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Plug-and-Play Gesture Control Using Muscle and Motion Sensors

Authors:

Joseph DelPreto,

Daniela RusAuthors Info & Claims

HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

Pages 439 - 448

https://doi.org/10.1145/3319502.3374823

Published: 09 March 2020 Publication History

Abstract

As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time.

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References

[1]

Di Ao, Rong Song, and JinWu Gao. 2017. Movement Performance of Human-Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot. IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, 8 (Aug 2017), 1125--1134. https://doi.org/10.1109/TNSRE.2016.2583464

[2]

Ognjen Arandjelovic and Roberto Cipolla. 2005. Incremental Learning of Temporally-Coherent Gaussian Mixture Models. In Proceedings of the British Machine Vision Conference (BMVC). BMVA Press, 59.1--59.10. https://doi.org/10.5244/c.19.59

[3]

Panagiotis K Artemiadis and Kostas J Kyriakopoulos. 2010a. EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings. IEEE Transactions on Robotics, Vol. 26, 2 (April 2010), 393--398. https://doi.org/10.1109/TRO.2009.2039378

Digital Library

[4]

Panagiotis K Artemiadis and Kostas J Kyriakopoulos. 2010b. An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features. IEEE Transactions on Information Technology in Biomedicine, Vol. 14, 3 (2010), 582--588. https://doi.org/10.1109/TITB.2010.2040832

Digital Library

[5]

Ari Y Benbasat and Joseph A Paradiso. 2001. An Inertial Measurement Framework for Gesture Recognition and Applications. In International Gesture Workshop. Springer, 9--20. https://doi.org/10.1007/3--540--47873--6_2

[6]

Christopher M Bishop. 2006. Pattern Recognition and Machine Learning .Springer.

Digital Library

[7]

Sylvain Calinon and Aude Billard. 2007. Incremental Learning of Gestures by Imitation in a Humanoid Robot. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI) . ACM, 255--262. https://doi.org/10.1145/1228716.1228751

Digital Library

[8]

Ettore Cavallaro, Jacob Rosen, Joel C Perry, Stephen Burns, and Blake Hannaford. 2005. Hill-Based Model as a Myoprocessor for a Neural Controlled Powered Exoskeleton Arm - Parameters Optimization. In 2005 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 4514--4519. https://doi.org/10.1109/ROBOT.2005.1570815

[9]

Moses Charikar, Chandra Chekuri, Tomás Feder, and Rajeev Motwani. 2004. Incremental Clustering and Dynamic Information Retrieval. SIAM J. Comput., Vol. 33, 6 (2004), 1417--1440. https://doi.org/10.1137/S0097539702418498

Digital Library

[10]

Jean-Baptiste Chossat, Yiwei Tao, Vincent Duchaine, and Yong-Lae Park. 2015. Wearable Soft Artificial Skin for Hand Motion Detection with Embedded Microfluidic Strain Sensing. In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2568--2573. https://doi.org/10.1109/ICRA.2015.7139544

[11]

Jun-Uk Chu, Inhyuk Moon, Yun-Jung Lee, Shin-Ki Kim, and Mu-Seong Mun. 2007. A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control. IEEE/ASME Transactions on Mechatronics, Vol. 12, 3 (June 2007), 282--290. https://doi.org/10.1109/TMECH.2007.897262

[12]

Beau Crawford, Kai Miller, Pradeep Shenoy, and Rajesh Rao. 2005. Real-Time Classification of Electromyographic Signals for Robotic Control. In Proceedings of the 20th National Conference on Artificial Intelligence . AAAI Press, 523--528. http://new.aaai.org/Papers/AAAI/2005/AAAI05-082.pdf

[13]

Louis J Dankovich and Sarah Bergbreiter. 2019. Gesture Recognition via Flexible Capacitive Touch Electrodes. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 9028--9034. https://doi.org/10.1109/ICRA.2019.8794202

[14]

Carlo J De Luca. 2002. Surface Electromyography: Detection and Recording. DelSys Incorporated (2002). https://www.semanticscholar.org/paper/992836d36c1ae4b56314e8b15f087822a1995c93

[15]

Arnaud Declercq and Justus H Piater. 2008. Online Learning of Gaussian Mixture Models - A Two-Level Approach. In Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISAPP). 605--611. http://hdl.handle.net/2268/37204

[16]

Joseph DelPreto, Andres F Salazar-Gomez, Stephanie Gil, Ramin M Hasani, Frank H Guenther, and Daniela Rus. 2018. Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection. In Robotics: Science and Systems (RSS) . https://doi.org/10.15607/RSS.2018.XIV.063

[17]

Joseph DelPreto, Andres F Salazar-Gomez, Stephanie Gil, Ramin M Hasani, Frank H Guenther, and Daniela Rus. 2020. Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection. Autonomous Robots (conditionally accepted) (2020).

