Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice

Task specificity and neural adaptations after balance learning in young adults

Background: Only 30 min of balance skill training can significantly improve behavioral and neuromuscular outcomes. However, it is unclear if such a rapidly acquired skill is also retained and transferred to other untrained balance tasks.Research question: What are the effects of a single balance training session on balance skill acquisition, retention, and transferability and on measures of neural plasticity examined by transcranial magnetic brain stimulation (TMS) and inter-muscular coherence?Methods: Healthy younger adults (n = 36, age 20.9, 18 M) were randomly assigned to: Balance training (BT); Active control (cycling training, CT) or non-active control (NC) and received a 20min intervention. Before, immediately and similar to 7 days after the interventions, we assessed performance in the trained wobble board task, untrained static standing tasks and dynamic beam walking balance tasks. Underlying neural plasticity was assessed by tibialis anterior motor evoked potential, intracortical facilitation, short-interval intracortical inhibition and long-interval intracortical inhibition using TMS and by inter-muscular coherence.Results: BT, but not CT (18%, d = 0.32) or NC (-1%, d = -0.02), improved balance performance in the trained, wobble board task by 207% (effect size d = 2.12). BT retained the acquired skill after a 1-week no-training period (136%, d = 1.57). No changes occurred in 4 measures of balance beam walking, in 8 measures of static balance, in 8 measures of intermuscular coherence, and in 4 TMS measures of supra-spinal plasticity (all p &gt; 0.05).Significance: Healthy young adults can learn a specific balance skill very rapidly but one should be aware that while such improvements were retained, the magnitude of transfer (32%, d = 0.94) to other balancing skills was statistically not significant. Additional studies are needed to determine the underlying neural mechanisms of rapid balance skill acquisition, retention, and transfer.</p

Can We Identify Subgroups of Patients with Chronic Low Back Pain Based on Motor Variability? A Systematic Scoping Review

The identification of homogeneous subgroups of patients with chronic low back pain (CLBP), based on distinct patterns of motor control, could support the tailoring of therapy and improve the effectiveness of rehabilitation. The purpose of this review was (1) to assess if there are differences in motor variability between patients with CLBP and pain-free controls, as well as inter-individually among patients with CLBP, during the performance of functional tasks; and (2) to examine the relationship between motor variability and CLBP across time. A literature search was conducted on the electronic databases Pubmed, EMBASE, and Web of Science, including papers published any time up to September 2021. Two reviewers independently screened the search results, assessed the risk of bias, and extracted the data. Twenty-two cross-sectional and three longitudinal studies investigating motor variability during functional tasks were examined. There are differences in motor variability between patients with CLBP and pain-free controls during the performance of functional tasks, albeit with discrepant results between tasks and among studies. The longitudinal studies revealed the persistence of motor control changes following interventions, but the relationship between changes in motor variability and reduction in pain intensity was inconclusive. Based on the reviewed literature, no stratification of homogeneous subgroups into distinct patterns of motor variability in the CLBP population could be made. Studies diverged in methodologies and theoretical frameworks and in metrics used to assess and interpret motor variability. In the future, more large-sample studies, including longitudinal designs, are needed, with standardized metrics that quantify motor variability to fill the identified evidence gaps

Postural threat during walking:Effects on energy cost and accompanying gait changes

Background: Balance control during walking has been shown to involve a metabolic cost in healthy subjects, but it is unclear how this cost changes as a function of postural threat. The aim of the present study was to determine the influence of postural threat on the energy cost of walking, as well as on concomitant changes in spatiotemporal gait parameters, muscle activity and perturbation responses. In addition, we examined if and how these effects are dependent on walking speed. Methods: Healthy subjects walked on a treadmill under four conditions of varying postural threat. Each condition was performed at 7 walking speeds ranging from 60-140% of preferred speed. Postural threat was induced by applying unexpected sideward pulls to the pelvis and varied experimentally by manipulating the width of the path subjects had to walk on. Results: Results showed that the energy cost of walking increased by 6-13% in the two conditions with the largest postural threat. This increase in metabolic demand was accompanied by adaptations in spatiotemporal gait parameters and increases in muscle activity, which likely served to arm the participants against a potential loss of balance in the face of the postural threat. Perturbation responses exhibited a slower rate of recovery in high threat conditions, probably reflecting a change in strategy to cope with the imposed constraints. The observed changes occurred independent of changes in walking speed, suggesting that walking speed is not a major determinant influencing gait stability in healthy young adults. Conclusions: The current study shows that in healthy adults, increasing postural threat leads to a decrease in gait economy, independent of walking speed. This could be an important factor in the elevated energy costs of pathological gait

