research-article

Recognizing Running Movement Changes with Quaternions on a Sports Watch

Authors:

Matthias Seuter,

Alexandra Pollock,

Christian KrayAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 4

Article No.: 151, Pages 1 - 18

https://doi.org/10.1145/3432197

Published: 18 December 2020 Publication History

Abstract

Sports watches are popular amongst runners but limited in terms of sensor locations (i.e., one location at the wrist). Thus, apps on the watch cannot directly sense movement changes in arbitrary body locations. This, in turn, severely limits training support services, contextual awareness and seamless interaction mechanisms. Our approach addresses this gap and connects the watch with two strategically placed inertial measurement units (IMUs) via bluetooth low energy (BLE). Our prototypical app for Wear OS receives orientation (quaternion) data and matches the sensors to the arm or leg segments using a flexible and simple procedure. We collected data from eight runners and used support vector machines (SVMs) to recognize different movements. Findings from our evaluation with different parameters indicate feasible recognition and low false-positive rates for two to three different movements, for both placements. Our approach can thus help to improve applications that support training and thus contributes to developing motion capture for personal use; it also enables movement-based interaction while running.

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References

[1]

Shamir Alavi, Dennis Arsenault, and Anthony Whitehead. 2016. Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors. Sensors 16, 5 (April 2016), 605. https://doi.org/10.3390/s16050605

[2]

Buke Ao, Yongcai Wang, Hongnan Liu, Deying Li, Lei Song, and Jianqiang Li. 2018. Context Impacts in Accelerometer-Based Walk Detection and Step Counting. Sensors 18, 11 (Nov. 2018), 3604. https://doi.org/10.3390/s18113604

[3]

Dennis Arsenault and Anthony Whitehead. 2015. Wearable Sensor Networks for Motion Capture. In Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment. IEEE, Torino, Italy. https://doi.org/10.4108/icst.intetain.2015.259265

[4]

Roger Boldu, Alexandru Dancu, Denys J.C. Matthies, Pablo Gallego Cascón, Shanaka Ransir, and Suranga Nanayakkara. 2018. Thumb-In-Motion: Evaluating Thumb-to-Ring Microgestures for Athletic Activity. In Proceedings of the Symposium on Spatial User Interaction (SUI '18). ACM, New York, NY, USA, 150--157. https://doi.org/10.1145/3267782.3267796

Digital Library

[5]

Mahmoud Ben Brahem, Bob-Antoine J. Ménélas, and Martin J. D. Otis. 2013. Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI. Procedia Computer Science 19 (Jan. 2013), 453--460. https://doi.org/10.1016/j.procs.2013.06.061

[6]

Agata Brajdic and Robert Harle. 2013. Walk detection and step counting on unconstrained smartphones. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp '13). Association for Computing Machinery, Zurich, Switzerland, 225--234. https://doi.org/10.1145/2493432.2493449

Digital Library

[7]

Carmen M. N. Brigante, Nunzio Abbate, Adriano Basile, Alessandro Carmelo Faulisi, and Salvatore Sessa. 2011. Towards Miniaturization of a MEMS-Based Wearable Motion Capture System. IEEE Transactions on Industrial Electronics 58, 8 (Aug. 2011), 3234--3241. https://doi.org/10.1109/TIE.2011.2148671

[8]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 3 (May 2011), 27:1-27:27. https://doi.org/10.1145/1961189.1961199

Digital Library

[9]

Rodrigo de Oliveira and Nuria Oliver. 2008. TripleBeat: Enhancing Exercise Performance with Persuasion. In Proceedings of the 10th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI '08). ACM, New York, NY, USA, 255--264. https://doi.org/10.1145/1409240.1409268

Digital Library

[10]

Nicholas Diliberti, Chao Peng, Christopher Kaufman, Yangzi Dong, and Jeffrey T. Hansberger. 2019. Real-Time Gesture Recognition Using 3D Sensory Data and a Light Convolutional Neural Network. In Proceedings of the 27th ACM International Conference on Multimedia (MM '19). Association for Computing Machinery, Nice, France, 401--410. https://doi.org/10.1145/3343031.3350958

[11]

