Using Gait Variability to Predict Inter-individual Differences in Learning Rate of a Novel Obstacle Course.

Current assessments of fatigue and sleepiness rely on patient reported outcomes (PROs), which are subjective and prone to recall bias. The current study investigated the use of gait variability in the "real world" to identify patient fatigue and daytime sleepiness. Inertial measurement units were worn on the lower backs of 159 participants (117 with six different immune and neurodegenerative disorders and 42 healthy controls) for up to 20 days, whom completed regular PROs. To address walking bouts that were short and sparse, four feature groups were considered: sequence-independent variability (SIV), sequence-dependant variability (SDV), padded SDV (PSDV), and typical gait variability (TGV) measures. These gait variability measures were extracted from step, stride, stance, and swing time, step length, and step velocity. These different approaches were compared using correlations and four machine learning classifiers to separate low/high fatigue and sleepiness.Most balanced accuracies were above 50%, the highest was 57.04% from TGV measures. The strongest correlation was 0.262 from an SDV feature against sleepiness. Overall, TGV measures had lower correlations and classification accuracies.Identifying fatigue or sleepiness from gait variability is extremely complex and requires more investigation with a larger data set, but these measures have shown performances that could contribute to a larger feature set.Clinical relevance- Gait variability has been repeatedly used to assess fatigue in the lab. The current study, however, explores gait variability for fatigue and daytime sleepiness in real-world scenarios with multiple gait-impacted disorders.

Variability in spatiotemporal step characteristics and its relationship to walking performance post-stroke

In a long-term study of student progress in the Loyola University of the South Summer Reading Clinic, patterns of variance for sex and grade level were examined. Three assessment tools were used: the Nelson Reading Test (Vocabulary and Paragraph Comprehension) for grades four through eight, the Gates-MacGinitie Reading Test (Vocabulary and Comprehension) for grades one through three, and the Spache Diagnostic Reading Scales (Instructional [oral] and Independent [silent] subtests) for all students. Subjects were 684 public and private school students in grades one through eight referred to the Clinic over an eight-year period. All were referred for possible reading disabilities. Because reading disabled males outnumber reading disabled females in the general population, the Clinic's data were examined to elucidate the comparative success rates of boys and girls in an intensive reading clinic setting. Grade differences also were examined to find significant differences in rate of learning among different grades. Females outscored males significantly on all measures. Both a difference in performance among grades and a difference in rate of learning among grades were shown.

Do learning rates differ by race/ethnicity over kindergarten? Reconciling results across gain score, first-difference, and random effects models

BackgroundA well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs.MethodsThe accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously.ResultsFour outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes.ConclusionThese findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.

Do spatiotemporal parameters and gait variability differ across the lifespan of healthy adults? A systematic review

Gait disturbances are some of the earliest changes in dementia and their monitoring presents an opportunity for early diagnosis. The exact relationship between gait and well-established biomarkers of Alzheimer’s disease (AD) remains to be clarified. In this study we compared gait-related measures with cerebrospinal fluid (CSF) markers of AD pathology. We recruited seventeen participants with mild AD in a multi-site study and performed gait assessment as well as lumbar punctures to obtain CSF. CSF Aβ42/Aβ40 and Aβ42/Aβ38 correlated positively with measures of variability (step time and step length) in the clinic-based assessments. This was driven by a negative relationship between gait variability and Aβ40 and Aβ38 but not Aβ42.The amyloid ratios and gait variability measures were also associated with more severe functional impairment. We interpret these data as an indication that increasing amyloid production (i.e., increasing Aβ40 and Aβ38) is associated with diminishing cognitive-motor control of gait. These preliminary results suggest that the two amyloid ratios may be a marker of the earliest disturbances in the interplay between cognitive and motor control which characterize dementia.

Individuals with multiple sclerosis (MS) experience gait impairments, particularly while dual-tasking, that contribute to an increased risk of falls. Because falls negatively impact participation and quality of life, it is essential to examine how to improve dual-tasking gait. However, no studies, to date, have examined how gait variability is affected by partial body weight support (PBWS) while dual-tasking in older adults with MS. This study examined how PBWS can affect dual-tasking gait variability in older adults with MS and age-matched healthy older adults (HOA). Twenty individuals from each group underwent a dual-tasking paradigm under PBWS and no body weight support (NBWS) while recording gait variability measures. Under PBWS, older adults with MS exhibited significantly greater decreases in gait variability measurements (i.e. smaller coefficient of variation for step width and stride time) when compared with HOA and NBWS. These study findings suggest that PBWS can assist with dual-tasking gait variability and may serve as a therapeutic tool for clinicians and rehabilitation specialists for improving dual-task ability and potentially decreasing fall risk. This study was the first to investigate the effects of dual-tasking under PBWS on gait variability measures in older adults with MS and age-matched controls.

