Literature DB >> 29399378

Reliability of a Feedback-Controlled Treadmill Algorithm Dependent on the User's Behavior.

Casey Wiens1, Will Denton1, Molly Schieber1, Ryan Hartley1, Vivien Marmelat1, Sara Myers1, Jennifer Yentes1.   

Abstract

The reliability of the treadmill belt speed using a feedback-controlled treadmill algorithm was analyzed in this study. Using biomechanical factors of the participant's walking behavior, an estimated walking speed was calculated and used to adjust the speed of the treadmill. Our proposed algorithm expands on the current hypotheses of feedback-controlled treadmill algorithms and is presented below. Nine healthy, young adults walked on a treadmill controlled by the algorithm for three trials over two days. Each participant walked on the feedback-controlled treadmill for one 16-minute and one five-minute trial during day one and one 16-minute trial during day two. Mean, standard deviation, interclass correlation coefficient (ICC), and standard error of measurement (SEM) were analyzed on the treadmill belt speed mean, standard deviation, and coefficient of variation. There were significantly high ICC for mean treadmill speed within- and between-days. Treadmill speed standard deviation and coefficient of variation were significantly reliable within-day. These results suggest the algorithm will reliably produce the same treadmill belt speed mean, but may only produce a similar treadmill belt speed standard deviation and coefficient of variation if the trials are performed in the same day. A feedback-controlled treadmill algorithm that accounts for the user's behavior provides a greater level of control and minimizes any possible constraints of walking on a conventional treadmill.

Entities:  

Year:  2017        PMID: 29399378      PMCID: PMC5790169          DOI: 10.1109/EIT.2017.8053423

Source DB:  PubMed          Journal:  IEEE Int Conf Electro Inf Technol        ISSN: 2154-0357


  11 in total

1.  A feedback-controlled treadmill (treadmill-on-demand) and the spontaneous speed of walking and running in humans.

Authors:  Alberto E Minetti; Lorenzo Boldrini; Laura Brusamolin; Paola Zamparo; Tom McKee
Journal:  J Appl Physiol (1985)       Date:  2003-04-11

Review 2.  Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking.

Authors:  Jeffrey M Hausdorff
Journal:  Hum Mov Sci       Date:  2007-07-05       Impact factor: 2.161

3.  A Feedback-Controlled Interface for Treadmill Locomotion in Virtual Environments.

Authors:  Lee Lichtenstein; James Barabas; Russell L Woods; Eli Peli
Journal:  ACM Trans Appl Percept       Date:  2007-01       Impact factor: 1.550

4.  The consistency of maximum running speed measurements in humans using a feedback-controlled treadmill, and a comparison with maximum attainable speed during overground locomotion.

Authors:  Mark V Bowtell; Huiling Tan; Alan M Wilson
Journal:  J Biomech       Date:  2009-08-15       Impact factor: 2.712

5.  Reliability of the walking speed and gait dynamics variables while walking on a feedback-controlled treadmill.

Authors:  Jin-Seung Choi; Dong-Won Kang; Jeong-Woo Seo; Gye-Rae Tack
Journal:  J Biomech       Date:  2015-03-11       Impact factor: 2.712

6.  Self-paced versus fixed speed treadmill walking.

Authors:  L H Sloot; M M van der Krogt; J Harlaar
Journal:  Gait Posture       Date:  2013-08-31       Impact factor: 2.840

7.  The integrated virtual environment rehabilitation treadmill system.

Authors:  Jeff Feasel; Mary C Whitton; Laura Kassler; Frederick P Brooks; Michael D Lewek
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-06       Impact factor: 3.802

8.  A novel method for automatic treadmill speed adaptation.

Authors:  Joachim von Zitzewitz; Michael Bernhardt; Robert Riener
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2007-09       Impact factor: 3.802

9.  A user-driven treadmill control scheme for simulating overground locomotion.

Authors:  Jonghyun Kim; Christopher J Stanley; Lindsey A Curatalo; Hyung-Soon Park
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

10.  A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation.

Authors:  Jungwon Yoon; Hyung-Soon Park; Diane Louise Damiano
Journal:  J Neuroeng Rehabil       Date:  2012-08-28       Impact factor: 4.262

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  6 in total

1.  On the application of entropic half-life and statistical persistence decay for quantification of time dependency in human gait.

Authors:  Peter C Raffalt; Jennifer M Yentes
Journal:  J Biomech       Date:  2020-06-13       Impact factor: 2.712

2.  Adaptive treadmill control can be manipulated to increase propulsive impulse while maintaining walking speed.

Authors:  Kayla M Pariser; Margo C Donlin; Kaitlyn E Downer; Jill S Higginson
Journal:  J Biomech       Date:  2022-01-28       Impact factor: 2.712

3.  Optic flow improves step width and length in older adults while performing dual task.

Authors:  Taylor Leeder; Farahnaz Fallahtafti; Molly Schieber; Sara A Myers; Julie Blaskewicz Boron; Jennifer M Yentes
Journal:  Aging Clin Exp Res       Date:  2018-10-26       Impact factor: 3.636

4.  Task specificity impacts dual-task interference in older adults.

Authors:  Farahnaz Fallahtafti; Julie B Boron; Dawn M Venema; Hyeon Jung Kim; Jennifer M Yentes
Journal:  Aging Clin Exp Res       Date:  2020-05-06       Impact factor: 3.636

5.  Speed-related but not detrended gait variability increases with more sensitive self-paced treadmill controllers at multiple slopes.

Authors:  Cesar R Castano; Helen J Huang
Journal:  PLoS One       Date:  2021-05-07       Impact factor: 3.240

6.  Strength of Plantar- and Dorsiflexors Mediates Step Regularity During a High Cognitive Load Situation in a Cross-sectional Cohort of Older and Younger Adults.

Authors:  Farahnaz FallahTafti; Kristen Watson; Julie Blaskewicz Boron; Sara A Myers; Kendra K Schmid; Jennifer M Yentes
Journal:  J Geriatr Phys Ther       Date:  2020 Oct/Dec       Impact factor: 3.190

  6 in total

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