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Control of Leg Movements Driven by EMG Activity of Shoulder Muscles.

La Scaleia V, Sylos-Labini F, Hoellinger T, Wang L, Cheron G, Lacquaniti F, Ivanenko YP - Front Hum Neurosci (2014)

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Neuromotor Physiology, Santa Lucia Foundation , Rome , Italy ; Centre of Space Bio-Medicine, University of Rome Tor Vergata , Rome , Italy.

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.

No MeSH data available.


Performance of leg kinematics prediction algorithm using shoulder muscle EMGs during walking at constant speeds. (A) An example of muscle activity and kinematic patterns of one subject during walking at 4 km/h. Note, a fairly good correspondence between predicted (solid lines) and actual (dotted lines) thigh, shank, and foot elevation angles. (B) Correlation coefficients (averaged across all steps and subjects) between predicted and real shank segment elevation angles using different cut-off frequencies of low-pass filter. (C) Pie charts showing the percentage of subjects with a successful 10 consecutive strides prediction from shoulder EMGs activity (both 1 and 2 Hz low-pass filter for each speed). (D) Correlation (+SD) between actual and predicted kinematic patterns of individual subjects (2 Hz low-pass) using two and four shoulder muscle EMGs. Note, better predictions when using four EMGs.
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Figure 2: Performance of leg kinematics prediction algorithm using shoulder muscle EMGs during walking at constant speeds. (A) An example of muscle activity and kinematic patterns of one subject during walking at 4 km/h. Note, a fairly good correspondence between predicted (solid lines) and actual (dotted lines) thigh, shank, and foot elevation angles. (B) Correlation coefficients (averaged across all steps and subjects) between predicted and real shank segment elevation angles using different cut-off frequencies of low-pass filter. (C) Pie charts showing the percentage of subjects with a successful 10 consecutive strides prediction from shoulder EMGs activity (both 1 and 2 Hz low-pass filter for each speed). (D) Correlation (+SD) between actual and predicted kinematic patterns of individual subjects (2 Hz low-pass) using two and four shoulder muscle EMGs. Note, better predictions when using four EMGs.

Mentions: Figure 2A illustrates an example of shoulder muscle EMG signals during walking at 4 km/h. Typically, the deltoid muscle demonstrated alternating activity during walking: alternation occurred both between left and right sides of the body and between anterior and posterior bellies of the deltoid (DELTa and DELTp). However, there could be an additional smaller second burst of activity over the gait cycle, as well as some inter-individual variability in the timing of the main EMG bursts [see also Ballesteros et al. (1965), Hogue (1969), Ivanenko et al. (2006), Kuhtz-Buschbeck and Jing (2012)]. Nevertheless, in most cases, there were prominent peaks of EMG1 and EMG2 around the beginning of the swing phase of the right and left legs, respectively (Figures 1A and 2A), which allowed us to associate this phasic alternating pattern of the upper limb EMG activity during arm–leg co-ordination with the spatiotemporal pattern of gait kinematics.


Control of Leg Movements Driven by EMG Activity of Shoulder Muscles.

La Scaleia V, Sylos-Labini F, Hoellinger T, Wang L, Cheron G, Lacquaniti F, Ivanenko YP - Front Hum Neurosci (2014)

Performance of leg kinematics prediction algorithm using shoulder muscle EMGs during walking at constant speeds. (A) An example of muscle activity and kinematic patterns of one subject during walking at 4 km/h. Note, a fairly good correspondence between predicted (solid lines) and actual (dotted lines) thigh, shank, and foot elevation angles. (B) Correlation coefficients (averaged across all steps and subjects) between predicted and real shank segment elevation angles using different cut-off frequencies of low-pass filter. (C) Pie charts showing the percentage of subjects with a successful 10 consecutive strides prediction from shoulder EMGs activity (both 1 and 2 Hz low-pass filter for each speed). (D) Correlation (+SD) between actual and predicted kinematic patterns of individual subjects (2 Hz low-pass) using two and four shoulder muscle EMGs. Note, better predictions when using four EMGs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4202724&req=5

Figure 2: Performance of leg kinematics prediction algorithm using shoulder muscle EMGs during walking at constant speeds. (A) An example of muscle activity and kinematic patterns of one subject during walking at 4 km/h. Note, a fairly good correspondence between predicted (solid lines) and actual (dotted lines) thigh, shank, and foot elevation angles. (B) Correlation coefficients (averaged across all steps and subjects) between predicted and real shank segment elevation angles using different cut-off frequencies of low-pass filter. (C) Pie charts showing the percentage of subjects with a successful 10 consecutive strides prediction from shoulder EMGs activity (both 1 and 2 Hz low-pass filter for each speed). (D) Correlation (+SD) between actual and predicted kinematic patterns of individual subjects (2 Hz low-pass) using two and four shoulder muscle EMGs. Note, better predictions when using four EMGs.
Mentions: Figure 2A illustrates an example of shoulder muscle EMG signals during walking at 4 km/h. Typically, the deltoid muscle demonstrated alternating activity during walking: alternation occurred both between left and right sides of the body and between anterior and posterior bellies of the deltoid (DELTa and DELTp). However, there could be an additional smaller second burst of activity over the gait cycle, as well as some inter-individual variability in the timing of the main EMG bursts [see also Ballesteros et al. (1965), Hogue (1969), Ivanenko et al. (2006), Kuhtz-Buschbeck and Jing (2012)]. Nevertheless, in most cases, there were prominent peaks of EMG1 and EMG2 around the beginning of the swing phase of the right and left legs, respectively (Figures 1A and 2A), which allowed us to associate this phasic alternating pattern of the upper limb EMG activity during arm–leg co-ordination with the spatiotemporal pattern of gait kinematics.

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Neuromotor Physiology, Santa Lucia Foundation , Rome , Italy ; Centre of Space Bio-Medicine, University of Rome Tor Vergata , Rome , Italy.

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.

No MeSH data available.