A Quality of experience assessment of haptic and augmented reality feedback modalities in a gait analysis system

Abstract

Gait analysis is a technique that is used to understand movement patterns and, in some cases, to inform the development of rehabilitation protocols. Traditional rehabilitation approaches have relied on expert guided feedback in clinical settings. Such efforts require the presence of an expert to inform the re-training (to evaluate any improvement) and the patient to travel to the clinic. Nowadays, potential opportunities exist to employ the use of digitized “feedback” modalities to help a user to “understand” improved gait technique. This is important as clear and concise feedback can enhance the quality of rehabilitation and recovery. A critical requirement emerges to consider the quality of feedback from the user perspective i.e. how they process, understand and react to the feedback. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two feedback modalities: Augmented Reality (AR) and Haptic, employed as part of an overall gait analysis system. The aim of the feedback is to reduce varus/valgus misalignments, which can cause serious orthopedics problems. The QoE analysis considers objective (improvement in knee alignment) and subjective (questionnaire responses) user metrics in 26 participants, as part of a within subject design. Participants answered 12 questions on QoE aspects such as utility, usability, interaction and immersion of the feedback modalities via post-test reporting. In addition, objective metrics of participant performance (angles and alignment) were also considered as indicators of the utility of each feedback modality. The findings show statistically significant higher QoE ratings for AR feedback. Also, the number of knee misalignments was reduced after users experienced AR feedback (35% improvement with AR feedback relative to baseline when compared to haptic). Gender analysis showed significant differences in performance for number of misalignments and time to correct valgus misalignment (for males when they experienced AR feedback). The female group self-reported higher utility and QoE ratings for AR when compared to male group.