Sheila A. Dugan and Krishna P. Bhat. 2005. Biomechanics and analysis of running gait. Physical Medicine and Rehabilitation Clinics of North America 16, 3 (Aug. 2005), 603--621. https://doi.org/10.1016/j.pmr.2005.02.007

[12]

Steven Díaz, Jeannie B. Stephenson, and Miguel A. Labrador. 2020. Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis. Applied Sciences 10, 1 (Jan. 2020), 234. https://doi.org/10.3390/app10010234

[13]

SparkFun Electronics. 2020. Arduino library for the Qwiic VR IMU (BNO080). Retrieved July 3, 2020 from https://github.com/sparkfun/SparkFun_BNO080_Arduino_Library

[14]

SparkFun Electronics. 2020. SparkFun VR IMU Breakout - BNO080. Retrieved July 3, 2020 from https://www.sparkfun.com/products/14686

[15]

Garmin. 2020. Garmin Running Dynamics Pod. Retrieved July 3, 2020 from buy.garmin.com/en-US/US/p/561205

[16]

Marc Gowing, Amin Ahmadi, François Destelle, David S. Monaghan, Noel E. O'Connor, and Kieran Moran. 2014. Kinect vs. Low-cost Inertial Sensing for Gesture Recognition. In MultiMedia Modeling (Lecture Notes in Computer Science), Cathal Gurrin, Frank Hopfgartner, Wolfgang Hurst, Håvard Johansen, Hyowon Lee, and Noel O'Connor (Eds.). Springer International Publishing, Cham, 484--495. https://doi.org/10.1007/978-3-319-04114-8_41

[17]

Linsey Griffin, Crystal Compton, and Lucy E. Dunne. 2016. An Analysis of the Variability of Anatomical Body References Within Ready-to-wear Garment Sizes. In Proceedings of the 2016 ACM International Symposium on Wearable Computers (ISWC '16). ACM, New York, NY, USA, 84--91. https://doi.org/10.1145/2971763.2971800

[18]

Nur Al-huda Hamdan, Ravi Kanth Kosuru, Christian Corsten, and Jan Borchers. 2017. Run&Tap: Investigation of On-Body Tapping for Runners. In Proceedings of the Interactive Surfaces and Spaces on ZZZ - ISS '17. ACM Press, Brighton, United Kingdom, 280--286. https://doi.org/10.1145/3132272.3134140

Digital Library

[19]

Tian Hao, Guoliang Xing, and Gang Zhou. 2015. RunBuddy: A Smartphone System for Running Rhythm Monitoring. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). ACM, New York, NY, USA, 133--144.

Digital Library

[20]

Brian K. Higginson. 2009. Methods of Running Gait Analysis:. Current Sports Medicine Reports 8, 3 (2009), 136--141.

[21]

Hillcrest Laboratories, Inc. 2017. BNO080 Data Sheet. Retrieved July 3, 2020 from https://cdn.sparkfun.com/assets/1/3/4/5/9/BNO080_Datasheet_v1.3.pdf

[22]

Hillcrest Laboratories, Inc. 2017. BNO080 Sensor Calibration Procedure. Retrieved July 3, 2020 from https://cdn.sparkfun.com/assets/9/e/1/d/9/Sensor-Calibration-Procedure-v1.1.pdf

[23]

Bas Van Hooren, Jos Goudsmit, Juan Restrepo, and Steven Vos. 2019. Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements. Journal of Sports Sciences 0, 0 (Dec. 2019), 1--17. https://doi.org/10.1080/02640414.2019.1690960

[24]

Adafruit Industries. 2020. Adafruit Feather M0 Bluefruit LE. Retrieved July 3, 2020 from https://web.archive.org/web/20200703210317/https://www.adafruit.com/product/2995

[25]

Adafruit Industries. 2020. Arduino library for the Adafruit BluefruitLE nRF51. Retrieved July 3, 2020 from https://github.com/adafruit/Adafruit_BluefruitLE_nRF51

[26]

