Dataset Information

Reflex control of robotic gait using human walking data.

ABSTRACT: Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous system to quantify their contribution to motor control. RunBot is a bipedal robot which operates through reflexes without using central pattern generators or trajectory planning algorithms. Ground contact information from the feet is used to activate motors in the legs, generating a gait cycle visually similar to that of humans. Rather than developing a more complicated biologically realistic neural system to control the robot's stepping, we have instead further simplified our model by measuring the correlation between heel contact and leg muscle activity (EMG) in human subjects during walking and from this data created filter functions transferring the sensory data into motor actions. Adaptive filtering was used to identify the unknown transfer functions which translate the contact information into muscle activation signals. Our results show a causal relationship between ground contact information from the heel and EMG, which allows us to create a minimal, linear, analogue control system for controlling walking. The derived transfer functions were applied to RunBot II as a proof of concept. The gait cycle produced was stable and controlled, which is a positive indication that the transfer functions have potential for use in the control of assistive devices for the retraining of an efficient and effective gait with potential applications in SCI rehabilitation.

SUBMITTER: Macleod CA

PROVIDER: S-EPMC4210155 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Reflex control of robotic gait using human walking data.

Macleod Catherine A CA Meng Lin L Conway Bernard A BA Porr Bernd B

PloS one 20141027 10

Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous system to quantify their contribution to motor control. RunBot is a bipedal robot which operates through reflexes without using central pattern generators or trajectory planning algorithms. Ground contact ...[more]

PMID: 25347544

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Project description:High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer.We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques.Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking.Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.

Project description:In the process of walking, attentional resources are flexibly allocated to deal with varying environmental constraints correlated with attentional control (AC). A dual-task paradigm was used to investigate the effects of AC on gait and inter-joint coordination. Fifty students volunteered to participate in this study. Based on the reaction time (RT) in the Stroop task, the top 15 participants were assigned to the High Attentional Control (HAC) group, while the last 15 participants were assigned to the Low Attentional Control (LAC) group. The participants in the two groups were randomly asked to perform three tasks: (i) single 2-back working memory task (ST 2-back); (ii) single walking task (ST walking); and (iii) dual task (DT). Cognitive outcomes and gait spatiotemporal parameters were measured. Continuous relative phase (CRP), derived from phase angles of two adjacent joints, was used to assess inter-joint coordination. The LAC group exhibited significant task effects regarding RT, correct rate (CR), step width, gait cycle, step time, forefoot contact times, heel-forefoot times, hip-knee mean absolute relative phase (MARP), and deviation phase (DP) in the stance and swing phases (p < 0.05). In the HAC group, significant task effects were only detected in RT and foot progression angle of the left foot (p < 0.05). Under the three task conditions, the LAC group exhibited a higher CR in ST, longer heel contact times, and longer heel-forefoot times when compared with the LAC group (p < 0.05). Compared with the LAC group, the HAC group exhibited significantly smaller (closer to zero) MARP and weaker hip-knee DP values in the swing phase across all gait conditions (p < 0.05). In the stance phase, the HAC group had smaller MARP (closer to zero) values when compared with the LAC group (p < 0.05). In conclusion, the ability to maintain gait control and modulate inter-joint coordination patterns in young adults is affected by the level of attentional control in accommodating gait disturbances. AC is correlated with the performance of motor control, which theoretically supports the competitive selection of athletes and fall prevention strategies for a specific population.

Dataset Information

Reflex control of robotic gait using human walking data.

Publications

Reflex control of robotic gait using human walking data.

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