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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.


On-line shoulder muscle EMG control of leg movements. (A) Controlling of walking avatar in a virtual reality setup (third person viewpoint). To control the timing and duration of individual steps, the subject produced alternating arm swinging movements in standing position (upper panel). Lower panel – pie charts showing the percentage of trials with a successful 1-min test for producing stepping (if the algorithm predicted consecutive uninterrupted steps during the 1-min trial) using alternating arm swinging at different frequencies (n = 8 subjects, 24 trials total for each condition). (B) Arm EMG-based control of stepping in the exoskeleton by the healthy subject. Upper traces – rectified (gray) and low-pass filtered (black) EMGs of shoulder muscles. Each step duration and initiation were calculated and triggered based on the timing of the shoulder EMG peaks. Bottom traces – knee and hip joint angle kinematic patterns of eight consecutive steps along a 9-m walkway.
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Figure 4: On-line shoulder muscle EMG control of leg movements. (A) Controlling of walking avatar in a virtual reality setup (third person viewpoint). To control the timing and duration of individual steps, the subject produced alternating arm swinging movements in standing position (upper panel). Lower panel – pie charts showing the percentage of trials with a successful 1-min test for producing stepping (if the algorithm predicted consecutive uninterrupted steps during the 1-min trial) using alternating arm swinging at different frequencies (n = 8 subjects, 24 trials total for each condition). (B) Arm EMG-based control of stepping in the exoskeleton by the healthy subject. Upper traces – rectified (gray) and low-pass filtered (black) EMGs of shoulder muscles. Each step duration and initiation were calculated and triggered based on the timing of the shoulder EMG peaks. Bottom traces – knee and hip joint angle kinematic patterns of eight consecutive steps along a 9-m walkway.

Mentions: The suggested algorithm has also been implemented in the pilot experiments to trigger steps and control avatar walking (Figure 4A) and an exoskeleton during over-ground stepping (Figure 4B). Each trial consisted of the three locomotor-related phases controlled by the timing of EMG peaks: gait initiation, walking at a variable speed, and gait termination. In the absence of the EMG peak, the virtual avatar or exoskeleton did not produce further steps and gait termination was performed. The exoskeleton was tested only in one trained subject since typically the wearer has to use crutches to guarantee lateral stability (Wang et al., 2013). Nevertheless, this subject succeeded to use alternating EMG bursts of shoulder muscles to trigger 7–10 consecutive strides along a 8-m walkway (Figure 4B). Again, the percentage of successful trials for controlling virtual avatar at slow, self-selected, and fast frequency of arm movements was high (Figure 4A, lower panel) and similar to that found in the first experiment (Figure 2C) even though different subjects participated in the two protocols.


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)

On-line shoulder muscle EMG control of leg movements. (A) Controlling of walking avatar in a virtual reality setup (third person viewpoint). To control the timing and duration of individual steps, the subject produced alternating arm swinging movements in standing position (upper panel). Lower panel – pie charts showing the percentage of trials with a successful 1-min test for producing stepping (if the algorithm predicted consecutive uninterrupted steps during the 1-min trial) using alternating arm swinging at different frequencies (n = 8 subjects, 24 trials total for each condition). (B) Arm EMG-based control of stepping in the exoskeleton by the healthy subject. Upper traces – rectified (gray) and low-pass filtered (black) EMGs of shoulder muscles. Each step duration and initiation were calculated and triggered based on the timing of the shoulder EMG peaks. Bottom traces – knee and hip joint angle kinematic patterns of eight consecutive steps along a 9-m walkway.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: On-line shoulder muscle EMG control of leg movements. (A) Controlling of walking avatar in a virtual reality setup (third person viewpoint). To control the timing and duration of individual steps, the subject produced alternating arm swinging movements in standing position (upper panel). Lower panel – pie charts showing the percentage of trials with a successful 1-min test for producing stepping (if the algorithm predicted consecutive uninterrupted steps during the 1-min trial) using alternating arm swinging at different frequencies (n = 8 subjects, 24 trials total for each condition). (B) Arm EMG-based control of stepping in the exoskeleton by the healthy subject. Upper traces – rectified (gray) and low-pass filtered (black) EMGs of shoulder muscles. Each step duration and initiation were calculated and triggered based on the timing of the shoulder EMG peaks. Bottom traces – knee and hip joint angle kinematic patterns of eight consecutive steps along a 9-m walkway.
Mentions: The suggested algorithm has also been implemented in the pilot experiments to trigger steps and control avatar walking (Figure 4A) and an exoskeleton during over-ground stepping (Figure 4B). Each trial consisted of the three locomotor-related phases controlled by the timing of EMG peaks: gait initiation, walking at a variable speed, and gait termination. In the absence of the EMG peak, the virtual avatar or exoskeleton did not produce further steps and gait termination was performed. The exoskeleton was tested only in one trained subject since typically the wearer has to use crutches to guarantee lateral stability (Wang et al., 2013). Nevertheless, this subject succeeded to use alternating EMG bursts of shoulder muscles to trigger 7–10 consecutive strides along a 8-m walkway (Figure 4B). Again, the percentage of successful trials for controlling virtual avatar at slow, self-selected, and fast frequency of arm movements was high (Figure 4A, lower panel) and similar to that found in the first experiment (Figure 2C) even though different subjects participated in the two protocols.

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.