Posts Tagged locomotion

[ARTICLE] Fractal analyses reveal independent complexity and predictability of gait – Full Text

Abstract

Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent αand the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that αand D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures.

Introduction

The stride interval of normal human walking is the time period between consecutive heel strikes of the same foot [1]. For more than two decades, a line of research focused on the understanding of the nature of the subtle variations observed in stride intervals and the origin of typical long-range structures in these variations. Today, these investigations are of paramount importance since they could provide a better understanding of the physiological mechanisms involved in normal human walking and in alterations observed in clinical practice. The nature of these stride interval variations could arise either from noisy neural processes that result in errors in the motor output or from alterations in the motor command that account for balance instabilities [2].

Normal gait is characterized by the presence of autocorrelations in the stride interval when considering walking on a sufficiently long time scale [13]. The origin of these autocorrelations may be attributed to neural central pattern generators (CPGs) [13] or a super CPG coupled to a forced Van der Pol oscillator [4], and/or to the biomechanics of walking [56]. For many years, gait analysis has been studied with classical methods adopting biomechanical models in which variability was not of interest. More recent techniques derived from chaos theory are well adapted to analyse time series that exhibit long-range autocorrelation. Importantly, they treat variability as a meaningful interpretable signal. Since the pioneering works of Hausdorff et al. [13], long-range autocorrelations in time series are estimated by the Hurst or fractal exponent (α). A fractal, introduced in 1975 by the French mathematician Benoît Mandelbrot (1924–2010) [7], is defined as a geometrical structure that has a regular or an uneven shape repeated over all scales of measurement. It is characterized by a fractal dimension (D) greater than the spatial dimension of the structure [8]. A famous example of such object is a snow flake. Objects that are statistically self-similar—parts of it show the same statistical properties at many scales—exhibit strong autocorrelation. The Hurst exponent α is a statistical measure of long-term memory of time series (see e.g. [9] for a review) and is usually associated to fractal-like behaviour. In particular, the peculiar behavior of the stride interval may be referred to as “fractal behavior” [3].

The theoretical model of optimal movement complexity [10] is based on the complementary concepts of predictability and complexity. Nature let us find optimal behavior in terms of skills and variability through evolution. As proposed by Lipsitz and Glodberger in their pioneering work, the optimal state of a biological system may be characterized by chaotic temporal variations in the steady state output that correspond to maximal complexity [11]. Any deviation from healthy state, like senescence and disease, causes a loss in complexity (see also [12]). Too few practice results in high disorder (randomness, no predictability) and excessive practice leads to high order (periodic signal, maximal predictability). Adaptation of a system to external stimuli is maximal only at an intermediate state of predictability. Furthermore, a signal from a dynamical system also holds some inherent complexity. A decrease of complexity of a physiological system results from either a reduction in the number of structural components or an alteration in the coupling function between these components. For instance, a joint can become rigid with senescence, hence decreasing the degree of freedom of the system and consequently, its complexity. A holistic approach to study these mechanisms requires to associate specific measurements to these two concepts. The Hurst exponent α captures part of the story and is well suited to reflect predictability. While the Minkowski fractal dimension Dprovides good measurability of the “apparent rugosity” of fractals [13] and reflects complexity. Note that the quantification of a concept such as complexity may not be linked to a unique measure; entropy-related measures have also been shown to be relvant in that domain [12]. Here, we use these parameters to complement the usual quantification of autocorrelation α in unusual and perturbed gait conditions in an attempt to probe adaptability in the framework of the model of optimal movement complexity [10].

As of today, the vast majority of studies explored autocorrelation in the stride interval during natural forward walking. In one notable exception however, Bollens et al. [14] also tested backward walking in a small sample of young healthy subjects. The authors did not find significant differences in long-range autocorrelation between both walking directions. However, backward walking measures revealed to be more sensitive than forward walking measures to classify elderly fallers compare to non-fallers [15]. The study of backward walking under the perspective of fractal analyses is therefore promising to provide more reliable predictive index of fallers, as previously proposed for forward walking [16]. Backward walking is also frequently used in sports and in rehabilitation settings, and a better understanding of the variability of stride interval in this condition is needed since it is believed that backward walking is at least partly controlled by specialized neural circuits [17].

