Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people

This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein's algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.

Application of vibration to the soles increases long-range correlations in the stride parameters during walking

Temporal fluctuations in the stride parameters during human walking exhibit long-range correlations, but these long-range correlations in the stride parameters decrease due to aging or neuromuscular diseases. These observations suggest that any quantified index of the long-range correlation can be regarded as an indicator of gait functionality. Considering the effect of task-relevant sensory feedback on augmenting human motor performance, we devised shoes with active insoles that could deliver noisy vibration to the soles of feet and assessed their efficacy in enhancing the long-range correlations in the stride parameters for healthy young adults. The vibration could be wirelessly controlled using a smartphone. The actuators, control unit, and battery in the devised shoes were light and embedded in the shoes. By virtue of this compactness, the shoes could be easily used for daily walking outside a laboratory. We performed walking experiments with 20 healthy adults and evaluated the effects of sub- and supra-threshold vibration on long-range correlations in stride interval and length. We performed detrended fluctuation analysis to quantify the long-range correlation of temporal changes in stride interval and length. We found that supra-threshold vibration, applied to the soles with the amplitude of 130 % of the sensory threshold, significantly increased the long-range correlations in stride interval and length by 10.3 % (p = 0.009) and 10.1 % (p = 0.021), respectively. On the other hand, sub-threshold vibration with the amplitude of 90 % of the sensory threshold had no significant effect. These results demonstrate that additional somatosensory feedback through barely detectable vibrations, which are supplied by compact shoes with active insoles, can enhance the indices of "healthy" complexity of locomotor function.

Gauging Gait Disorders with a Method Inspired by Motor Control Theories: A Pilot Study in Friedreich's Ataxia

To date, it has been challenging for clinicians and researchers alike to use the multiple outcome measures available to create a meaningful clinical picture and perform effective longitudinal follow-up. It has been found that instrumented gait analysis can provide information associated with a patient’s performance and help to remedy the shortcomings of the currently available outcome measures. The goal of this methodological article is to set the background and justify a new outcome measure inspired by the motor control theories to analyze gait using spatiotemporal parameters. The method is applied in a population of individuals living with Friedreich’s ataxia (FRDA), a neurodegenerative disease. The sample population consisted of 19 subjects, 11 to 65 years of age with FRDA, who either ambulated independently, with a cane, or with a rollator. Three scores based on the distance from healthy normative data were used: Organization Score, Variability Score, and an overall measurement, the Global Ambulation Score. The scores were then compared to the Scale for Assessment and Rating of Ataxia (SARA) Gait Score (SARA-GS), a clinical scale currently being used for gait analysis in FRDA. Organization Scores demonstrated a longitudinal deterioration in the gait characteristics from independent ambulators to those who ambulated with a rollator. Variability Scores mostly reflected dynamic instability, which became greater as the requirement of an ambulation aid or the switch from a cane to a rollator was imminent. The global value given by the Global Ambulation Score, which takes into consideration both the Organization Score, the Variability Score, and the level of assistive device, demonstrated a logarithmic relationship with the SARA-GS. Overall, these results highlight that both components introduced should be analyzed concurrently and suggest that the Global Ambulation Score may be a valuable outcome measure for longitudinal disease progression.

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure (COP) trajectory is sufficiently unique to identify a person with high certainty. Thirty-six adults walked for 30 min on a treadmill equipped with a force platform that continuously recorded the positions of the COP. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2250 segments with an overall accuracy of 99.9%. A second set of 4500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used to fine tune the pretrained CNNs. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures while walking and that CNNs can learn the distinctive features of these trajectories. By applying a pretrained CNN (transfer learning), a couple of strides seem enough to learn and identify new gaits. However, these promising results should be confirmed in a larger sample under realistic conditions.