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Relationships between sensory stimuli and autonomic nervous regulation during real and virtual exercises.

Kiryu T, Iijima A, Bando T - J Neuroeng Rehabil (2007)

Bottom Line: We estimated the ANA from the R-R interval time series of electrocardiogram and incoming sensory stimuli that would activate the ANA.For the virtual exercise, the ANA-related conditions revealed a remarkable time distribution of trigger points that would change eye movement and evoke unpleasant sensations.For expanding the options of motor rehabilitation using VE technology, approaches need to be developed for simultaneously monitoring and separately evaluating the activation of autonomic nervous regulation in relation to neuromuscular and sensory systems with different time scales.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School of Science and Technology, Niigata University, 8050 Ikarashi-2, Nishi-Ku, Niigata 950-2181, Japan. kiryu@eng.niigata-u.ac.jp

ABSTRACT

Background: Application of virtual environment (VE) technology to motor rehabilitation increases the number of possible rehabilitation tasks and/or exercises. However, enhancing a specific sensory stimulus sometimes causes unpleasant sensations or fatigue, which would in turn decrease motivation for continuous rehabilitation. To select appropriate tasks and/or exercises for individuals, evaluation of physical activity during recovery is necessary, particularly the changes in the relationship between autonomic nervous activity (ANA) and sensory stimuli.

Methods: We estimated the ANA from the R-R interval time series of electrocardiogram and incoming sensory stimuli that would activate the ANA. For experiments in real exercise, we measured vehicle data and electromyogram signals during cycling exercise. For experiments in virtual exercise, we measured eye movement in relation to image motion vectors while the subject was viewing a mountain-bike video image from a first-person viewpoint.

Results: For the real cycling exercise, the results were categorized into four groups by evaluating muscle fatigue in relation to the ANA. They suggested that fatigue should be evaluated on the basis of not only muscle activity but also autonomic nervous regulation after exercise. For the virtual exercise, the ANA-related conditions revealed a remarkable time distribution of trigger points that would change eye movement and evoke unpleasant sensations.

Conclusion: For expanding the options of motor rehabilitation using VE technology, approaches need to be developed for simultaneously monitoring and separately evaluating the activation of autonomic nervous regulation in relation to neuromuscular and sensory systems with different time scales.

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Related in: MedlinePlus

Scatter graphs between prRSA and γARV-MPF during climbing for four categories: (a) prRSA before climbing; (b) prRSA during the rest after climbing. The number of samples for each group is displayed with the number of power-assist-off trials in parentheses.
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Figure 2: Scatter graphs between prRSA and γARV-MPF during climbing for four categories: (a) prRSA before climbing; (b) prRSA during the rest after climbing. The number of samples for each group is displayed with the number of power-assist-off trials in parentheses.

Mentions: We categorized each trial into one of four groups on the basis of the median of prRSA and the sign of γARV-MPF for five consecutive pedal strokes immediately before the hilltop and plotted the results in a scatter graph (Figure 2). Table 1 presents the results for other indices. The four groups are denoted HRSA-I/D, HRSA-F/D, LRSA-I/D, and LRSA-F/D. In Figure 2(a), the percentage of power-assist-off trials was the highest for LRSA-I/D and the lowest for HRSA-I/D. The results for HRSA-F/D, which had the largest number of trials, showed negative γARV-MPF with a high prRSA; even in HRSA with power-assist-on trials, negative γARV-MPF sometimes occurred. In this group, the speed was medium, and the torque was the lowest (Table 1). In contrast, the results for LRSA-F/D showed negative γARV-MPF and LRSA. The speed was the lowest, and the torque was medium. In the LRSA-I/D group, the speed was close to that of the HRSA-F/D group and the torque was larger than those of the HRSA-F/D and the LRSA-F/D groups. The highest speed and torque with positive γARV-MPF was for HRSA-I/D. As shown in Figure 2(b), the prRSA during the rest after climbing was significantly higher than prRSA before climbing (paired t-test, p < 0.05), especially for the HRSA-I/D group. Contrary to our expectation for torque-assisted bicycles, the torque-assist supported the appearance of HRSA, but it was sometimes not enough for muscle fatigue.


