Abstract
During human walking, there exists a functional neural coupling between arms and legs, and between cervical and lumbosacral pattern generators. Here, we present a novel approach for associating the electromyographic (EMG) activity from upper limb muscles with leg kinematics. Our methodology takes advantage of the high involvement of shoulder muscles in most locomotor-related movements and of the natural co-ordination between arms and legs. Nine healthy subjects were asked to walk at different constant and variable speeds (3–5 km/h), while EMG activity of shoulder (deltoid) muscles and the kinematics of walking were recorded. To ensure a high level of EMG activity in deltoid, the subjects performed slightly larger arm swinging than they usually do. The temporal structure of the burst-like EMG activity was used to predict the spatiotemporal kinematic pattern of the forthcoming step. A comparison of actual and predicted stride leg kinematics showed a high degree of correspondence (r >0.9). This algorithm has been also implemented in pilot experiments for controlling avatar walking in a virtual reality setup and an exoskeleton during over-ground stepping. The proposed approach may have important implications for the design of human–machine interfaces and neuroprosthetic technologies such as those of assistive lower limb exoskeletons.
Original language | English |
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Article number | 838 |
Journal | Frontiers in Human Neuroscience |
Volume | 8 |
Issue number | OCT |
DOIs | |
Publication status | Published - Oct 20 2014 |
Keywords
- Arm–leg co-ordination
- EMG patterns
- Gait kinematics
- Neuroprosthetic technology
- Quadrupedal locomotion
ASJC Scopus subject areas
- Psychiatry and Mental health
- Neurology
- Biological Psychiatry
- Behavioral Neuroscience
- Neuropsychology and Physiological Psychology