The validity and usability of an eight marker model for avatar-based biofeedback gait training

被引:8
|
作者
Booth, A. T. C. [1 ,2 ]
van der Krogt, M. M. [1 ]
Buizer, A. I. [1 ]
Steenbrink, F. [2 ]
Harlaar, J. [1 ,3 ]
机构
[1] Vrije Univ Amsterdam, Med Ctr, Amsterdam Movement Sci, Dept Rehabil Med,Amsterdam UMC, Amsterdam, Netherlands
[2] Motek Med BV, Dept Clin Applicat & Res, Amsterdam, Netherlands
[3] Delft Univ Technol, Dept Biomech Engn, Delft, Netherlands
基金
欧盟地平线“2020”;
关键词
Rehabilitation; Cerebral palsy; Biofeedback; Walking; Virtual reality; CEREBRAL-PALSY; TREADMILL; CHILDREN; SYSTEM; REHABILITATION; REPEATABILITY; ADULTS;
D O I
10.1016/j.clinbiomech.2019.08.013
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Background: 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. Methods: 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. Findings: Gait outcomes of Human Body Model and eight marker model were comparable, with small differences in gait parameters. The discrepancies between models were <5 degrees, 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)). Interpretation: 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.
引用
收藏
页码:146 / 152
页数:7
相关论文
共 37 条
  • [21] The Effect of Biofeedback-based Balance Training while Performing Cognitive Tasks on Temporal and Spatial Parameters and Gait Stability of the Elderly
    Lee, Kyoung Jin
    Shin, Seung Sub
    Song, Chang Ho
    JOURNAL OF PHYSICAL THERAPY SCIENCE, 2012, 24 (08) : 645 - 649
  • [22] A biofeedback cycling training to improve locomotion: a case series study based on gait pattern classification of 153 chronic stroke patients
    Ferrante, Simona
    Ambrosini, Emilia
    Ravelli, Paola
    Guanziroli, Eleonora
    Molteni, Franco
    Ferrigno, Giancarlo
    Pedrocchi, Alessandra
    JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2011, 8
  • [23] Gait Analysis Based on Fractal Model for Lower Limb Rehabilitation Training Robot
    Chen, Xin
    Sun, Baiqing
    PROCEEDINGS OF 2018 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE RCAR), 2018, : 282 - 287
  • [24] Model analyses of visual biofeedback training for EEG-based brain-computer interface
    Chen, Chih-Wei
    Ju, Ming-Shaung
    Sun, Yun-Nien
    Lin, Chou-Ching K.
    JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2009, 27 (03) : 357 - 368
  • [25] Model analyses of visual biofeedback training for EEG-based brain-computer interface
    Chih-Wei Chen
    Ming-Shaung Ju
    Yun-Nien Sun
    Chou-Ching K. Lin
    Journal of Computational Neuroscience, 2009, 27 : 357 - 368
  • [26] ASSESSING THE POTENTIAL INFLUENCE OF DIFFERENT WALKING STRATEGIES ON PLANTAR PRESSURE DISTRIBUTION TRIGGERED BY A PORTABLE BIOFEEDBACK-BASED GAIT TRAINING DEVICE
    Ying, Ji-Ming
    Chen, Wen-Ming
    Wang, Duo-Jin
    Wang, Ze-Sheng
    JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2020, 20 (10)
  • [27] A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System
    Guo, Junqi
    Yang, Lan
    Umek, Anton
    Bie, Rongfang
    Tomazic, Saso
    Kos, Anton
    SENSORS, 2020, 20 (16) : 1 - 16
  • [28] Gait training efficacy using a home-based practice model in chronic hemiplegia
    Rodriquez, AA
    Black, PO
    Kile, KA
    Sherman, J
    Stellberg, B
    McCormick, J
    Roszkowski, J
    Swiggum, E
    ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION, 1996, 77 (08): : 801 - 805
  • [29] Effect of pelvic tilt exercise using pressure-based visual biofeedback training on the gait parameter in elderly patients with Alzheimer's disease
    Kim, J. -S.
    Kang, M. -H.
    Moon, D. -C.
    Oh, J. -S.
    EUROPEAN GERIATRIC MEDICINE, 2017, 8 (01) : 30 - 36
  • [30] ExerG - an exergame-based training device for the rehabilitation of older adults: a functional model usability study
    Muheim, Jane
    Hotz, Isabella
    Kuebler, Franziska
    Herren, Silvia
    Sollereder, Simon
    Kruszewski, Katharina
    Martin-Niedecken, Anna Lisa
    Schaettin, Alexandra
    Behrendt, Frank
    Boeckler, Sonja
    Schmidlin, Stefan
    Jurt, Roman
    Niedecken, Stephan
    Riederer, Yanick
    Brenneis, Christian
    Bonati, Leo H.
    Seebacher, Barbara
    Schuster-Amft, Corina
    BMC GERIATRICS, 2024, 24 (01)