Adaptive Control of Dynamic Balance across the Adult Lifespan

Introduction The ability to adapt dynamic balance to perturbations during gait deteriorates with age. To prevent age-related decline in adaptive control of dynamic balance, we must first understand how adaptive control of dynamic balance changes across the adult lifespan. We examined how adaptive control of the margin of stability (MoS) changes across the lifespan during perturbed and unperturbed walking on the split-belt treadmill. Methods Seventy-five healthy adults (age range, 18-80 yr) walked on an instrumented split-belt treadmill with and without split-belts. Linear regression analyses were performed for the mediolateral (ML) and anteroposterior (AP) MoS, step length, single support time, step width, double support time, and cadence during unperturbed and perturbed walking (split-belt perturbation), with age as predictor. Results Age did not significantly affect dynamic balance during unperturbed walking. However, during perturbed walking, the ML MoS of the leg on the slow belt increased across the lifespan due to a decrease in bilateral single support time. The AP MoS did not change with aging despite a decrease in step length. Double support time decreased and cadence increased across the lifespan when adapting to split-belt walking. Age did not affect step width. Conclusions Aging affects the adaptive control of dynamic balance during perturbed but not unperturbed treadmill walking with controlled walking speed. The ML MoS increased across the lifespan, whereas bilateral single support times decreased. The lack of aging effects on unperturbed walking suggests that participants' balance should be challenged to assess aging effects during gait. The decrease in double support time and increase in cadence suggests that older adults use the increased cadence as a balance control strategy during challenging locomotor tasks

Do gait and muscle activation patterns change at middle-age during split-belt adaptation?

Advancing age affects gait adaptability, but it is unclear if such adaptations to split-belt perturbations are already affected at middle-age. Changes in neuromuscular control, that already start at middle-age, may underlie the age-related changes in gait adaptation. Thus, we examined the effects of age on adaptations in gait and muscle activation patterns during split-belt walking in healthy young and middle-aged adults. Young (23.3±3.13 years) and middle-aged adults (55.3±2.91 years) walked on an instrumented split-belt treadmill. Both age groups adapted similarly by reducing asymmetry in step length and double support time. Surface EMG was recorded from eight leg muscles bilaterally. Principal Component Analysis (PCA) was applied to the EMG data of all subjects, for the fast and slow leg separately, to identify muscle activation patterns. The principal components consisted of i.e. temporal projections that were analyzed with Statistical Parametric Mapping (SPM). The functional muscle groups, identified by PCA, increased activation during early adaptation and post-adaptation, and decreased activation over time similarly in both age groups. Extra activation peaks of the plantar- and dorsiflexors suggest a role in gait modulation during split-belt walking. Both young and middle-aged adults re-established gait symmetry and showed adaptation effects in the muscle activation patterns. Since the adaptation of muscle activation patterns parallels adaptation of gait symmetry, changes in muscle activation likely underlie the changes in step parameters during split-belt adaptation. In conclusion, split-belt adaptation, in terms of gait and muscle activation patterns, is still preserved at middle-age, suggesting that age-related differences occur later in the lifespan