The vestibular system provides an essential sensory contribution to the maintenance of balance during human walking [18]. Individuals with vestibular disorders show a decreased walking stability accompanied by an increased risk to fall [19]. Therefore, perturbing the vestibular system of healthy subjects with galvanic vestibular stimulation (GVS) is a well targeted mean to probe gait: it is standardized, well tolerated by subjects, generated by currently affordable electrostimulators, and easy to implement when a large number of stride intervals are recorded with an instrumented treadmill. The use of GVS is also an increasingly common clinical intervention on locomotion [2022].

Previously, autocorrelations in stride interval time series have been identified not only in healthy young adults [3] but also in children [23] and elderly [24], and even—although significantly modified—in several neurodegenerative conditions. In particular, the cases of Huntington’s disease [24], amyotrophic lateral sclerosis [25], and Parkinson’s disease have been studied [2627], with a hope of connecting the observed modifications of fractal behavior to some relevant evaluation of the risk of falling [16]. Here, we hypothesize that the combined effects of walking direction and the application of GVS on long-range autocorrelations in the stride interval could enhance the sensitivity of fractal analysis to identify impaired gait. We measured α and D during forward and backward walking, with and without the application of binaural and monaural GVS. We speculate that these two indexes should be able to capture differences between experimental conditions and therefore provide better indexes to classify patients.[…]

Continue —>  Fractal analyses reveal independent complexity and predictability of gait

Fig 1. Typical stride interval time series in the different experimental conditions.
FW or BW stand for forward and backward walking respectively. The indices S+ or S0 indicate the presence or absence of GVS.
https://doi.org/10.1371/journal.pone.0188711.g001

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[Abstract+References] Forced Use of the Paretic Leg Induced by a Constraint Force Applied to the Nonparetic Leg in Individuals Poststroke During Walking

Background. Individuals with stroke usually show reduced muscle activities of the paretic leg and asymmetrical gait pattern during walking. Objective. To determine whether applying a resistance force to the nonparetic leg would enhance the muscle activities of the paretic leg and improve the symmetry of spatiotemporal gait parameters in individuals with poststroke hemiparesis. Methods. Fifteen individuals with chronic poststroke hemiparesis participated in this study. A controlled resistance force was applied to the nonparetic leg using a customized cable-driven robotic system while subjects walked on a treadmill. Subjects completed 2 test sections with the resistance force applied at different phases of gait (ie, early and late swing phases) and different magnitudes (10%, 20%, and 30% of maximum voluntary contraction [MVC] of nonparetic leg hip flexors). Electromyographic (EMG) activity of the muscles of the paretic leg and spatiotemporal gait parameters were collected. Results. Significant increases in integrated EMG of medial gastrocnemius, medial hamstrings, vastus medialis, and tibialis anterior of the paretic leg were observed when the resistance was applied during the early swing phase of the nonparetic leg, compared with baseline. Additionally, resistance with 30% of MVC induced the greatest level of muscle activity than that with 10% or 20% of MVC. The symmetry index of gait parameters also improved with resistance applied during the early swing phase. Conclusion. Applying a controlled resistance force to the nonparetic leg during early swing phase may induce forced use on the paretic leg and improve the spatiotemporal symmetry of gait in individuals with poststroke hemiparesis.

References

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via Forced Use of the Paretic Leg Induced by a Constraint Force Applied to the Nonparetic Leg in Individuals Poststroke During WalkingNeurorehabilitation and Neural Repair – Chao-Jung Hsu, Janis Kim, Elliot J. Roth, William Z. Rymer, Ming Wu, 2017

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[REVIEW] Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed? – Full Text

Abstract

We summarize content from the opening thematic session of the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM). Scientific discoveries from the past 20 years of research are covered, highlighting the impacts of rapid technological, computational, and financial growth on motor control research. We discuss spinal-level communication mechanisms, relationships between muscle structure and function, and direct cortical movement representations that can be decoded in the control of neuroprostheses. In addition to summarizing the rich scientific ideas shared during the session, we reflect on research infrastructure and capacity that contributed to progress in the field, and outline unresolved issues and remaining open questions.

Background

At the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM), the opening thematic session was chaired by Dr. Fay Horak (Oregon Health & Science University). Presentations and discussions covered insights from 20 years of research in the field of motor control, delivered by Drs. Zev Rymer (Rehabilitation Institute of Chicago), Andy Biewener (Harvard University), Andy Schwartz (University of Pittsburgh), and Daofen Chen (National Institute of Neurological Disorders and Stroke). Presentation themes included the impact of technological advancements on motor control research, unresolved issues in muscle biology and neurophysiology, and changes in the scientific funding landscape. This brief review summarizes content presented by each speaker, along with discussions from the audience.