Naomi Johnson, Michael Jones, Kevin Seppi, and Lawrence Thatcher. 2019. Understanding How Non-experts Collect and Annotate Activity Data. In Human Activity Sensing: Corpus and Applications, Nobuo Kawaguchi, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, and Kristof Van Laerhoven (Eds.). Springer International Publishing, Cham, 91--110. https://doi.org/10.1007/978-3-030-13001-5_7

[27]

Xiaomin Kang, Baoqi Huang, Runze Yang, and Guodong Qi. 2018. Accurately Counting Steps of the Pedestrian with Varying Walking Speeds. In 2018 IEEE Smart World, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). https://doi.org/10.1109/SmartWorld.2018.00134

[28]

Joseph J. LaViola. 2013. 3D Gestural Interaction: The State of the Field. ISRN Artificial Intelligence 2013 (2013), 1--18. https://doi.org/10.1155/2013/514641

[29]

Yiqin Lu, Bingjian Huang, Chun Yu, Guahong Liu, and Yuanchun Shi. 2020. Designing and Evaluating Hand-to-Hand Gestures with Dual Commodity Wrist-Worn Devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (March 2020), 20:1-20:27. https://doi.org/10.1145/3380984

Digital Library

[30]

Richard Magill and David Anderson. 2014. Motor learning and control: concepts and applications (tenth edition ed.). McGraw-Hill, New York, NY.

[31]

Charles Malleson, Andrew Gilbert, Matthew Trumble, John Collomosse, Adrian Hilton, and Marco Volino. 2017. Real-Time Full-Body Motion Capture from Video and IMUs. In 2017 International Conference on 3D Vision (3DV). IEEE, Qingdao, 449--457. https://doi.org/10.1109/3DV.2017.00058

[32]

F. Landis Markley, Yang Cheng, John L. Crassidis, and Yaakov Oshman. 2007. Averaging Quaternions. Journal of Guidance, Control, and Dynamics 30, 4 (July 2007), 1193--1197. https://doi.org/10.2514/1.28949

[33]

Christine F. Martindale, Florian Hoenig, Christina Strohrmann, and Bjoern M. Eskofier. 2017. Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models. Sensors 17, 10 (Oct. 2017), 2328. https://doi.org/10.3390/s17102328

[34]

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

Digital Library

[35]

Rolf Moe-Nilssen and Jorunn L. Helbostad. 2004. Estimation of gait cycle characteristics by trunk accelerometry. Journal of Biomechanics 37, 1 (Jan. 2004), 121--126. https://doi.org/10.1016/S0021-9290(03)00233-1

[36]

Thomas B. Moeslund, Adrian Hilton, and Volker Krüger. 2006. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 2 (2006), 90 - 126. Special Issue on Modeling People: Vision-based understanding of a person's shape, appearance, movement and behaviour.

Digital Library

[37]

Vivian Genaro Motti. 2020. Design Guidelines and Evaluation. Springer International Publishing, Cham, 109--148. https://doi.org/10.1007/978-3-030-27111-4_4

[38]

Chaithanya Kumar Mummadi, Frederic Philips Peter Leo, Keshav Deep Verma, Shivaji Kasireddy, Philipp M. Scholl, Jochen Kempfle, and Kristof Van Laerhoven. 2018. Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove. Informatics 5, 2 (June 2018), 28. https://doi.org/10.3390/informatics5020028

[39]

Meinard Müller. 2007. Information retrieval for music and motion. Springer, New York.

Digital Library

[40]

Tom F Novacheck. 1998. The biomechanics of running. Gait & Posture 7, 1 (1998), 77--95.

[41]

Nuria Oliver and Fernando Flores-Mangas. 2006. MPTrain: A Mobile, Music and Physiology-based Personal Trainer. In Proceedings of the 8th Conference on Human-computer Interaction with Mobile Devices and Services (MobileHCI '06). ACM, New York, NY, USA, 21--28. https://doi.org/10.1145/1152215.1152221

Digital Library

[42]

Tim Op De Beéck, Wannes Meert, Kurt Schütte, Benedicte Vanwanseele, and Jesse Davis. 2018. Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, London, United Kingdom, 606--615. https://doi.org/10.1145/3219819.3219864

Digital Library

[43]