Item Type:	Article (Published)
Refereed:	Yes
Additional Information:	1. Harris GF, Wertsch JJ. Procedures for gait analysis. Archives of Physical Medicine and Rehabilitation. 1994;75:216–225. 2. Sharma L, Song J, Dunlop D, Felson D, Lewis CE, Segal N. Varus and valgus alignment and incident and progressive knee osteoarthritis. Annals of the rheumatic diseases. 2010;69:1940–1945. 3. Freisinger GM, Hutter EE, Lewis J, Granger JF, Glassman AH, Beal MD. Relationships between varus-valgus laxity of the severely osteoarthritic knee and gait, instability, clinical performance, and function. Journal of orthopaedic research : official publication of the Orthopaedic Research Society. 2017;35:1644–1652. 4. Baudet A, Morisset C, Athis P, Maillefert JF, Casillas JM, Ornetti P. Cross-Talk Correction Method for Knee Kinematics in Gait Analysis Using Principal Component Analysis (PCA): A New Proposal. PLOS ONE. 2014;9:102098–102098. 5. Jafarnezhadgero AA, Majlesi M, Etemadi H, Robertson DGE. Rehabilitation improves walking kinematics in children with a knee varus: Randomized controlled trial. Annals of Physical and Rehabilitation Medicine. 2018;61:125–134. March 7, 2020 16/22 6. Figueiredo LS, Ugrinowitsch H, Freire AB, Shea JB, Benda RN. External Control of Knowledge of Results: Learner Involvement Enhances Motor Skill Transfer. Perceptual and Motor Skills. 2018;125:400–416. 7. Carr K, Zachariah N, Weir P, Mcnevin N. An Examination of Feedback Use in Rehabilitation Settings. Critical Reviews in Physical and Rehabilitation Medicine. 2011;23(1-4):147–160. 8. Ahrens A, Lund KD, Marschall M, Dau T. Sound source localization with varying amount of visual information in virtual reality. PLOS ONE. 2019;14:214603–214603. 9. Hartanto D, Kampmann IL, Morina N, Emmelkamp PGM, Neerincx MA, Brinkman WP. Controlling Social Stress in Virtual Reality Environments. PLOS ONE. 2014;9:92804–92804. 10. Stepp CE, An Q, Matsuoka Y. Repeated Training with Augmentative Vibrotactile Feedback Increases Object Manipulation Performance. PLOS ONE. 2012;7:32743–32743. 11. Lurie KL, Shull PB, Nesbitt KF, Cutkosky MR. Informing haptic feedback design for gait retraining. 2011 IEEE World Haptics Conference. 2011; p. 19–24. 12. Shull PB, Jirattigalachote W, Zhu X. An Overview of Wearable Sensing and Wearable Feedback for Gait Retraining. In: ICIRA; 2013. 13. Vu TM, Katushin N, Pumwa J. Motion tracking glove for augmented reality and virtual reality. Paladyn. 2019;10:160–160. 14. Cipresso P, Giglioli IAC, Raya MA, Riva G. The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature. In: Front. Psychol.; 2018. 15. Brunnstrm K, Moor KD, Dooms A, Egger-Lampl S, Garcia MN, Hossfeld T. Qualinet White Paper on Definitions of Quality of Experience; 2013. 16. Robitza W, Ahmad A, Kara PA, Atzori L, Martini MG, Raake A. Challenges of future multimedia QoE monitoring for internet service providers. Multimedia Tools and Applications. 2017;76:22243–22266. 17. Murray N, Lee B, Qiao Y, Miro-Muntean G. The Impact of Scent Type on Olfaction-Enhanced Multimedia Quality of Experience. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017;47:2503–2515. 18. Floris A, Atzori L. Quality of Experience in the Multimedia Internet of Things: Definition and practical use-cases. 2015 IEEE International Conference on Communication Workshop (ICCW). 2015; p. 1747–1752. 19. Martini M, Chen CW, Chen Z, Dagiuklas T, Sun L, Zhu X. QoE-Aware Wireless Multimedia Systems; 2012. 20. Linden JV, Schoonderwaldt E, Bird J. Towards a real-time system for teaching novices correct violin bowing technique. 2009 IEEE International Workshop on Haptic Audio visual Environments and Games. 2009; p. 81–86. 21. Lieberman J, Breazeal C. TIKL: Development of a Wearable Vibrotactile Feedback Suit for Improved Human Motor Learning. IEEE Transactions on Robotics. 2007;23:919–926. March 7, 2020 17/22 22. Xu J, Lee UH, Bao T, Huang Y, Sienko KH, Shull PB. Wearable sensing and haptic feedback research platform for gait retraining. 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks. 2017; p. 125–128. 