; (Y.B.); (M.Z.); (Y.M.)
| | - Rui Song
- The School of Control Science and Engineering, Shandong University, Jinan 250061, China; (Y.B.); (M.Z.); (Y.M.)
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Lin M, Paul R, Liao X, Doulgeris J, Menzer EL, Dhar UK, Tsai CT, Vrionis FD. A New Method to Evaluate Pressure Distribution Using a 3D-Printed C2-C3 Cervical Spine Model with an Embedded Sensor Array. SENSORS (BASEL, SWITZERLAND) 2023; 23:9547. [PMID: 38067922 PMCID: PMC10708625 DOI: 10.3390/s23239547] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023]
Abstract
Cervical degenerative disc diseases such as myelopathy and radiculopathy often require conventional treatments like artificial cervical disc replacement or anterior cervical discectomy and fusion (ACDF). When designing a medical device, like the stand-alone cage, there are many design inputs to consider. However, the precise biomechanics of the force between the vertebrae and implanted devices under certain conditions require further investigation. In this study, a new method was developed to evaluate the pressure between the vertebrae and implanted devices by embedding a sensor array into a 3D-printed C2-C3 cervical spine. The 3D-printed cervical spine model was subjected to a range of axial loads while under flexion, extension, bending and compression conditions. Cables were used for the application of a preload and a robotic arm was used to recreate the natural spine motions (flexion, extension, and bending). To verify and predict the total pressure between the vertebrae and the implanted devices, a 3D finite element (FE) numerical mathematical model was developed. A preload was represented by applying 22 N of force on each of the anterior tubercles for the C2 vertebra. The results of this study suggest that the sensor is useful in identifying static pressure. The pressure with the robot arm was verified from the FE results under all conditions. This study indicates that the sensor array has promising potential to reduce the trial and error with implants for various surgical procedures, including multi-level artificial cervical disk replacement and ACDF, which may help clinicians to reduce pain, suffering, and costly follow-up procedures.
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Affiliation(s)
- Maohua Lin
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA; (M.L.); (R.P.); (U.K.D.); (C.-T.T.)
| | - Rudy Paul
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA; (M.L.); (R.P.); (U.K.D.); (C.-T.T.)
| | - Xinqin Liao
- Department of Electronic Science, Xiamen University, Xiamen 361005, China;
| | - James Doulgeris
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA; (M.L.); (R.P.); (U.K.D.); (C.-T.T.)
| | - Emma Lilly Menzer
- Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Utpal Kanti Dhar
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA; (M.L.); (R.P.); (U.K.D.); (C.-T.T.)
| | - Chi-Tay Tsai
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA; (M.L.); (R.P.); (U.K.D.); (C.-T.T.)
| | - Frank D. Vrionis
- Department of Neurosurgery, Marcus Neuroscience Institute, Boca Raton Regional Hospital, Boca Raton, FL 33486, USA
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