Reinforcement learning (RL) models have been broadly used in modeling the choice behavior of humans and other animals. In standard RL models, the action values are assumed to be updated according to the reward prediction error (RPE), i.e., the difference between the obtained reward and the expected reward. Numerous studies have noted that the magnitude of the update is biased depending on the sign of the RPE. The bias is represented in RL models by differential learning rates for positive and negative RPEs. However, which aspect of behavioral data that the estimated differential learning rates reflect is not well understood. In this study, we investigate how the differential learning rates influence the statistical properties of choice behavior (i.e., the relation between past experiences and the current choice) based on theoretical considerations and numerical simulations. We clarify that when the learning rates differ, the impact of a past outcome depends on the subsequent outcomes, in contrast to standard RL models with symmetric value updates. Based on the results, we propose a model-neutral statistical test to validate the hypothesis that value updates are asymmetric. The asymmetry in the value updates induces the autocorrelation of choice (i.e., the tendency to repeat the same choice or to switch the choice irrespective of past rewards). Conversely, if an RL model without an intrinsic autocorrelation factor is fitted to data that possess an intrinsic autocorrelation, a statistical bias to overestimate the difference in learning rates arises. We demonstrate that this bias can cause a statistical artifact in RL-model fitting leading to a “pseudo-positivity bias” and a “pseudo-confirmation bias.”

Validation of a micro-doppler radar for measuring gait modifications during multidirectional visual perturbations

Pavlovian biases influence instrumental learning by coupling reward seeking with action invigoration and punishment avoidance with action suppression. Using a probabilistic go/no-go task designed to orthogonalize action (go/no-go) and valence (reward/punishment), recent studies have shown that the interaction between the two is dependent on the striatum and its key neuromodulator dopamine. Using this task, we sought to identify how structural and neuromodulatory age-related differences in the striatum may influence Pavlovian biases and instrumental learning in 25 young and 31 older adults. Computational modeling revealed a significant age-related reduction in reward and punishment sensitivity and marked (albeit not significant) reduction in learning rate and lapse rate (irreducible noise). Voxel-based morphometry analysis using 7Tesla MRI images showed that individual differences in learning rate in older adults were related to the volume of the caudate nucleus. In contrast, dopamine synthesis capacity in the dorsal striatum, assessed using [18F]-DOPA positron emission tomography in 22 of these older adults, was not associated with learning performance and did not moderate the relationship between caudate volume and learning rate. This multiparametric approach suggests that age-related differences in striatal volume may influence learning proficiency in old age.

That learning and memory deficits persist many years following mild traumatic brain injury (mTBI) is controversial due to inconsistent objective evidence supporting subjective complaints. Our prior work demonstrated significant reductions in performance on the initial trial of a verbal learning task and overall slower rate of learning in well-motivated mTBI participants relative to demographically matched controls. In our previous work, we speculated that differences in strategy use could explain the differences in rate of learning. The current study serves to test this hypothesis by examining strategy use on the California Verbal Learning Test-Second Edition. Our present findings support the primary hypothesis that mTBI participants under-utilize semantic clustering strategies during list-learning relative to control participants. Despite achieving comparable total learning scores, we posit that the persisting learning and memory difficulties reported by some mTBI patients may be related to reduced usage of efficient internally driven strategies that facilitate learning. Given that strategy training has demonstrated improvements in learning and memory in educational and occupational settings, we offer that these findings have translational value in offering an additional approach in remediation of learning and memory complaints reported by some following mTBI.

The black box method was developed as an “asocial control” to allow for payoff-based learning while eliminating social responses in repeated public goods games. Players are told they must decide how many virtual coins they want to input into a virtual black box that will provide uncertain returns. However, in truth, they are playing with each other in a repeated social game. By “black boxing” the game’s social aspects and payoff structure, the method creates a population of self-interested but ignorant or confused individuals that must learn the game’s payoffs. This low-information environment, stripped of social concerns, provides an alternative, empirically derived null hypothesis for testing social behaviours, as opposed to the theoretical predictions of rational self-interested agents (Homo economicus). However, a potential problem is that participants can unwittingly affect the learning of other participants. Here, we test a solution to this problem in a range of public goods games by making participants interact, unknowingly, with simulated players (“computerised black box”). We find no significant differences in rates of learning between the original and the computerised black box, therefore either method can be used to investigate learning in games. These results, along with the fact that simulated agents can be programmed to behave in different ways, mean that the computerised black box has great potential for complementing studies of how individuals and groups learn under different environments in social dilemmas.

The Reliability and Validity of Measures of Gait Variability in Community-Dwelling Older Adults

Independent Paths in the Development of Infant Learning and Forgetting