23. Gaffary Y, Gouis BL, Marchal M, Argelaguet F, Arnaldi B, Lcuyer A. AR Feels Softer than VR: Haptic Perception of Stiffness in Augmented versus Virtual Reality. IEEE Transactions on Visualization and Computer Graphics. 2017;23:2372–2377. 24. Koritnik T, Koenig A, Bajd T, Riener R, Munih M. Comparison of visual and haptic feedback during training of lower extremities. Gait Posture. 2010;32(4):540–546. 25. Kauhanen L, Palomki T, Jylnki P, Aloise F, Nuttin M, Millan JDR. Haptic Feedback Compared with Visual Feedback for BCI. Infoscience EPFL scientific publications. 2006;. 26. Sigrist R, Rauter G, Riener R, Wolf P. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychonomic Bulletin & Review. 2013;20:21–53. 27. Turchet L, Burelli P, Serafin S. Haptic Feedback for Enhancing Realism of Walking Simulations. IEEE Transactions on Haptics. 2013;6:35–45. 28. Shull P, Lurie K, Shin M, Besier T, Cutkosky M. Haptic gait retraining for knee osteoarthritis treatment. 2010 IEEE Haptics Symposium. 2010; p. 409–416. 29. Nagymt G, Kiss RM. Affordable gait analysis using augmented reality markers. PLOS ONE. 2019;14:212319–212319. 30. Diaz GJ, Parade MS, Barton SL, Fajen BR. The pickup of visual information about size and location during approach to an obstacle. PLOS ONE. 2018;13:192044–192044. 31. Matthis J, Barton S, Fajen B. The critical phase for visual control of human walking over complex terrain. Proceedings of the National Academy of Sciences;114:7–24. 32. Binaee K, Diaz GJ. Assessment of an augmented reality apparatus for the study of visually guided walking and obstacle crossing. Behavior Research Methods. 2019;51:523–531. 33. Kothari R, Binaee K, Matthis JS, Bailey R, Diaz GJ. Novel apparatus for investigation of eye movements when walking in the presence of 3D projected obstacles. In: presented at the Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications; 2016. 34. Sekhavat YA, Namani MS. Projection-Based AR: Effective Visual Feedback in Gait Rehabilitation. IEEE Transactions on Human-Machine Systems. 2018;48:626–636. 35. Lee CH, Kim Y, Lee BH. Augmented reality-based postural control training improves gait function in patients with stroke: Randomized controlled trial. Hong Kong Physiotherapy Journal. 2014;32:51–57. March 7, 2020 18/22 36. Bennour S, Ulrich B, Legrand T, Jolles B, Favre J. A gait retraining system using augmented-reality to modify footprint parameters: Effects on lower-limb sagittal-plane kinematics. Journal of Biomechanics. 2017;66(26-35). 37. Keighrey C, Flynn R, Murray S, Murray N. A QoE evaluation of immersive augmented and virtual reality speech & language assessment applications. 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX. 2017; p. 1–6. 38. Omelina L, Bonnechre B, Jan SVS, Jansen B. Analyzing the quality of experience of computer games in rehabilitation: the therapist’s perspective. In: and others, editor. REHAB ’16 Proceedings of the 4th Workshop on ICTs for improving Patients Rehabilitation Research Techniques, 13-14, Lisbon. ACM; 2016. 39. Haines T, Mcbride J, Triplett N, Skinner J, Fairbrother K, Kirby TJ. A comparison of men’s and women’s strength to body mass ratio and varus/valgus knee angle during jump landings. Journal of sports sciences. 2011;29:1435–1477. 40. Schmitz RJ, Ficklin TK, Shimokochi Y, Nguyen AD, Beynnon BD, Perrin DH. Varus/valgus and internal/external torsional knee joint stiffness differs between sexes. The American journal of sports medicine. 2008;36:1380–1388. 41. Russell KA, Palmieri RM, Zinder SM, Ingersoll CD. Sex differences in valgus knee angle during a single-leg drop jump. Journal of athletic training. 2006;41:166–171. 42. Quatman CE, Hewett TE. The anterior cruciate ligament injury controversy: is "valgus collapse" a sex-specific mechanism? British journal of sports medicine. 2009;43:328–335. 43. Technology XDMT. MTw Awinda - Wireless Motion Tracker; 2019. Available from: https://www.xsens.com/products/mtw-awinda/. 44. Rodrigues T, Cathain C, Devine D, Moran K, O’Connor NE, Murray N. An Evaluation of a 3D Multimodal Marker-less Motion Analysis System. In: ACM Multimedia Systems Conference; 2019. 