Relationships between sensory stimuli and autonomic nervous regulation during real and virtual exercises.

Kiryu T, Iijima A, Bando T - J Neuroeng Rehabil (2007)

Scatter graphs between prRSA and γARV-MPF during climbing for four categories: (a) prRSA before climbing; (b) prRSA during the rest after climbing. The number of samples for each group is displayed with the number of power-assist-off trials in parentheses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2117019&req=5

Figure 2: Scatter graphs between prRSA and γARV-MPF during climbing for four categories: (a) prRSA before climbing; (b) prRSA during the rest after climbing. The number of samples for each group is displayed with the number of power-assist-off trials in parentheses.
Mentions: We categorized each trial into one of four groups on the basis of the median of prRSA and the sign of γARV-MPF for five consecutive pedal strokes immediately before the hilltop and plotted the results in a scatter graph (Figure 2). Table 1 presents the results for other indices. The four groups are denoted HRSA-I/D, HRSA-F/D, LRSA-I/D, and LRSA-F/D. In Figure 2(a), the percentage of power-assist-off trials was the highest for LRSA-I/D and the lowest for HRSA-I/D. The results for HRSA-F/D, which had the largest number of trials, showed negative γARV-MPF with a high prRSA; even in HRSA with power-assist-on trials, negative γARV-MPF sometimes occurred. In this group, the speed was medium, and the torque was the lowest (Table 1). In contrast, the results for LRSA-F/D showed negative γARV-MPF and LRSA. The speed was the lowest, and the torque was medium. In the LRSA-I/D group, the speed was close to that of the HRSA-F/D group and the torque was larger than those of the HRSA-F/D and the LRSA-F/D groups. The highest speed and torque with positive γARV-MPF was for HRSA-I/D. As shown in Figure 2(b), the prRSA during the rest after climbing was significantly higher than prRSA before climbing (paired t-test, p < 0.05), especially for the HRSA-I/D group. Contrary to our expectation for torque-assisted bicycles, the torque-assist supported the appearance of HRSA, but it was sometimes not enough for muscle fatigue.

Bottom Line: We estimated the ANA from the R-R interval time series of electrocardiogram and incoming sensory stimuli that would activate the ANA.For the virtual exercise, the ANA-related conditions revealed a remarkable time distribution of trigger points that would change eye movement and evoke unpleasant sensations.For expanding the options of motor rehabilitation using VE technology, approaches need to be developed for simultaneously monitoring and separately evaluating the activation of autonomic nervous regulation in relation to neuromuscular and sensory systems with different time scales.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School of Science and Technology, Niigata University, 8050 Ikarashi-2, Nishi-Ku, Niigata 950-2181, Japan. kiryu@eng.niigata-u.ac.jp

ABSTRACT

Background: Application of virtual environment (VE) technology to motor rehabilitation increases the number of possible rehabilitation tasks and/or exercises. However, enhancing a specific sensory stimulus sometimes causes unpleasant sensations or fatigue, which would in turn decrease motivation for continuous rehabilitation. To select appropriate tasks and/or exercises for individuals, evaluation of physical activity during recovery is necessary, particularly the changes in the relationship between autonomic nervous activity (ANA) and sensory stimuli.

Methods: We estimated the ANA from the R-R interval time series of electrocardiogram and incoming sensory stimuli that would activate the ANA. For experiments in real exercise, we measured vehicle data and electromyogram signals during cycling exercise. For experiments in virtual exercise, we measured eye movement in relation to image motion vectors while the subject was viewing a mountain-bike video image from a first-person viewpoint.

Results: For the real cycling exercise, the results were categorized into four groups by evaluating muscle fatigue in relation to the ANA. They suggested that fatigue should be evaluated on the basis of not only muscle activity but also autonomic nervous regulation after exercise. For the virtual exercise, the ANA-related conditions revealed a remarkable time distribution of trigger points that would change eye movement and evoke unpleasant sensations.

Conclusion: For expanding the options of motor rehabilitation using VE technology, approaches need to be developed for simultaneously monitoring and separately evaluating the activation of autonomic nervous regulation in relation to neuromuscular and sensory systems with different time scales.

Show MeSH
Related in: MedlinePlus