Klaske van Kammen1, Anne M Boonstra2, Lucas H V van der Woude3, Heleen A Reinders-Messelink4, Rob den Otter5. 1. University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands; Rehabilitation Center 'Revalidatie Friesland', Beetsterzwaag, The Netherlands. Electronic address: k.van.kammen@umcg.nl. 2. Rehabilitation Center 'Revalidatie Friesland', Beetsterzwaag, The Netherlands. Electronic address: a.m.boonstra@revalidatie-friesland.nl. 3. University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands; University of Groningen, University Medical Center Groningen, Center for Rehabilitation, Groningen, The Netherlands. Electronic address: l.h.v.van.der.woude@umcg.nl. 4. Rehabilitation Center 'Revalidatie Friesland', Beetsterzwaag, The Netherlands; University of Groningen, University Medical Center Groningen, Center for Rehabilitation, Groningen, The Netherlands. Electronic address: h.reinders-messelink@revalidatie-friesland.nl. 5. University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands. Electronic address: a.r.den.otter@umcg.nl.
Abstract
BACKGROUND: The ability to provide automated movement guidance is unique for robot assisted gait trainers such as the Lokomat. For the design of training protocols for the Lokomat it is crucial to understand how movement guidance affects the patterning of muscle activity that underlies walking, and how these effects interact with settings for bodyweight support and gait speed. METHODS: Ten healthy participants walked in the Lokomat, with varying levels of guidance (0, 50 and 100%), bodyweight support (0 or 50% of participants' body weight) and gait speed (0.22, 0.5 or 0.78m/s). Surface electromyography of Erector Spinae, Gluteus Medius, Vastus Lateralis, Biceps Femoris, Medial Gastrocnemius and Tibialis Anterior were recorded. Group averaged levels of muscle activity were compared between conditions, within specific phases of the gait cycle. FINDINGS: The provision of guidance reduced the amplitude of activity in muscles associated with stability and propulsion (i.e. Erector Spinae, Gluteus Medius, Biceps Femoris and Medial Gastrocnemius) and normalized abnormally high levels of activity observed in a number of muscles (i.e. Gluteus Medius, Biceps Femoris, and Tibialis anterior). The magnitude of guidance effects depended on both speed and bodyweight support, as reductions in activity were most prominent at low speeds and high levels of bodyweight support. INTERPRETATION: The Lokomat can be effective in eliciting normal patterns of muscle activity, but only under specific settings of its training parameters.
BACKGROUND: The ability to provide automated movement guidance is unique for robot assisted gait trainers such as the Lokomat. For the design of training protocols for the Lokomat it is crucial to understand how movement guidance affects the patterning of muscle activity that underlies walking, and how these effects interact with settings for bodyweight support and gait speed. METHODS: Ten healthy participants walked in the Lokomat, with varying levels of guidance (0, 50 and 100%), bodyweight support (0 or 50% of participants' body weight) and gait speed (0.22, 0.5 or 0.78m/s). Surface electromyography of Erector Spinae, Gluteus Medius, Vastus Lateralis, Biceps Femoris, Medial Gastrocnemius and Tibialis Anterior were recorded. Group averaged levels of muscle activity were compared between conditions, within specific phases of the gait cycle. FINDINGS: The provision of guidance reduced the amplitude of activity in muscles associated with stability and propulsion (i.e. Erector Spinae, Gluteus Medius, Biceps Femoris and Medial Gastrocnemius) and normalized abnormally high levels of activity observed in a number of muscles (i.e. Gluteus Medius, Biceps Femoris, and Tibialis anterior). The magnitude of guidance effects depended on both speed and bodyweight support, as reductions in activity were most prominent at low speeds and high levels of bodyweight support. INTERPRETATION: The Lokomat can be effective in eliciting normal patterns of muscle activity, but only under specific settings of its training parameters.
Authors: Shih-Ching Chen; Jiunn-Horng Kang; Chih-Wei Peng; Chih-Chao Hsu; Yen-Nung Lin; Chien-Hung Lai Journal: Int J Environ Res Public Health Date: 2022-07-04 Impact factor: 4.614
Authors: Klaske van Kammen; Anne M Boonstra; Lucas H V van der Woude; Heleen A Reinders-Messelink; Rob den Otter Journal: J Neuroeng Rehabil Date: 2017-04-20 Impact factor: 4.262
Authors: Sylvana Weiland; Ineke H Smit; Heleen Reinders-Messelink; Lucas H V van der Woude; Klaske van Kammen; Rob den Otter Journal: PLoS One Date: 2018-06-04 Impact factor: 3.240