Interactive Rhythmic Cue Facilitates Gait Relearning in Patients with Parkinson's Disease

To develop a method for cooperative human gait training, we investigated whether interactive rhythmic cues could improve the gait performance of Parkinson's disease patients. The interactive rhythmic cues ware generated based on the mutual entrainment between the patient's gait rhythms and the cue rhythms input to the patient while the patient walked. Previously, we found that the dynamic characteristics of stride interval fluctuation in Parkinson's disease patients were improved to a healthy 1/f fluctuation level using interactive rhythmic cues and that this effect was maintained in the short term. However, two problems remained in our previous study. First, it was not clear whether the key factor underpinning the effect was the mutual entrainment between the gait rhythms and the cue rhythms or the rhythmic cue fluctuation itself. Second, it was not clear whether or not the gait restoration was maintained longitudinally and was relearned after repeating the cue-based gait training. Thus, the present study clarified these issues using 32 patients who participated in a four-day experimental program. The patients were assigned randomly to one of four experimental groups with the following rhythmic cues: (a) interactive rhythmic cue, (b) fixed tempo cue, (c) 1/f fluctuating tempo cue, and (d) no cue. It has been reported that the 1/f fluctuation of stride interval in healthy gait is absent in Parkinson's disease patients. Therefore, we used this dynamic characteristic as an evaluation index to analyze gait relearning in the four different conditions. We observed a significant effect in condition (a) that the gait fluctuation of the patients gradually returned to a healthy 1/f fluctuation level, whereas this did not occur in the other conditions. This result suggests that the mutual entrainment can facilitate gait relearning effectively. It is expected that interactive rhythmic cues will be widely applicable in the fields of rehabilitation and assistive technology.

[1]  Bruce J. West,et al.  Understanding the complexity of human gait dynamics. , 2009, Chaos.

[2]  Jeffrey M. Hausdorff,et al.  Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease. , 2007, Physica A.

[3]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  T. Hasegawa,et al.  Rhythmic synchronization tapping to an audio–visual metronome in budgerigars , 2011, Scientific reports.

[5]  Nicholas I. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[6]  Michael J. Hove,et al.  Interactive Rhythmic Auditory Stimulation Reinstates Natural 1/f Timing in Gait of Parkinson's Patients , 2012, PloS one.

[7]  W. Weiner.  Parkinson’s Disease and Movement Disorders , 2000, Current Clinical Practice.

[8]  Nicholas Stergiou,et al.  Sensitivity of the Wolf’s and Rosenstein’s Algorithms to Evaluate Local Dynamic Stability from Small Gait Data Sets: Response to Commentaries by Bruijn et al. , 2012, Annals of Biomedical Engineering.

[9]  N. Stergiou,et al.  Gait Variability is Altered in Older Adults When Listening to Auditory Stimuli with Differing Temporal Structures , 2013, Annals of Biomedical Engineering.

[10]  Douglas G. Stuart,et al.  Neural Control of Locomotion , 1976, Advances in Behavioral Biology.

[11]  Yoshihiro Miyake,et al.  Two types of phase correction mechanism involved in synchronized tapping , 2007, Neuroscience Letters.

[12]  Yoshihiro Miyake,et al.  Gait Rhythm of Parkinson’s Disease Patients and an Interpersonal Synchrony Emulation System based on Cooperative Gait , 2013 .

[13]  Yoshihiro Miyake,et al.  Mutual-entrainment-based internal control in adaptive process of human-robot cooperative walk , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[14]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[15]  Yoshihiro Miyake,et al.  Man-Machine Interaction as Co-Emergence Communication , 2001 .

[16]  J. Mattingley,et al.  Reduction in external cues and movement sequencing in Parkinson's disease. , 1994, Journal of neurology, neurosurgery, and psychiatry.

[17]  Marjorie H. Woollacott,et al.  Motor Control: Theory and Practical Applications , 1995 .

[18]  H. Yuasa,et al.  Coordination of many oscillators and generation of locomotory patterns , 1990, Biological Cybernetics.

[19]  G. Kwakkel,et al.  Cueing training in the home improves gait-related mobility in Parkinson’s disease: the RESCUE trial , 2007, Journal of Neurology, Neurosurgery & Psychiatry.

[20]  Michael H. Thaut,et al.  Rhythmic Auditory Stimulation in Rehabilitation of Movement Disorders: A Review Of Current Research , 2010 .

[21]  Jeffrey M. Hausdorff.  Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. , 2007, Human movement science.