[18]

Joseph DelPreto and Daniela Rus. 2019. Sharing the Load: Human-Robot Team Lifting Using Muscle Activity. In 2019 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 7906--7912. https://doi.org/10.1109/ICRA.2019.8794414

[19]

Matthew DiCicco, Lenny Lucas, and Yoky Matsuoka. 2004. Comparison of Control Strategies for an EMG Controlled Orthotic Exoskeleton for the Hand. In 2004 IEEE International Conference on Robotics and Automation (ICRA), Vol. 2. IEEE, 1622--1627. https://doi.org/10.1109/ROBOT.2004.1308056

[20]

Laura Dipietro, Angelo M Sabatini, and Paolo Dario. 2008. A Survey of Glove-Based Systems and Their Applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 38, 4 (2008), 461--482. https://doi.org/10.1109/TSMCC.2008.923862

Digital Library

[21]

Jacob Eisenstein and Randall Davis. 2004. Visual and Linguistic Information in Gesture Classification. In Proceedings of the 6th International Conference on Multimodal Interfaces . ACM, 113--120. https://doi.org/10.1145/1027933.1027954

Digital Library

[22]

Paulo Martins Engel and Milton Roberto Heinen. 2010. Incremental Learning of Multivariate Gaussian Mixture Models. In Advances in Artificial Intelligence -- SBIA 2010. Springer, 82--91. https://doi.org/10.1007/978--3--642--16138--4_9

[23]

RARC Gopura, DSV Bandara, JMP Gunasekara, and TSS Jayawardane. 2013. Recent Trends in EMG-Based Control Methods for Assistive Robots. In Electrodiagnosis in New Frontiers of Clinical Research. IntechOpen. https://doi.org/10.5772/56174

[24]

Ranathunga Arachchilage Ruwan Chandra Gopura, Kazuo Kiguchi, and Yang Li. 2009. SUEFUL-7: A 7DOF Upper-Limb Exoskeleton Robot with Muscle-Model-Oriented EMG-Based Control. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 1126--1131. https://doi.org/10.1109/IROS.2009.5353935

[25]

Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Advances in Psychology . Vol. 52. Elsevier, 139--183. https://doi.org/10.1016/S0166--4115(08)62386--9

[26]

Bastian Hartmann and Norbert Link. 2010. Gesture Recognition with Inertial Sensors and Optimized DTW Prototypes. In 2010 IEEE International Conference on Systems, Man and Cybernetics . IEEE, 2102--2109. https://doi.org/10.1109/ICSMC.2010.5641703

[27]

Guan-Feng He, Sun-Kyung Kang, Won-Chang Song, and Sung-Tae Jung. 2011. Real-Time Gesture Recognition using 3D Depth Camera. In 2011 IEEE 2nd International Conference on Software Engineering and Service Science. IEEE, 187--190. https://doi.org/10.1109/ICSESS.2011.5982286

[28]

Neville Hogan. 1984. Adaptive Control of Mechanical Impedance by Coactivation of Antagonist Muscles. IEEE Trans. Automat. Control, Vol. 29, 8 (1984), 681--690. https://doi.org/10.1109/TAC.1984.1103644

[29]

Yangjian Huang, Weichao Guo, Jianwei Liu, Jiayuan He, Haisheng Xia, Xinjun Sheng, Haitao Wang, Xuetao Feng, and Peter B Shull. 2015. Preliminary Testing of a Hand Gesture Recognition Wristband Based on EMG and Inertial Sensor Fusion. In Intelligent Robotics and Applications . Springer, 359--367. https://doi.org/10.1007/978--3--319--22879--2_33

[30]

Nebojsa Jojic, Barry Brumitt, Brian Meyers, Steve Harris, and Thomas Huang. 2000. Detection and Estimation of Pointing Gestures in Dense Disparity Maps. In Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580). IEEE, 468--475. https://doi.org/10.1109/AFGR.2000.840676

Digital Library

[31]

Holger Junker, Oliver Amft, Paul Lukowicz, and Gerhard Tröster. 2008. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, Vol. 41, 6 (2008), 2010--2024. https://doi.org/10.1016/j.patcog.2007.11.016

Digital Library

[32]

Holger Kenn, Friedrich Van Megen, and Robert Sugar. 2007. A glove-based gesture interface for wearable computing applications. In 4th International Forum on Applied Wearable Computing 2007. VDE, 1--10. https://ieeexplore.ieee.org/abstract/document/5760491

[33]

Kazuo Kiguchi and Yoshiaki Hayashi. 2012. An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 42, 4 (Aug 2012), 1064--1071. https://doi.org/10.1109/TSMCB.2012.2185843

