- •An application for biofeedback gait training was developed for an integrated treadmill.
- •With 8 passive markers, simple gait biomechanics can be calculated in real-time.
- •Gait can be visualised and challenged using an avatar in virtual reality.
- •The application is clinically usable and may benefit patient populations undergoing gait training.
Virtual reality presents a platform for therapeutic gaming, and incorporation of immersive biofeedback on gait may enhance outcomes in rehabilitation. Time is limited in therapeutic practice, therefore any potential gait training tool requires a short set up time, while maintaining clinical relevance and accuracy. The aim of this study was to develop, validate, and establish the usability of an avatar-based application for biofeedback-enhanced gait training with minimal set up time.
A simplified, eight marker model was developed using eight passive markers placed on anatomical landmarks. This allowed for visualisation of avatar-based biofeedback on pelvis kinematics, hip and knee sagittal angles in real-time. Retrospective gait analysis data from typically developing children (n = 41) and children with cerebral palsy (n = 25), were used to validate eight marker model. Gait outcomes were compared to the Human Body Model using statistical parametric mapping. Usability for use in clinical practice was tested in five clinical rehabilitation centers with the system usability score.
Gait outcomes of Human Body Model and eight marker model were comparable, with small differences in gait parameters. The discrepancies between models were <5°, except for knee extension where eight marker model showed significantly less knee extension, especially towards full extension. The application was considered of ‘high marginal acceptability’ (system usability score, mean 68 (SD 13)).
Gait biofeedback can be achieved, to acceptable accuracy for within-session gait training, using an eight marker model. The application may be considered usable and implemented for use in patient populations undergoing gait training.
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Clinical Biomechanics
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- Intra-rater repeatability of gait parameters in healthy adults during self-paced treadmill-based virtual reality walking.Comput. Methods Biomech. Biomed. Engin. 2017; 20: 1669-1677https://doi.org/10.1080/10255842.2017.1404994
- An empirical evaluation of the system usability scale.Int. J. Hum. Comput. Interact. 2008; 24: 574-594https://doi.org/10.1080/10447310802205776
- The efficacy of functional gait training in children and young adults with cerebral palsy: a systematic review and meta-analysis.Dev. Med. Child Neurol. 2018; 60: 866-883https://doi.org/10.1111/dmcn.13708
- Immediate effects of immersive biofeedback on gait in children with cerebral palsy.Arch. Phys. Med. Rehabil. 2019; 100: 598-605https://doi.org/10.1016/j.apmr.2018.10.013
- SUS: a quick and dirty usability scale.Usability Eval. Ind. 1995; 189
- The effect of treadmill training on gross motor function and walking speed in ambulatory adolescents with cerebral palsy.Am. J. Phys. Med. Rehabil. 2012; 91: 747-760https://doi.org/10.1097/PHM.0b013e3182643eba
- A systematic review of the effectiveness of treadmill training and body weight support in pediatric rehabilitation.J. Neurol. Phys. Ther. 2009; 33: 27-44https://doi.org/10.1097/NPT.0b013e31819800e2
- Rehabilitation after stroke.N. Engl. J. Med. 2005; 352: 1677-1684https://doi.org/10.1056/NEJMcp043511
- OpenSim versus human body model: a comparison study for the lower limbs during gait.J. Appl. Biomech. 2018; : 1-47https://doi.org/10.1123/jab.2017-0156
- Immersive virtual reality to improve walking abilities in cerebral palsy: a pilot study.Ann. Biomed. Eng. 2018; 46: 1376-1384https://doi.org/10.1007/s10439-018-2039-1
- The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units.ˇ arevic. 2017; 55: 27-33https://doi.org/10.1016/j.jbiomech.2017.02.016
- Biofeedback in rehabilitation.J. Neuroeng. Rehabil. 2013; 10: 60https://doi.org/10.1186/1743-0003-10-60
- USEQ: a short questionnaire for satisfaction evaluation of virtual rehabilitation systems.Sensors. 2017; 17: 1589https://doi.org/10.3390/s17071589
- A joint coordinate system for the clinical description of three-dimensional motions: application to the knee.J. Biomech. Eng. 1983; 105: 136https://doi.org/10.1115/1.3138397
- Pedometer-based gait training in children with spastic hemiparetic cerebral palsy: a randomized controlled study.Clin. Rehabil. 2011; 25: 157-165https://doi.org/10.1177/0269215510382147
- Prediction of the hip joint centre in adults, children, and patients with cerebral palsy based on magnetic resonance imaging.J. Biomech. 2007; 40: 595-602https://doi.org/10.1016/j.jbiomech.2006.02.003