Andres A. Perez and Miguel A. Labrador. 2016. A Smartphone-Based System for Clinical Gait Assessment. In 2016 IEEE International Conference on Smart Computing (SMARTCOMP). 1--8. https://doi.org/10.1109/SMARTCOMP.2016.7501675

[44]

Polar. 2019. Polar M600. Retrieved July 3, 2020 from https://web.archive.org/web/20200703211316/https://support.polar.com/us-en/support/m600

[45]

RapidMiner. 2020. RapidMiner. Retrieved July 3, 2020 from https://web.archive.org/web/20200703205540/https://rapidminer.com/

[46]

Jasper Reenalda, Erik Maartens, Lotte Homan, and J. H. (Jaap) Buurke. 2016. Continuous three dimensional analysis of running mechanics during a marathon by means of inertial magnetic measurement units to objectify changes in running mechanics. Journal of Biomechanics 49, 14 (Oct. 2016), 3362--3367. https://doi.org/10.1016/j.jbiomech.2016.08.032

[47]

RunScribe. 2020. RunScribe. Retrieved July 3, 2020 from https://web.archive.org/web/20200602133110/https://runscribe.com/

[48]

João Santos, António Costa, and Maria João Nicolau. 2019. Autocorrelation analysis of accelerometer signal to detect and count steps of smartphone users. In 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN). 1--7. https://doi.org/10.1109/IPIN.2019.8911755

[49]

Stefan Schneegass and Alexandra Voit. 2016. GestureSleeve: Using Touch Sensitive Fabrics for Gestural Input on the Forearm for Controlling Smartwatches. In Proceedings of the 2016 ACM International Symposium on Wearable Computers (ISWC '16). ACM, New York, NY, USA, 108--115.

Digital Library

[50]

Thomas Seel, Jörg Raisch, and Thomas Schauer. 2014. IMU-Based Joint Angle Measurement for Gait Analysis. Sensors 14, 4 (April 2014), 6891--6909. https://doi.org/10.3390/s140406891

[51]

Matthias Seuter, Eduardo Rodriguez Macrillante, Gernot Bauer, and Christian Kray. 2018. Running with Drones: Desired Services and Control Gestures. In Proceedings of the 30th Australian Conference on Computer-Human Interaction (OzCHI '18). ACM, New York, NY, USA, 384--395. https://doi.org/10.1145/3292147.3292156

Digital Library

[52]

Matthias Seuter, Lucien Opitz, Gernot Bauer, and David Hochmann. 2016. Live-feedback from the IMUs: Animated 3D Visualization for Everyday-exercising. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp '16). ACM, New York, NY, USA, 904--907. https://doi.org/10.1145/2968219.2968576

Digital Library

[53]

Matthias Seuter, Max Pfeiffer, Gernot Bauer, Karen Zentgraf, and Christian Kray. 2017. Running with Technology: Evaluating the Impact of Interacting with Wearable Devices on Running Movement. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3 (Sept. 2017), 101:1-101:17. https://doi.org/10.1145/3130966

Digital Library

[54]

Boris Smus and Vassilis Kostakos. 2010. Running Gestures: Hands-free Interaction During Physical Activity. In Proceedings of the 12th ACM International Conference Adjunct Papers on Ubiquitous Computing - Adjunct (UbiComp '10 Adjunct). ACM, New York, NY, USA, 433--434.

Digital Library

[55]

Christina Strohrmann, Holger Harms, Cornelia Kappeler-Setz, and Gerhard Troster. 2012. Monitoring Kinematic Changes With Fatigue in Running Using Body-Worn Sensors. Trans. Info. Tech. Biomed. 16, 5 (Sept. 2012), 983--990. https://doi.org/10.1109/TITB.2012.2201950

[56]

Stryd. 2020. Stryd. Retrieved July 3, 2020 from https://web.archive.org/web/20200703205521/https://www.stryd.com/

[57]

Jacopo Tosi, Fabrizio Taffoni, Marco Santacatterina, Roberto Sannino, and Domenico Formica. 2017. Performance Evaluation of Bluetooth Low Energy: A Systematic Review. Sensors (Basel, Switzerland) 17, 12 (Dec. 2017). https://doi.org/10.3390/s17122898