[22]  K. Torre,et al.  Fractal dynamics of human gait: a reassessment of the 1996 data of Hausdorff et al. , 2009, Journal of applied physiology.

[23]  Jeffrey M. Hausdorff.  Gait dynamics in Parkinson's disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. , 2009, Chaos.

[24]  A M Graybiel,et al.  The basal ganglia and adaptive motor control. , 1994, Science.

[25]  Jirí Mates,et al.  A model of synchronization of motor acts to a stimulus sequence , 2004, Biological Cybernetics.

[26]  G. Ermentrout,et al.  Coupled oscillators and the design of central pattern generators , 1988 .

[27]  G. Rizzolatti,et al.  Premotor cortex and the recognition of motor actions. , 1996, Brain research. Cognitive brain research.

[28]  M. Turvey,et al.  Phase transitions and critical fluctuations in the visual coordination of rhythmic movements between people. , 1990, Journal of experimental psychology. Human perception and performance.

[29]  L. Liebovitch,et al.  "Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations". , 1996, Journal of applied physiology.

[30]  M. Newman.  Power laws, Pareto distributions and Zipf's law , 2005 .

[31]  Taiki Ogata,et al.  Temporal co-creation between self and others with multi-sensory inputs , 2006, Adv. Eng. Informatics.

[32]  Jeffrey M. Hausdorff,et al.  The sensory feedback mechanisms enabling couples to walk synchronously: An initial investigation , 2007, Journal of NeuroEngineering and Rehabilitation.

[33]  Christopher T. Kello,et al.  Scaling laws in cognitive sciences , 2010, Trends in Cognitive Sciences.

[34]  S. Grillner,et al.  Neuronal Control of LocomotionFrom Mollusc to Man , 1999 .

[35]  R. Iansek,et al.  Movement-related potentials in Parkinson's disease. Presence and predictability of temporal and spatial cues. , 1995, Brain : a journal of neurology.

[36]  Gentaro Taga,et al.  A model of the neuro-musculo-skeletal system for human locomotion , 1995, Biological Cybernetics.

[37]  G. Aschersleben,et al.  The Theory of Event Coding (TEC): a framework for perception and action planning. , 2001, The Behavioral and brain sciences.

[38]  T. Hermana,et al.  Gait instability and fractal dynamics of older adults with a “ cautious ” gait : why do certain older adults walk fearfully ? , 2005 .

[39]  Jeffrey M. Hausdorff,et al.  Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. , 2000, Journal of applied physiology.

[40]  Yoshihiro Miyake,et al.  Mutual entrainment based human-robot communication field-paradigm shift from "human interface" to "communication field" , 1994, Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication.

[41]  Yoshihiro Miyake,et al.  Two types of anticipation in synchronization tapping. , 2004, Acta neurobiologiae experimentalis.

[42]  Gentaro Taga,et al.  A model of the neuro-musculo-skeletal system for human locomotion , 1995, Biological Cybernetics.

[43]  S. Kotz,et al.  Non-motor basal ganglia functions: A review and proposal for a model of sensory predictability in auditory language perception , 2009, Cortex.

[44]  D L Gilden,et al.  1/f noise in human cognition. , 1995, Science.

[45]  Jeffrey M. Hausdorff,et al.  Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. , 1996, Journal of applied physiology.

[46]  Hiroshi Shimizu,et al.  Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment , 1991, Biological Cybernetics.

[47]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[48]  Yoshihiro Miyake,et al.  Interpersonal Synchronization of Body Motion and the Walk-Mate Walking Support Robot , 2009, IEEE Transactions on Robotics.

[49]  T. Muto,et al.  Virtual Robot for Interactive Gait Training Improving Regularity and Dynamic Stability of the Stride Patterns , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[50]  S. Grillner.  Neurobiological bases of rhythmic motor acts in vertebrates. , 1985, Science.

[51]  Thomas L. Thornton,et al.  Provenance of correlations in psychological data , 2005, Psychonomic bulletin & review.

[52]  Aniruddh D. Patel,et al.  The impact of basal ganglia lesions on sensorimotor synchronization, spontaneous motor tempo, and the detection of tempo changes , 2011, Behavioural Brain Research.

[53]  M. Brett,et al.  Impairment of beat-based rhythm discrimination in Parkinson's disease , 2009, Cortex.

[54]  Jirí Mates,et al.  A model of synchronization of motor acts to a stimulus sequence , 1994, Biological Cybernetics.