45. Rodrigues TB, Salgado DP, Catháin CO, Connor N, Murray N. Human gait assessment using a 3D marker-less multimodal motion capture system. Multimedia Tools and Applications. 2019;. 46. Hu V, Charry E, Umer M, Ronchi A, Taylor S. An inertial sensor system for measurements of tibia angle with applications to knee valgus/varus detection. In: and others, editor. 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 21-24, Singapore. IEEE; 2014. 47. Vargas-Valencia LS, Elias A, Rocon E, Bastos-Filho T, Frizera A. An IMU-to-Body Alignment Method Applied to Human Gait Analysis. Sensors. 2016;16:2090–2090. 48. Sebastian M, OH, Andrew H, JL, Ravi V. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics; 2011. 49. Mcdaniel T, Krishna S, Villanueva D, Panchanathan S. A haptic belt for vibrotactile communication. 2010 IEEE International Symposium on Haptic Audio Visual Environments and Games. 2010; p. 1–2. March 7, 2020 19/22 50. Rahman MF, Patterson D, Cheok A, Betz R. 30 - Motor Drives. Power Electronics Handbook. 2018; p. 945–1021. 51. Corp ©SE. MOVERIO BT-30C - Smart Glasses; 2019. Available from: https://moverio.epson.com/. 52. Bhatia V, Joshi S, Chapaneri R. Websocket-Evented Real-Time Online Coding Collaborator. In: Smart Intelligent Computing and Applications; 2019. p. 325–334. 53. Ilahi O, Kadakia N, Huo M. Inter- and intraobserver variability of radiographic measurements of knee alignment. The American journal of knee surgery. 2001;14:238–280. 54. Itu-T. Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU2016;. 55. Egan D, Brennan S, Barrett J, Qiao Y, Timmerer C, Murray N. An evaluation of Heart Rate and ElectroDermal Activity as an objective QoE evaluation method for immersive virtual reality environments. 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX). 2016; p. 1–6. 56. Gregori N, Feuer W, Rosenfeld P. Novel method for analyzing snellen visual acuity measurements. Retina, The Journal of Retinal and Vitreous Diseases. 2010;30(7):1046–1050. 57. Pickford RW. The Ishihara Test for Colour Blindness. Nature. 1944;153:656–657. 58. Bell-Krotoski JA, Fess EE, Figarola JH, Hiltz D. Threshold Detection and Semmes-Weinstein Monofilaments. Journal of Hand Therapy;8:155–162. 59. Rodrigues T. QoE Questionnaire of Gait Feedback System; 2019. Available from: http://bit.ly/QoEGaitFeedback. 60. Laghari AA, Hui H, Shafiq M, Khan A. Assessing effect of Cloud distance on end user’s Quality of Experience (QoE). 2016 2nd IEEE International Conference on Computer and Communications (ICCC). 2016; p. 500–505. 61. Bangor A, Kortum PT, Miller JT. An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction. 2008;24:7–7. 62. Schubert T, Friedmann F, Regenbrecht H. The Experience of Presence: Factor Analytic Insights. Presence: Teleoperators and Virtual Environments. 2001;10:266–281. 63. Lewis J, R J. IBM Computer Usability Satisfaction Questionnaires: Psychometric Evaluation and Instructions for Use. International Journal of Human-Computer Interaction. 1993;7(1):57–78. 64. Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth. 2019;22(1):67–72. 65. Field A; 2013. Discovering Statistics using IBM SPSS Statistics (4th. ed.). Sage Publications Ltd.
Uncontrolled Keywords:	Quality of Experience; Gait Analysis; Augmented Reality Feedback; Haptic Feedback; Inertial Sensors; Objective Evaluation; Subjective Evaluation
Subjects:	Computer Science > Interactive computer systems
DCU Faculties and Centres:	DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:	Public Library of Science
Official URL:	http://dx.doi.org/10.1371/journal.pone.0230570
Copyright Information:	© 2020 The Authors Open Access
Use License:	This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:	Irish Research Council under grant GOIPG/2017/803, Science Foundation Ireland grant number SFI/12/RC/2289_P2 a, Science Foundation Ireland grant number SFI/13/RC/2106
ID Code:	24268
Deposited On:	15 Apr 2020 14:58 by Noel Edward O'connor . Last Modified 15 Apr 2020 14:58

