Dataset Information

Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

ABSTRACT: In order to determine optimal training parameters for robot-assisted treadmill walking, it is essential to understand how a robotic device interacts with its wearer, and thus, how parameter settings of the device affect locomotor control. The aim of this study was to assess the effect of different levels of guidance force during robot-assisted treadmill walking on cortical activity. Eighteen healthy subjects walked at 2 km.h-1 on a treadmill with and without assistance of the Lokomat robotic gait orthosis. Event-related spectral perturbations and changes in power spectral density were investigated during unassisted treadmill walking as well as during robot-assisted treadmill walking at 30%, 60% and 100% guidance force (with 0% body weight support). Clustering of independent components revealed three clusters of activity in the sensorimotor cortex during treadmill walking and robot-assisted treadmill walking in healthy subjects. These clusters demonstrated gait-related spectral modulations in the mu, beta and low gamma bands over the sensorimotor cortex related to specific phases of the gait cycle. Moreover, mu and beta rhythms were suppressed in the right primary sensory cortex during treadmill walking compared to robot-assisted treadmill walking with 100% guidance force, indicating significantly larger involvement of the sensorimotor area during treadmill walking compared to robot-assisted treadmill walking. Only marginal differences in the spectral power of the mu, beta and low gamma bands could be identified between robot-assisted treadmill walking with different levels of guidance force. From these results it can be concluded that a high level of guidance force (i.e., 100% guidance force) and thus a less active participation during locomotion should be avoided during robot-assisted treadmill walking. This will optimize the involvement of the sensorimotor cortex which is known to be crucial for motor learning.

SUBMITTER: Knaepen K

PROVIDER: S-EPMC4617721 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

Knaepen Kristel K Mierau Andreas A Swinnen Eva E Fernandez Tellez Helio H Michielsen Marc M Kerckhofs Eric E Lefeber Dirk D Meeusen Romain R

PloS one 20151020 10

In order to determine optimal training parameters for robot-assisted treadmill walking, it is essential to understand how a robotic device interacts with its wearer, and thus, how parameter settings of the device affect locomotor control. The aim of this study was to assess the effect of different levels of guidance force during robot-assisted treadmill walking on cortical activity. Eighteen healthy subjects walked at 2 km.h-1 on a treadmill with and without assistance of the Lokomat robotic gai ...[more]

PMID: 26485148

Similar Datasets

Project description:BACKGROUND:Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) proved to be a promising treatment for restoring and improving the ability to walk. Due to heterogenuous study designs and fragmentary knowlegde about the neural correlates associated with RAGT and the relation to motor recovery, guidelines for an individually optimized therapy can hardly be derived. To optimize robotic rehabilitation, it is crucial to understand how robotic assistance affect locomotor control and its underlying brain activity. Thus, this study aimed to investigate the effects of robotic assistance (RA) during treadmill walking (TW) on cortical activity and the relationship between RA-related changes of cortical activity and biomechanical gait characteristics. METHODS:Twelve healthy, right-handed volunteers (9 females; M = 25 ± 4 years) performed unassisted walking (UAW) and robot-assisted walking (RAW) trials on a treadmill, at 2.8 km/h, in a randomized, within-subject design. Ground reaction forces (GRFs) provided information regarding the individual gait patterns, while brain activity was examined by measuring cerebral hemodynamic changes in brain regions associated with the cortical locomotor network, including the sensorimotor cortex (SMC), premotor cortex (PMC) and supplementary motor area (SMA), using functional near-infrared spectroscopy (fNIRS). RESULTS:A statistically significant increase in brain activity was observed in the SMC compared with the PMC and SMA (p < 0.05), and a classical double bump in the vertical GRF was observed during both UAW and RAW throughout the stance phase. However, intraindividual gait variability increased significantly with RA and was correlated with increased brain activity in the SMC (p = 0.05; r = 0.57). CONCLUSIONS:On the one hand, robotic guidance could generate sensory feedback that promotes active participation, leading to increased gait variability and somatosensory brain activity. On the other hand, changes in brain activity and biomechanical gait characteristics may also be due to the sensory feedback of the robot, which disrupts the cortical network of automated walking in healthy individuals. More comprehensive neurophysiological studies both in laboratory and in clinical settings are necessary to investigate the entire brain network associated with RAW.

Project description:BackgroundNo previous study has examined the age-dependent characteristics of gait in individuals between 50 and 79 years simultaneously in healthy individuals and individuals with Parkinson's disease (PD) over continuous gait cycles. This study aimed to investigate age-related differences in gait characteristics on individuals age ranged 50-79 years, including individuals with PD, during a 1-minute treadmill walking session. Additionally, we aimed to investigate the differences associated with spatiotemporal gait parameters and PD compared in age-matched individuals.MethodsThis study included 26 individuals with PD and 90 participants age ranged 50-79 years. The treadmill walking test at a self-preferred speed was performed for 1 min. The embedded inertial measurement unit sensor in the left and right outsoles-based system was used to collect gait characteristics based on tri-axial acceleration and tri-axial angular velocities.ResultsParticipants aged >60 years had a decreased gait speed and shortened stride and step, which may demonstrate a distinct shift in aging (all p < 0.005). Individuals with PD showed more of a decrease in variables with a loss of consistency, including gait asymmetry (GA), phase coordination index (PCI) and coefficient of variation (CV) of all variables, than age-matched individuals (all p < 0.001). Gait speed, stride and step length, stance phase, variability, GA and PCI were the variables that highly depended on age and PD.DiscussionOlder adults could be considered those older than 60 years of age when gait alterations begin, such as a decreased gait speed as well as shortened stride and step length. On the other hand, a loss of consistency in spatiotemporal parameters and a higher GA and PCI could be used to identify individuals with PD. Thus, the CV of all spatiotemporal parameters, GA and PCI during walking could play an important role and be useful in identifying individuals with PD.ConclusionThis study provided the notable aging pattern characteristics of gait in individuals >50 years, including individuals with PD. Increasing age after 60 years is associated with deterioration in spatiotemporal parameters of gait during continuous 1-minute treadmill walking. Additionally, GA, PCI and the CV of all variables could be used to identify PD which would be placed after 70 years of age. It may be useful to determine the decline of gait performance in general and among individuals with PD.

Dataset Information

Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

Publications

Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets