This systematic review examined available findings on spatial and temporal characteristics of cortical activity in response to unpredicted mechanical perturbations. Secondly, this review investigated associations between cortical activity and behavioral/biomechanical measures. Databases were searched from 1980–2021 and a total of 35 cross-sectional studies (31 EEG and 4 fNIRS) were included. Majority of EEG studies assessed perturbation-evoked potentials (PEPs), whereas other studies assessed changes in cortical frequencies. Further, fNIRS studies assessed hemodynamic changes. The PEP-N1, commonly identified at sensorimotor areas, was most examined and was influenced by context prediction, perturbation magnitude, motor adaptation and age. Other PEPs were identified at frontal, parietal and sensorimotor areas and were influenced by task position. Further, changes in cortical frequencies were observed at prefrontal, sensorimotor and parietal areas and were influenced by task difficulty. Lastly, hemodynamic changes were observed at prefrontal and frontal areas and were influenced by task prediction. Limited studies reported associations between cortical and behavioral outcomes. This review provided evidence regarding the involvement of cerebral cortex for sensory processing of unpredicted perturbations, error-detection of expected versus actual postural state, and planning and execution of compensatory stepping responses. There is still limited evidence examining cortical activity during reactive balance tasks in populations with high fall-risk.

This study assessed the effect of filter parameters on gait characteristics and the performance of machine-learning models. Overground walking trials (n = 99) with and without perturbations (slips, trips) were collected for 33 healthy older adults. Kinematics were collected by a motion capture system. Different Butterworth low-pass parameters were applied to the raw data, including three orders (2–6) and nine cutoffs (4–20 Hz). Spatiotemporal gait outcomes were then calculated to develop classification models to automatically identify the trial type (gait, gait–slip, or gait–trip) using Logistic Regression, Support Vector Classification, and Random Forest Classification. A 3 × 9 ANOVA showed main effects of order and cutoff (p < 0.01 for all) on gait characteristics during both perturbed and regular walking trials. However, the gait characteristics were different between them. The filter parameters significantly affected the performance of classification models using different classifiers, with significant main effects of the filter order (p < 0.05) and cutoff (p < 0.01) on AUC and overall accuracy for all of the models. Our results suggest that the standard Butterworth filter (fourth-order, cutoff: 6 Hz) is suitable for the development of classification models with low–medium complexity, while for models with high complexity (i.e., ensemble models), a filter with a higher order and cutoff (sixth-order, cutoff 10–12 Hz) might yield better performance.

We examined the effect of aging and cortical stroke on the rate of motor adaptation (adaptation rate) and amount of performance gains (adaptation plateau) in balance skills. Fourteen older (≥60 years) and fifteen younger (<60 years) adults with chronic stroke, and thirteen healthy older adults (≥60 years) participated. Participants experienced 8 consecutive gait-slips (≤45 cm) to their non-paretic/dominant limb. Slip outcome (backward/no balance loss) was compared using generalized estimating equations (GEE). Proactive (pre-slip stability) and reactive adjustments (post-slip stability, slip displacement and velocity, and compensatory step length) were compared using non-linear regression models. GEE showed the main effect of group, trial, and group × trial interaction for slip outcome (p < 0.05). There were no differences in the adaptation rate for proactive and reactive variables and plateau for proactive variables (p > 0.05). However, both stroke groups demonstrated a smaller adaptation plateau for the majority of reactive variables compared to healthy older adults (p < 0.05). The rate of adaptation to gait-slips does not slow with aging and cortical stroke; however, cortical stroke, age notwithstanding, may reduce performance gains in reactive balance skills, possibly hindering retention and transfer to real-life scenarios. People with stroke may need adjunctive therapies/supplemental agents to apply laboratory-acquired balance skills to daily life.

One emerging method in home stroke rehabilitation is digital technology. However, existing approaches typically target one domain (e.g., upper limb). Moreover, existing interventions do not cater to older adults with stroke (OAwS), especially those with high motor impairment, who require adjunct therapeutic agents to independently perform challenging exercises. We examined the feasibility of Smartphone-based Exercise Training after Stroke (SETS) with Functional Electrical Stimulation (FES). A total of 12 participants (67 ± 5 years) with stroke (onset > 6 months) exhibiting moderate-to-high motor impairment (Chedoke McMaster Leg ≤ 4/7) underwent 6 weeks of multicomponent (gait, functional strength, dynamic balance) training integrated with FES to paretic lower limb muscles. Primary measures included safety and adherence. Secondary measures included motivation, acceptability and attitude, usability, and clinical measures of gait and balance function like the 10-Meter Walk Test and Mini-BESTest. Participants reported no adverse events and moderate-to-high adherence (84.17 ± 11.24%) and improvement (up to 40%) in motivation, acceptability, and attitude and system usability. Participants also showed pre-post improvements in all measures of gait and balance function (p < 0.05). Integrating SETS and FES is feasible and yields short-term gains in gait and balance function among OAwS. Future studies could validate our findings by examining its efficacy with control groups to identify the differential effects of SETS and FES.

Background: Biomechanical and neuromuscular differences between falls and recoveries have been well-studied; however, the cortical correlations remain unclear. Using mobile brain imaging via electroencephalography (EEG), we examined differences in sensorimotor beta frequencies between falls and recoveries during an unpredicted slip in walking. Methods: We recruited 22 young adults (15 female; 18–35 years) who experienced a slip (65 cm) during walking. Raw EEG signals were band-pass filtered, and independent component analysis was performed to remove non-neural sources, eventually three participants were excluded due to excessive artifacts. Peak beta power was extracted from three time-bins: 400 milliseconds pre-, 0–150 milliseconds post and 150–300 milliseconds post-perturbation from the midline (Cz) electrode. A 2 × 3 Analysis of Covariance assessed the interaction between time-bins and group on beta power, followed by Independent and Paired t-tests for between and within-group post hoc comparisons. Results: All participants (n = 19) experienced a balance loss, seven experienced a fall. There was a time × group interaction on beta power (p < 0.05). With no group differences pre-perturbation, participants who experienced a fall exhibited higher beta power during 0–150 milliseconds post-perturbation than those who recovered (p < 0.001). However, there were no group differences in beta power during 150–300 milliseconds post-perturbation. Conclusions: Young adults exhibiting a greater increase in beta power during the early post-perturbation period experienced a fall, suggesting a higher cortical error detection due to a larger mismatch in the expected and ongoing postural state and greater cortical dependence for sensorimotor processing. Our study results provide an overview of the possible cortical governance to modulate slip-fall/recovery outcomes.