Digital Library

[34]

Jonghwa Kim, Stephan Mastnik, and Elisabeth André. 2008. EMG-Based Hand Gesture Recognition for Realtime Biosignal Interfacing. In Proceedings of the 13th International Conference on Intelligent User Interfaces . ACM, 30--39. https://doi.org/10.1145/1378773.1378778

Digital Library

[35]

Przemyslaw A Lasota and Julie A Shah. 2019. Bayesian Estimator for Partial Trajectory Alignment. In Robotics: Science and Systems (RSS). Freiburg im Breisgau, Germany. https://doi.org/10.15607/RSS.2019.XV.080

[36]

T Lenzi, SMM De Rossi, N Vitiello, and MC Carrozza. 2011. Proportional EMG control for upper-limb powered exoskeletons. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society . IEEE, 628--631. https://doi.org/10.1109/IEMBS.2011.6090139

[37]

Yi Li. 2012. Hand Gesture Recognition Using Kinect. In 2012 IEEE International Conference on Computer Science and Automation Engineering. IEEE, 196--199. https://doi.org/10.1109/ICSESS.2012.6269439

[38]

Stuart Lloyd. 1982. Least Squares Quantization in PCM . IEEE Transactions on Information Theory, Vol. 28, 2 (1982), 129--137. https://doi.org/10.1109/TIT.1982.1056489

Digital Library

[39]

Natalia M López, Fernando di Sciascio, Carlos M Soria, and Max E Valentinuzzi. 2009. Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm. BioMedical Engineering OnLine, Vol. 8, 1 (2009), 5. https://doi.org/10.1186/1475--925X-8--5

[40]

Kurt Manal and Thomas S Buchanan. 2003. A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms. Journal of Biomechanics, Vol. 36, 8 (2003), 1197--1202. https://doi.org/10.1016/S0021--9290(03)00152-0

[41]

Jess McIntosh, Charlie McNeill, Mike Fraser, Frederic Kerber, Markus Löchtefeld, and Antonio Krüger. 2016. EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2332--2342. https://doi.org/10.1145/2858036.2858093

Digital Library

[42]

Geoffrey McLachlan and David Peel. 2004. Finite mixture models .John Wiley & Sons.

[43]

Samir Menon, Toki Migimatsu, and Oussama Khatib. 2016. A Parameterized Family of Anatomically Accurate Human Upper-Body Musculoskeletal Models for Dynamic Simulation & Control. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). 587--594. https://doi.org/10.1109/HUMANOIDS.2016.7803334

Digital Library

[44]

Sushmita Mitra and Tinku Acharya. 2007. Gesture Recognition: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 37, 3 (2007), 311--324. https://doi.org/10.1109/TSMCC.2007.893280

Digital Library

[45]

Marcello Mulas, Michele Folgheraiter, and Giuseppina Gini. 2005. An EMG-Controlled Exoskeleton for Hand Rehabilitation. 9th International Conference on Rehabilitation Robotics (ICORR) (2005), 371--374. https://doi.org/10.1109/ICORR.2005.1501122

[46]

Luka Peternel, Nikos Tsagarakis, and Arash Ajoudani. 2017. A Human--Robot Co-Manipulation Approach Based on Human Sensorimotor Information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, 7 (July 2017), 811--822. https://doi.org/10.1109/TNSRE.2017.2694553

[47]

Anahita Qashqai, Hossein Ehsani, and Mostafa Rostami. 2015. A Hill-based EMG-driven model to estimate elbow torque during flexion and extention. In 2015 22nd Iranian Conference on Biomedical Engineering (ICBME) . IEEE, 166--171. https://doi.org/10.1109/ICBME.2015.7404136

[48]

Jinxian Qi, Guozhang Jiang, Gongfa Li, Ying Sun, and Bo Tao. 2019. Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition. IEEE Access, Vol. 7 (2019), 61378--61387. https://doi.org/10.1109/ACCESS.2019.2914728

[49]

Jo ao Luiz AS Ramos and Marco A Meggiolaro. 2014. Use of Surface Electromyography for Human Amplification Using an Exoskeleton Driven by Artificial Pneumatic Muscles. In 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE, 585--590. https://doi.org/10.1109/BIOROB.2014.6913841

[50]

Ali-Akbar Samadani and Dana Kulic. 2014. Hand Gesture Recognition Based on Surface Electromyography. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 4196--4199. https://doi.org/10.1109/EMBC.2014.6944549

[51]

T Scott Saponas, Desney S Tan, Dan Morris, and Ravin Balakrishnan. 2008. Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 515--524. https://doi.org/10.1145/1357054.1357138

Digital Library

[52]