- Six weeks of intensive treadmill training improves gait and quality of life in patients with Parkinson’s disease: a pilot study.Arch. Phys. Med. Rehabil. 2007; 88: 1154-1158https://doi.org/10.1016/j.apmr.2007.05.015
- Validation of wearable visual feedback for retraining foot progression angle using inertial sensors and an augmented reality headset.J. Neuroeng. Rehabil. 2018; 15: 78https://doi.org/10.1186/s12984-018-0419-2
- A new anatomically based protocol for gait analysis in children.Gait Posture. 2007; 26: 560-571https://doi.org/10.1016/j.gaitpost.2006.12.018
- The effectiveness of robotic-assisted gait training for paediatric gait disorders: systematic review.J. Neuroeng. Rehabil. 2017; 14https://doi.org/10.1186/s12984-016-0214-x
- Spatiotemporal gait characteristics in patients with COPD during the Gait Real-time Analysis Interactive Lab-based 6-minute walk test.PLoS One. 2017; 12e0190099https://doi.org/10.1371/journal.pone.0190099
- Developing visualisation software for rehabilitation: investigating the requirements of patients, therapists and the rehabilitation process.Health Informatics J. 2012; 18: 171-180https://doi.org/10.1177/1460458212443901
- Biofeedback interventions for individuals with cerebral palsy: a systematic review.Disabil. Rehabil. 2018; 6: 1-23https://doi.org/10.1080/09638288.2018.1468933
- The reliability of three-dimensional kinematic gait measurements: a systematic review.Gait Posture. 2009; 29: 360-369https://doi.org/10.1016/j.gaitpost.2008.09.003
- Treadmill training and body weight support for walking after stroke.in: Cochrane Database of Systematic Reviews. 2014: CD002840https://doi.org/10.1002/14651858.CD002840.pub3
- Statistical parametric mapping to identify differences between consensus-based joint patterns during gait in children with cerebral palsy.PLoS One. 2017; 12: 1-22https://doi.org/10.1371/journal.pone.0169834
- How normal is normal: consequences of stride to stride variability, treadmill walking and age when using normative paediatric gait data.Gait Posture. 2019; 70: 289-297
- Generalized n -dimensional biomechanical field analysis using statistical parametric mapping.J. Biomech. 2010; 43: 1976-1982https://doi.org/10.1016/j.jbiomech.2010.03.008
- Effect of real-time biofeedback on peak knee adduction moment in patients with medial knee osteoarthritis: is direct feedback effective?.Clin. Biomech. 2017; : 0-1https://doi.org/10.1016/j.clinbiomech.2017.07.004
- The learning process of gait retraining using real-time feedback in patients with medial knee osteoarthritis.Gait Posture. 2018; 62: 1-6https://doi.org/10.1016/j.gaitpost.2018.02.023
- Robotic-assisted gait training in neurological patients: who may benefit?.Ann. Biomed. Eng. 2015; 43: 1260-1269https://doi.org/10.1007/s10439-015-1283-x
- Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review.Psychon. Bull. Rev. 2013; 20: 21-53https://doi.org/10.3758/s13423-012-0333-8
- Getting the best out of advanced rehabilitation technology for the lower limbs: minding motor learning principles.PM&R. 2018; 10: S165-S173https://doi.org/10.1016/j.pmrj.2018.06.007
- Prevalence of gait disorders in hospitalized neurological patients.Mov. Disord. 2005; 20: 89-94https://doi.org/10.1002/mds.20266
- Motor rehabilitation using virtual reality.J. Neuroeng. Rehabil. 2004; 1: 1-8https://doi.org/10.1186/1743-0003-1-10
- Augmented visual feedback of movement performance to enhance walking recovery after stroke: study protocol for a pilot randomised controlled trial.Trials. 2012; 13: 163https://doi.org/10.1186/1745-6215-13-163
- A real-time system for biomechanical analysis of human movement and muscle function.Med. Biol. Eng. Comput. 2013; 51: 1069-1077https://doi.org/10.1007/s11517-013-1076-z
- Real-time feedback to improve gait in children with cerebral palsy.Gait Posture. 2017; 52: 76-82https://doi.org/10.1016/j.gaitpost.2016.11.021
- Validity and repeatability of inertial measurement units for measuring gait parameters.Gait Posture. 2017; 55: 87-93https://doi.org/10.1016/j.gaitpost.2017.04.013
- ISB recommendations for standardization in the reporting of kinematic data.J. Biomech. 1995; 28: 1257-1261
- Virtual reality-based training improves community ambulation in individuals with stroke: a randomized controlled trial.Gait Posture. 2008; 28: 201-206https://doi.org/10.1016/j.gaitpost.2007.11.007
- Two simple methods for determining gait events during treadmill and overground walking using kinematic data.Gait Posture. 2008; 27: 710-714https://doi.org/10.1016/j.gaitpost.2007.07.007
Published online: August 24, 2019
Accepted: August 21, 2019
Received: January 11, 2019
© 2019 Elsevier Ltd. All rights reserved.