Considerable changes have occurred in the fields of biomechanics and motor control over the past 20 years, changes made possible by rapid technological advances in computing power and memory along with reduced physical size of biotechnology hardware. Because of these changes, research approaches have been reshaped and new questions have emerged. Previously, motor control research was constrained to laboratory-based assessments of individual neurons, muscles or joints, captured from low sample sizes. In the past, reliance on large, expensive, external recording devices, such as optical motion capture systems, understandably limited the feasibility of large-scale, multivariate research. Today, whole-body kinematic recordings using body-worn inertial measurement units, wireless electromyography (EMG), electroencephalography (EEG), and functional near infrared spectroscopy (fNIRS) systems, and electrode arrays for neural network recordings are increasingly commonplace. Alongside these technical leaps, sociocultural bounds have expanded research inclusion, as evidenced in the representation of speakers at the 2016 BANCOM meeting. In contrast to the 1996 meeting, which included three invited female speakers, 13 women were included as speakers in 2016. Such advancements will continue to shape our scientific landscape, driving innovation through new technologies and perspectives.[…]

Continue —>  Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed? | Journal of NeuroEngineering and Rehabilitation | Full Text

 

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[ARTICLE] The Efficacy of State of the Art Overground Gait Rehabilitation Robotics: A Bird’s Eye View – Full Text

Abstract

To date, rehabilitation robotics has come a long way effectively aiding the rehabilitation process of the patients suffering from paraplegia or hemiplegia due to spinal cord injury (SCI) or stroke respectively, through partial or even full functional recovery of the affected limb. The increased therapeutic outcome primarily results from a combination of increased patient independence and as well as reduced physical burden on the therapist. Especially for the case of gait rehabilitation following SCI or stroke, the rehab robots have the potential to significantly increase the independence of the patient during the rehabilitation process without the patient’s safety being compromised. An intensive gait-oriented rehabilitation therapy is often effective irrespective of the type of rehabilitation paradigm. However, eventually overground gait training, in comparison with body-weight supported treadmill training (BWSTT), has the potential of higher therapeutic outcome due its associated biomechanics being very close to that of the natural gait. Recognizing the apparent superiority of the overground gait training paradigms, a through literature survey on all the major overground robotic gait rehabilitation approaches was carried out and is presented in this paper. The survey includes an in-depth comparative study amongst these robotic approaches in terms of gait rehabilitation efficacy.

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Source: The Efficacy of State of the Art Overground Gait Rehabilitation Robotics: A Bird’s Eye View

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[Abstract] Robotic Devices to Enhance Human Movement Performance.

Abstract

Robotic exoskeletons and bionic prostheses have moved from science fiction to science reality in the last decade. These robotic devices for assisting human movement are now technically feasible given recent advancements in robotic actuators, sensors, and computer processors. However, despite the ability to build robotic hardware that is wearable by humans, we still do not have optimal controllers to allow humans to move with coordination and grace in synergy with the robotic devices. We consider the history of robotic exoskeletons and bionic limb prostheses to provide a better assessment of the roadblocks that have been overcome and to gauge the roadblocks that still remain. There is a strong need for kinesiologists to work with engineers to better assess the performance of robotic movement assistance devices. In addition, the identification of new performance metrics that can objectively assess multiple dimensions of human performance with robotic exoskeletons and bionic prostheses would aid in moving the field forward. We discuss potential control approaches for these robotic devices, with a preference for incorporating feedforward neural signals from human users to provide a wider repertoire of discrete and adaptive rhythmic movements.

Source: Robotic Devices to Enhance Human Movement Performance: Kinesiology Review: Vol 6, No 1

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[Abstract] Factors Influencing the Efficacy of Aerobic Exercise for Improving Fitness and Walking Capacity After Stroke: A Meta-Analysis with Meta-Regression.

Abstract

Objective

To assess the influence of dosing parameters and patient characteristics on the efficacy of aerobic exercise (AEX) post-stroke.

Data Sources

A systematic review was conducted using Pubmed, MEDLINE, CINAHL, PEDro and Academic Search Complete.