Pradeep Shenoy, Kai J Miller, Beau Crawford, and Rajesh PN Rao. 2008. Online Electromyographic Control of a Robotic Prosthesis. IEEE Transactions on Biomedical Engineering, Vol. 55, 3 (2008), 1128--1135. https://doi.org/10.1109/TBME.2007.909536

[53]

Mingzhou Song and Hongbin Wang. 2005. Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering. In Intelligent Computing: Theory and Applications III, Vol. 5803. International Society for Optics and Photonics, SPIE, 174--183. https://doi.org/10.1117/12.601724

[54]

Yale Song, David Demirdjian, and Randall Davis. 2012. Continuous Body and Hand Gesture Recognition for Natural Human-Computer Interaction. ACM Transactions on Interactive Intelligent Systems (TiiS), Vol. 2, 1 (2012), 5. https://doi.org/10.1145/2133366.2133371

Digital Library

[55]

Mark Tomaszewski. 2017. Myo SDK MATLAB MEX Wrapper . https://github.com/mark-toma/MyoMex

[56]

Michael Van den Bergh and Luc Van Gool. 2011. Combining RGB and ToF Cameras for Real-Time 3D Hand Gesture Interaction. In 2011 IEEE Workshop on Applications of Computer Vision (WACV) . IEEE, 66--72. https://doi.org/10.1109/WACV.2011.5711485

Digital Library

[57]

Jonathan Weisz, Peter K Allen, Alexander G Barszap, and Sanjay S Joshi. 2017. Assistive grasping with an augmented reality user interface. The International Journal of Robotics Research, Vol. 36, 5--7 (2017), 543--562. https://doi.org/10.1177/0278364917707024

[58]

Ying Yin and Randall Davis. 2014. Real-Time Continuous Gesture Recognition for Natural Human-Computer Interaction. In 2014 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) . IEEE, 113--120. https://doi.org/10.1109/VLHCC.2014.6883032

[59]

Yue H Yin, Yuan J Fan, and Li D Xu. 2012. EMG and EPP-Integrated Human-Machine Interface Between the Paralyzed and Rehabilitation Exoskeleton. IEEE Transactions on Information Technology in Biomedicine, Vol. 16, 4 (July 2012), 542--549. https://doi.org/10.1109/TITB.2011.2178034

Digital Library

[60]

Felix E Zajac. 1989. Muscle and Tendon: Properties, Models, Scaling, and Application to Biomechanics and Motor Control. Critical Reviews in Biomedical Engineering, Vol. 17, 4 (1989), 359--411. http://e.guigon.free.fr/rsc/article/Zajac89.pdf

[61]

Xu Zhang, Xiang Chen, Yun Li, Vuokko Lantz, Kongqiao Wang, and Jihai Yang. 2011. A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 41, 6 (2011), 1064--1076. https://doi.org/10.1109/TSMCA.2011.2116004

Digital Library

[62]

Shengli Zhou, Qing Shan, Fei Fei, Wen J Li, Chung Ping Kwong, Patrick CK Wu, Bojun Meng, Christina KH Chan, and Jay YJ Liou. 2009. Gesture Recognition for Interactive Controllers Using MEMS Motion Sensors. In 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems . IEEE, 935--940. https://doi.org/10.1109/NEMS.2009.5068728

[63]

Thomas G Zimmerman, Jaron Lanier, Chuck Blanchard, Steve Bryson, and Young Harvill. 1986. A Hand Gesture Interface Device. In Proceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface . ACM, 189--192. https://doi.org/10.1145/29933.275628

Digital Library

Cited By

Ren BGao ZLi YYou CChang lHan JLi J(2024)Real-Time Continuous Gesture Recognition System Based on PSO-PNNMeasurement Science and Technology10.1088/1361-6501/ad2a33Online publication date: 16-Feb-2024
https://doi.org/10.1088/1361-6501/ad2a33
Khen GZhao DBaca J(2024)Intuitive Human–Swarm Interaction with Gesture Recognition and Machine LearningProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-3556-3_39(485-494)Online publication date: 10-Aug-2024
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Plug-and-Play Gesture Control Using Muscle and Motion Sensors

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cover image ACM Conferences

HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

March 2020

690 pages

ISBN:9781450367462

DOI:10.1145/3319502

General Chairs:
Tony Belpaeme
Ghent University, Belgium
,
James Young
University of Manitoba, Canada
,
Program Chairs:
Hatice Gunes
University of Cambridge, UK
,
Laurel Riek
UC San Diego, USA

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Published: 09 March 2020

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HRI '20: ACM/IEEE International Conference on Human-Robot Interaction

March 23 - 26, 2020

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https://doi.org/10.1088/1361-6501/ad2a33
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