Shotgun approaches to gait analysis:insights &amp; limitations

Background: Identifying features for gait classification is a formidable problem. The number of candidate measures is legion. This calls for proper, objective criteria when ranking their relevance.Methods: Following a shotgun approach we determined a plenitude of kinematic and physiological gait measures and ranked their relevance using conventional analysis of variance (ANOVA) supplemented by logistic and partial least squares (PLS) regressions. We illustrated this approach using data from two studies involving stroke patients, amputees, and healthy controls.Results: Only a handful of measures turned out significant in the ANOVAs. The logistic regressions, by contrast, revealed various measures that clearly discriminated between experimental groups and conditions. The PLS regression also identified several discriminating measures, but they did not always agree with those of the logistic regression.Discussion &amp; conclusion: Extracting a measure's classification capacity cannot solely rely on its statistical validity but typically requires proper post-hoc analysis. However, choosing the latter inevitably introduces some arbitrariness, which may affect outcome in general. We hence advocate the use of generic expert systems, possibly based on machine-learning.</p

Age-related differences in the control of weight-shifting within the surface of support

BACKGROUND AND AIM: An important reason for falling in elderly is incorrect weight-shifting¹. In many daily life activities quick and accurate weight-shifting is needed to maintain balance, especially in situations when balance is suddenly disturbed and anticipation on the upcoming movement is difficult. Considering the deterioration in postural control in elderly², it is expected that they have more difficulties with executing these quick and accurate weight-transfers³. The present study aims to gain more insight in age-related differences in postural control strategies during a postural control task requiring weight-transfers of different amplitudes and in different directions within the surface of support METHODS: Nine healthy older adults (70.3±6.9 years) and twelve young adults (20.9±0.5 years) participated in the study. The participants performed a weight-shifting task by moving the whole body in different directions to move a cursor, representing real time COP position, towards targets of different sizes and at different distances projected on a screen. Movement time (MT) was the time between the appearance of the goal target and the moment a target switch was realized (i.e. the cursor stayed in the goal target for 0.5 second). The accuracy of the movement was quantified by Counts on Goal (CoG), that is the number of times the cursor hit the goal target before a target switch was realized and by Dwelling Time (DT), the time required to realize a target switch after the goal target was hit by the cursor for the first time. Fluency was expressed by the maximal deviation (MD) of the performed path with respect to the ideal path and the number of peaks (nP), or inflections in the performed path. RESULTS: Significant main effects of target size, target distance and age on all outcome measures were found (p<.01). With decreasing target size, increasing target distance and increasing age, MT significantly increased and fluency and accuracy significantly decreased (nP, MD, CoG and DT increased). Elderly used a slower, less accurate and less fluent weight-shifting strategy compared to younger adults with increasing task difficulty (e.g. decreasing target size and increasing target distance) as indicated by significant interaction effects of size*age and distance*age (p<.05). CONCLUSION: The results of this study provided insight in how elderly control their weight-shifting when the movement cannot be anticipatorily planned. Elderly exhibited slower and more variable movements, especially with increasing task difficulty. This weight-shifting strategy seems characterizing for an increased fall risk in elderly, since the results indicate that elderly might have more difficulties with executing an adequate (quick and accurate) adaptation to a perturbation in daily life. ¹SN Robinovitch et al. Lancet. (2013), 381(9860), 47-54. ²FB Horak. Age Ageing. (2006), 35(2), 7-11. ³V Jongman et al. Stud Health Technol Inform. (2012), 181, 93-97

The detection of age groups by dynamic gait outcomes using machine learning approaches

Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial Neural Network (ANN) were applied to the 23 gait outcomes without prior data reduction. Classification accuracy of SVM was 89%, RF accuracy was 73%, and ANN accuracy was 90%. Gait outcomes that significantly contributed to classification included: Root Mean Square (Anterior-Posterior, Vertical), Cross Entropy (Medio-Lateral, Vertical), Lyapunov Exponent (Vertical), step regularity (Vertical) and gait speed. ANN is preferable due to the automated data reduction and significant gait outcome identification. For clinicians, these gait outcomes could be used for diagnosing subjects with mobility disabilities, fall risk and to monitor interventions. (This work was supported by Keep Control project, funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 721577.