Item Type:

Article (Published)

Refereed:

Yes

Additional Information:

1. Harris GF, Wertsch JJ. Procedures for gait analysis. Archives of Physical Medicine and Rehabilitation. 1994;75:216–225. 2. Sharma L, Song J, Dunlop D, Felson D, Lewis CE, Segal N. Varus and valgus alignment and incident and progressive knee osteoarthritis. Annals of the rheumatic diseases. 2010;69:1940–1945. 3. Freisinger GM, Hutter EE, Lewis J, Granger JF, Glassman AH, Beal MD. Relationships between varus-valgus laxity of the severely osteoarthritic knee and gait, instability, clinical performance, and function. Journal of orthopaedic research : official publication of the Orthopaedic Research Society. 2017;35:1644–1652. 4. Baudet A, Morisset C, Athis P, Maillefert JF, Casillas JM, Ornetti P. Cross-Talk Correction Method for Knee Kinematics in Gait Analysis Using Principal Component Analysis (PCA): A New Proposal. PLOS ONE. 2014;9:102098–102098. 5. Jafarnezhadgero AA, Majlesi M, Etemadi H, Robertson DGE. Rehabilitation improves walking kinematics in children with a knee varus: Randomized controlled trial. Annals of Physical and Rehabilitation Medicine. 2018;61:125–134. March 7, 2020 16/22 6. Figueiredo LS, Ugrinowitsch H, Freire AB, Shea JB, Benda RN. External Control of Knowledge of Results: Learner Involvement Enhances Motor Skill Transfer. Perceptual and Motor Skills. 2018;125:400–416. 7. Carr K, Zachariah N, Weir P, Mcnevin N. An Examination of Feedback Use in Rehabilitation Settings. Critical Reviews in Physical and Rehabilitation Medicine. 2011;23(1-4):147–160. 8. Ahrens A, Lund KD, Marschall M, Dau T. Sound source localization with varying amount of visual information in virtual reality. PLOS ONE. 2019;14:214603–214603. 9. Hartanto D, Kampmann IL, Morina N, Emmelkamp PGM, Neerincx MA, Brinkman WP. Controlling Social Stress in Virtual Reality Environments. PLOS ONE. 2014;9:92804–92804. 10. Stepp CE, An Q, Matsuoka Y. Repeated Training with Augmentative Vibrotactile Feedback Increases Object Manipulation Performance. PLOS ONE. 2012;7:32743–32743. 11. Lurie KL, Shull PB, Nesbitt KF, Cutkosky MR. Informing haptic feedback design for gait retraining. 2011 IEEE World Haptics Conference. 2011; p. 19–24. 12. Shull PB, Jirattigalachote W, Zhu X. An Overview of Wearable Sensing and Wearable Feedback for Gait Retraining. In: ICIRA; 2013. 13. Vu TM, Katushin N, Pumwa J. Motion tracking glove for augmented reality and virtual reality. Paladyn. 2019;10:160–160. 14. Cipresso P, Giglioli IAC, Raya MA, Riva G. The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature. In: Front. Psychol.; 2018. 15. Brunnstrm K, Moor KD, Dooms A, Egger-Lampl S, Garcia MN, Hossfeld T. Qualinet White Paper on Definitions of Quality of Experience; 2013. 16. Robitza W, Ahmad A, Kara PA, Atzori L, Martini MG, Raake A. Challenges of future multimedia QoE monitoring for internet service providers. Multimedia Tools and Applications. 2017;76:22243–22266. 17. Murray N, Lee B, Qiao Y, Miro-Muntean G. The Impact of Scent Type on Olfaction-Enhanced Multimedia Quality of Experience. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017;47:2503–2515. 18. Floris A, Atzori L. Quality of Experience in the Multimedia Internet of Things: Definition and practical use-cases. 2015 IEEE International Conference on Communication Workshop (ICCW). 2015; p. 1747–1752. 19. Martini M, Chen CW, Chen Z, Dagiuklas T, Sun L, Zhu X. QoE-Aware Wireless Multimedia Systems; 2012. 20. Linden JV, Schoonderwaldt E, Bird J. Towards a real-time system for teaching novices correct violin bowing technique. 2009 IEEE International Workshop on Haptic Audio visual Environments and Games. 2009; p. 81–86. 21. Lieberman J, Breazeal C. TIKL: Development of a Wearable Vibrotactile Feedback Suit for Improved Human Motor Learning. IEEE Transactions on Robotics. 2007;23:919–926. March 7, 2020 17/22 22. Xu J, Lee UH, Bao T, Huang Y, Sienko KH, Shull PB. Wearable sensing and haptic feedback research platform for gait retraining. 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks. 2017; p. 125–128. 