Study Selection

Studies were selected that compared AEX to a non-aerobic control group among ambulatory persons with stroke.

Data Extraction

Extracted outcome data included: peak oxygen consumption during exercise testing (VO2peak), walking speed and walking endurance (6-minute walk test). Independent variables of interest were: AEX mode (seated or walking), AEX intensity (moderate or vigorous), AEX volume (total hours), stroke chronicity and baseline outcome scores.

Data Synthesis

Significant between-study heterogeneity was confirmed for all outcomes. Pooled AEX effect size estimates (AEX change – control change) from random effects models were: VO2peak, 2.2 mL/kg/min [95% CI: 1.3, 3.1]; walking speed, 0.06 m/s [95% CI: 0.01, 0.11]; and 6-minute walk test distance, 29 m [95% CI: 15, 42]. From meta-regression, greater VO2peak effect sizes were significantly associated with higher AEX intensity and higher baseline VO2peak. Greater effect sizes for walking speed and the 6-minute walk test were significantly associated with a walking AEX mode. In contrast, seated AEX did not have a significant effect on walking outcomes.

Conclusions

AEX significantly improves aerobic capacity post-stroke, but may need to be task specific to impact walking speed and endurance. Higher AEX intensity is associated with better outcomes. Future randomized studies are needed to confirm these results.

Source: Factors Influencing the Efficacy of Aerobic Exercise for Improving Fitness and Walking Capacity After Stroke: A Meta-Analysis with Meta-Regression – Archives of Physical Medicine and Rehabilitation

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[Abstract] Normal and impaired control of functional movements in stroke: Role of neural interlimb coupling – Clinical Neurophysiology

Highlights

A task-specific neural interlimb coupling mechanism underlies functional movements in humans.

After a stroke, the neural coupling mechanism is preserved from the unaffected side but defective from the affected limb(s).

Based on the knowledge of neural coupling, training of cooperative limb movements should be integrated into neuro-rehabilitation.

Abstract

In recent years it has become evident that, in a number of functional movements, synergistically acting limbs become task-specifically linked by a soft-wired ‘neural coupling’ mechanism (e.g. the legs during balancing, the arms and legs during gait and both arms during cooperative hand movements). Experimentally this mechanism became evident by the analysis of reflex responses as a marker for a neural coupling. It is reflected by the task-specific appearance of reflex EMG responses to non-noxious nerve stimulation, not only in muscles of the stimulated limb, but also, with same long latency, in muscles of meaningful coupled (contralateral) limb(s).

After a stroke, nerve stimulation of the unaffected limb during such cooperative tasks is followed by EMG responses in muscles of the (contralateral) coupled affected limb, i.e. unaffected motor centres influence synergistically acting movements of the paretic limb. In contrast, following stimulation of the affected limb, no contralateral responses appear due to defective processing of afferent input. As a consequence, it may be therapeutically possible to strengthen the influence of unaffected motor centres on the performance of affected limb movements through training of cooperative limb movements required during activities of daily living.

Source: Normal and impaired control of functional movements in stroke: Role of neural interlimb coupling – Clinical Neurophysiology

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[REVIEW] The effect of gait training and exercise programs on gait and balance in post-stroke patients – Full Text PDF

The aim of this review is to evaluate studies about gait training and exercise interventions applied to patients following chronic stroke on gait and balance.

The studies included in this review were random clinical trials, including only chronic post-stroke individuals that evaluated gait and balance outcomes and with a PEDro scale score ≥ 7.0. Eight studies were selected.

The results suggest gait and balance will only be affected in chronic post-stroke patients if training sessions last at least 30 minutes, are repeated three times a week, and maintained for at least five weeks. Gait training affects how chronic post-stroke individuals walk. They will probably walk faster and with a lower risk of falling; however, it is unclear whether the consequences of these procedures affect the quality of life.

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Locomotor Training Improves Daily Stepping Activity and Gait Efficiency in Individuals Poststroke Who Have Reached a “Plateau” in Recovery

…Conclusions— Intensive LT results in improved daily stepping in individuals poststroke who have been discharged from PT because of a perceived plateau in motor function. These improvements may be related to the amount and intensity of stepping practice…

μέσω Locomotor Training Improves Daily Stepping Activity and Gait Efficiency in Individuals Poststroke Who Have Reached a “Plateau” in Recovery.

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