[58]

M. Urban, P. Bajcsy, R. Kooper, and J. Lementec. 2004. Recognition of arm gestures using multiple orientation sensors: repeatability assessment. In Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems ( IEEE Cat. No.04TH8749). 553--558. https://doi.org/10.1109/ITSC.2004.1398960

[59]

Jacek K. Urbanek, Jaroslaw Harezlak, Nancy W. Glynn, Tamara Harris, Ciprian Crainiceanu, and Vadim Zipunnikov. 2017. Stride variability measures derived from wrist- and hip-worn accelerometers. Gait & Posture 52 (Feb. 2017), 217--223. https://doi.org/10.1016/j.gaitpost.2016.11.045

[60]

Velko Vechev, Alexandru Dancu, Simon T. Perrault, Quentin Roy, Morten Fjeld, and Shengdong Zhao. 2018. Movespace: On-body Athletic Interaction for Running and Cycling. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces (AVI '18). ACM, New York, NY, USA, 28:1-28:9. https://doi.org/10.1145/3206505.3206527

Digital Library

[61]

Jianbo Wang, Kai Qiu, Houwen Peng, Jianlong Fu, and Jianke Zhu. 2019. AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance. In Proceedings of the 27th ACM International Conference on Multimedia (MM '19). Association for Computing Machinery, Nice, France, 374--382. https://doi.org/10.1145/3343031.3350910

Digital Library

[62]

G. Welch and E. Foxlin. 2002. Motion tracking: no silver bullet, but a respectable arsenal. IEEE Computer Graphics and Applications 22, 6 (Nov. 2002), 24--38. https://doi.org/10.1109/MCG.2002.1046626

Digital Library

[63]

Frank J. Wouda, Matteo Giuberti, Giovanni Bellusci, and Peter H. Veltink. 2016. Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach? Sensors (Basel, Switzerland) 16, 12 (Dec. 2016). https://doi.org/10.3390/s16122138

[64]

Xuesu Xiao and Shuayb Zarar. 2018. Machine Learning for Placement-Insensitive Inertial Motion Capture. In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Brisbane, QLD, 6716--6721. https://doi.org/10.1109/ICRA.2018.8463176

[65]

Xuesu Xiao and Shuayb Zarar. 2018. A Wearable System for Articulated Human Pose Tracking Under Uncertainty of Sensor Placement. In 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). IEEE, Enschede, 1144--1150. https://doi.org/10.1109/BIOROB.2018.8487858

[66]

Che-Chang Yang, Yeh-Liang Hsu, Kao-Shang Shih, and Jun-Ming Lu. 2011. Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System. Sensors (Basel, Switzerland) 11, 8 (July 2011), 7314--7326. https://doi.org/10.3390/s110807314

[67]

Jun Yang. 2009. Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones. In Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics (IMCE '09). ACM, New York, NY, USA, 1--10. https://doi.org/10.1145/1631040.1631042

Digital Library

[68]

Runze Yang, Baoqi Huang, Jian Song, Bing Jia, and Wuyungerile Li. 2018. An Energy Efficient Smartphone Pedometer Based on an Auto-Correlation Analysis. In 2018 IEEE Intl Conf on Parallel Distributed Processing with Applications, Ubiquitous Computing Communications, Big Data Cloud Computing, Social Computing Networking, Sustainable Computing Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). 878--885. https://doi.org/10.1109/BDCloud.2018.00130

[69]

Cheng Zhang, Junrui Yang, Caleb Southern, Thad E. Starner, and Gregory D. Abowd. 2016. WatchOut: Extending Interactions on a Smartwatch with Inertial Sensing. In Proceedings of the 2016 ACM International Symposium on Wearable Computers (ISWC '16). ACM, New York, NY, USA, 136--143. https://doi.org/10.1145/2971763.2971775

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Index Terms

Recognizing Running Movement Changes with Quaternions on a Sports Watch
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
    2. Ubiquitous and mobile devices

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 4

December 2020

1356 pages

EISSN:2474-9567

DOI:10.1145/3444864

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Published: 18 December 2020

Published in IMWUT Volume 4, Issue 4

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