23. Gaffary Y, Gouis BL, Marchal M, Argelaguet F, Arnaldi B, Lcuyer A. AR Feels Softer than VR: Haptic Perception of Stiffness in Augmented versus Virtual Reality. IEEE Transactions on Visualization and Computer Graphics. 2017;23:2372–2377. 24. Koritnik T, Koenig A, Bajd T, Riener R, Munih M. Comparison of visual and haptic feedback during training of lower extremities. Gait Posture. 2010;32(4):540–546. 25. Kauhanen L, Palomki T, Jylnki P, Aloise F, Nuttin M, Millan JDR. Haptic Feedback Compared with Visual Feedback for BCI. Infoscience EPFL scientific publications. 2006;. 26. Sigrist R, Rauter G, Riener R, Wolf P. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychonomic Bulletin & Review. 2013;20:21–53. 27. Turchet L, Burelli P, Serafin S. Haptic Feedback for Enhancing Realism of Walking Simulations. IEEE Transactions on Haptics. 2013;6:35–45. 28. Shull P, Lurie K, Shin M, Besier T, Cutkosky M. Haptic gait retraining for knee osteoarthritis treatment. 2010 IEEE Haptics Symposium. 2010; p. 409–416. 29. Nagymt G, Kiss RM. Affordable gait analysis using augmented reality markers. PLOS ONE. 2019;14:212319–212319. 30. Diaz GJ, Parade MS, Barton SL, Fajen BR. The pickup of visual information about size and location during approach to an obstacle. PLOS ONE. 2018;13:192044–192044. 31. Matthis J, Barton S, Fajen B. The critical phase for visual control of human walking over complex terrain. Proceedings of the National Academy of Sciences;114:7–24. 32. Binaee K, Diaz GJ. Assessment of an augmented reality apparatus for the study of visually guided walking and obstacle crossing. Behavior Research Methods. 2019;51:523–531. 33. Kothari R, Binaee K, Matthis JS, Bailey R, Diaz GJ. Novel apparatus for investigation of eye movements when walking in the presence of 3D projected obstacles. In: presented at the Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications; 2016. 34. Sekhavat YA, Namani MS. Projection-Based AR: Effective Visual Feedback in Gait Rehabilitation. IEEE Transactions on Human-Machine Systems. 2018;48:626–636. 35. Lee CH, Kim Y, Lee BH. Augmented reality-based postural control training improves gait function in patients with stroke: Randomized controlled trial. Hong Kong Physiotherapy Journal. 2014;32:51–57. March 7, 2020 18/22 36. Bennour S, Ulrich B, Legrand T, Jolles B, Favre J. A gait retraining system using augmented-reality to modify footprint parameters: Effects on lower-limb sagittal-plane kinematics. Journal of Biomechanics. 2017;66(26-35). 37. Keighrey C, Flynn R, Murray S, Murray N. A QoE evaluation of immersive augmented and virtual reality speech & language assessment applications. 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX. 2017; p. 1–6. 38. Omelina L, Bonnechre B, Jan SVS, Jansen B. Analyzing the quality of experience of computer games in rehabilitation: the therapist’s perspective. In: and others, editor. REHAB ’16 Proceedings of the 4th Workshop on ICTs for improving Patients Rehabilitation Research Techniques, 13-14, Lisbon. ACM; 2016. 39. Haines T, Mcbride J, Triplett N, Skinner J, Fairbrother K, Kirby TJ. A comparison of men’s and women’s strength to body mass ratio and varus/valgus knee angle during jump landings. Journal of sports sciences. 2011;29:1435–1477. 40. Schmitz RJ, Ficklin TK, Shimokochi Y, Nguyen AD, Beynnon BD, Perrin DH. Varus/valgus and internal/external torsional knee joint stiffness differs between sexes. The American journal of sports medicine. 2008;36:1380–1388. 41. Russell KA, Palmieri RM, Zinder SM, Ingersoll CD. Sex differences in valgus knee angle during a single-leg drop jump. Journal of athletic training. 2006;41:166–171. 42. Quatman CE, Hewett TE. The anterior cruciate ligament injury controversy: is "valgus collapse" a sex-specific mechanism? British journal of sports medicine. 2009;43:328–335. 43. Technology XDMT. MTw Awinda - Wireless Motion Tracker; 2019. Available from: https://www.xsens.com/products/mtw-awinda/. 44. Rodrigues T, Cathain C, Devine D, Moran K, O’Connor NE, Murray N. An Evaluation of a 3D Multimodal Marker-less Motion Analysis System. In: ACM Multimedia Systems Conference; 2019. 45. Rodrigues TB, Salgado DP, Catháin CO, Connor N, Murray N. Human gait assessment using a 3D marker-less multimodal motion capture system. Multimedia Tools and Applications. 2019;. 46. Hu V, Charry E, Umer M, Ronchi A, Taylor S. An inertial sensor system for measurements of tibia angle with applications to knee valgus/varus detection. In: and others, editor. 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 21-24, Singapore. IEEE; 2014. 47. Vargas-Valencia LS, Elias A, Rocon E, Bastos-Filho T, Frizera A. An IMU-to-Body Alignment Method Applied to Human Gait Analysis. Sensors. 2016;16:2090–2090. 48. Sebastian M, OH, Andrew H, JL, Ravi V. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics; 2011. 49. Mcdaniel T, Krishna S, Villanueva D, Panchanathan S. A haptic belt for vibrotactile communication. 2010 IEEE International Symposium on Haptic Audio Visual Environments and Games. 2010; p. 1–2. March 7, 2020 19/22 50. Rahman MF, Patterson D, Cheok A, Betz R. 30 - Motor Drives. Power Electronics Handbook. 2018; p. 945–1021. 51. Corp ©SE. MOVERIO BT-30C - Smart Glasses; 2019. Available from: https://moverio.epson.com/. 52. Bhatia V, Joshi S, Chapaneri R. Websocket-Evented Real-Time Online Coding Collaborator. In: Smart Intelligent Computing and Applications; 2019. p. 325–334. 53. Ilahi O, Kadakia N, Huo M. Inter- and intraobserver variability of radiographic measurements of knee alignment. The American journal of knee surgery. 2001;14:238–280. 54. Itu-T. Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU2016;. 55. Egan D, Brennan S, Barrett J, Qiao Y, Timmerer C, Murray N. An evaluation of Heart Rate and ElectroDermal Activity as an objective QoE evaluation method for immersive virtual reality environments. 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX). 2016; p. 1–6. 56. Gregori N, Feuer W, Rosenfeld P. Novel method for analyzing snellen visual acuity measurements. Retina, The Journal of Retinal and Vitreous Diseases. 2010;30(7):1046–1050. 57. Pickford RW. The Ishihara Test for Colour Blindness. Nature. 1944;153:656–657. 58. Bell-Krotoski JA, Fess EE, Figarola JH, Hiltz D. Threshold Detection and Semmes-Weinstein Monofilaments. Journal of Hand Therapy;8:155–162. 59. Rodrigues T. QoE Questionnaire of Gait Feedback System; 2019. Available from: http://bit.ly/QoEGaitFeedback. 60. Laghari AA, Hui H, Shafiq M, Khan A. Assessing effect of Cloud distance on end user’s Quality of Experience (QoE). 2016 2nd IEEE International Conference on Computer and Communications (ICCC). 2016; p. 500–505. 61. Bangor A, Kortum PT, Miller JT. An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction. 2008;24:7–7. 62. Schubert T, Friedmann F, Regenbrecht H. The Experience of Presence: Factor Analytic Insights. Presence: Teleoperators and Virtual Environments. 2001;10:266–281. 63. Lewis J, R J. IBM Computer Usability Satisfaction Questionnaires: Psychometric Evaluation and Instructions for Use. International Journal of Human-Computer Interaction. 1993;7(1):57–78. 64. Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth. 2019;22(1):67–72. 65. Field A; 2013. Discovering Statistics using IBM SPSS Statistics (4th. ed.). Sage Publications Ltd.

Uncontrolled Keywords:

Quality of Experience; Gait Analysis; Augmented Reality Feedback; Haptic Feedback; Inertial Sensors; Objective Evaluation; Subjective Evaluation

Subjects:

Computer Science > Interactive computer systems

DCU Faculties and Centres:

DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics

Publisher:

Public Library of Science

Official URL:

http://dx.doi.org/10.1371/journal.pone.0230570

Use License:

This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License

Funders:

Irish Research Council under grant GOIPG/2017/803, Science Foundation Ireland grant number SFI/12/RC/2289_P2 a, Science Foundation Ireland grant number SFI/13/RC/2106

ID Code:

24268

Deposited On:

15 Apr 2020 14:58 by Noel Edward O'connor . Last Modified 15 Apr 2020 14:58

DORAS | DCU Research Repository

A Quality of experience assessment of haptic and augmented reality feedback modalities in a gait analysis system

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