Effect of age on detecting a loss of balance in a seated whole-body balancing task

Impaired perceived timing of falls in the elderly

Falls are the leading cause of injury-related deaths and hospitalizations, with older adults at an increased risk. As humans age, physical changes and health conditions make falls more likely. While we know how the body reflexively responds to prevent injury during a fall, we know little about how people perceive the fall itself. We previously found that young adults required a fall to precede a comparison sound stimulus by approximately 44ms to perceive the two events as simultaneous. This may relate to common anecdotal reports suggesting that humans often describe distortions in their perception of time - time seems to slow down during a fall - with very little recollection of how and when the fall began. Here we examine whether fall perception changes with age. Young (19-25y) and older (61-72y) healthy adults made temporal order judgments identifying whether the onset of their fall or the onset of a comparison sound came first to measure the point of subjective simultaneity. Results show that fall perception is nearly twice as slow for older adults, where perturbation onset has to precede sound onset by ∼88ms to appear coincident, compared to younger adults (∼44ms). We suggest that such age-related differences in fall perception may relate to increased fall rates in older adults. We conclude that a better understanding of how younger versus older adults perceive falls may identify important factors for innovative fall prevention strategies and rehabilitative training exercises to improve fall awareness.

Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response

Determining the neural correlates of loss of balance during walking could lead to improved clinical assessment and treatment for individuals predisposed to falls. We used high-density electroencephalography (EEG) combined with independent component analysis (ICA) to study loss of balance during human walking. We examined 26 healthy young subjects performing heel-to-toe walking on a treadmill-mounted balance beam as well as walking on the treadmill belt (both at 0.22 m/s). ICA identified clusters of electrocortical EEG sources located in or near anterior cingulate, anterior parietal, superior dorsolateral-prefrontal, and medial sensorimotor cortex that exhibited significantly larger mean spectral power in the theta band (4-7 Hz) during walking on the balance beam compared with treadmill walking. Left and right sensorimotor cortex clusters produced significantly less power in the beta band (12-30 Hz) during walking on the balance beam compared with treadmill walking. For each source cluster, we also computed a normalized mean time/frequency spectrogram time locked to the gait cycle during loss of balance (i.e., when subjects stepped off the balance beam). All clusters except the medial sensorimotor cluster exhibited a transient increase in theta band power during loss of balance. Cluster spectrograms demonstrated that the first electrocortical indication of impending loss of balance occurred in the left sensorimotor cortex at the transition from single support to double support prior to stepping off the beam. These findings provide new insight into the neural correlates of walking balance control and could aid future studies on elderly individuals and others with balance impairments.

Lower limb control and mobility following exercise training

The objective of the present study was to evaluate the effects of 8-week balance or weight training on ankle joint stiffness and limb stability for older adults, furthermore, on outcomes of slips while walking. Eighteen older adults volunteered for the study and randomly were assigned to the three groups, such as, weight, balance, or control group. While walking on a walking track, three-dimensional posture data were sampled and ankle joint stiffness and limb stability were computed to evaluate the effects of training. 2 (pre and post) × 3 (weight, balance, and control) × 2 (dominant and non-dominant legs) mixed factor repeated ANOVA was performed. The results indicated that only balance training group showed an improvement in joint stiffness and both the training groups showed improvements in limb stability. Also, fall frequency results suggested that joint stiffness and limb stability had an effect on the likelihood of slip-induced falls. In conclusion, training can facilitate improvements in joint and limb control mechanism for older adults contributing to an improvement in the likelihood of slip-induced falls.

Model-based assessments of the effects of age and ankle fatigue on the control of upright posture in humans

The aim of this study was to investigate how and why age and localized muscle fatigue affect postural control using model-based simulations. A balance control model, based on an optimal control strategy, was used to simulate trials of quiet upright stance both pre-fatigue and following induced ankle plantarflexor fatigue. Empirical data were obtained from an earlier study that included both younger and older participants. Effects of age and ankle fatigue were determined from center-of-pressure (COP) measures and fitted model parameters. Though some discrepancies existed, the simulated effects of age and ankle fatigue were consistent with experimental findings in terms of trends in COP-based measures with age and ankle fatigue. Changes in both COP-based measures and model parameters were used to infer potential underlying causal mechanisms for the observed effects of age and ankle fatigue. For example, the model-based simulations indicated that sensory delay time increased with age and ankle fatigue by 31.1% and 2.9%, respectively, suggesting a potentially important role for such delay in postural control and fall risks.

A balance control model of quiet upright stance based on an optimal control strategy

Models of balance control can aid in understanding the mechanisms by which humans maintain balance. A balance control model of quiet upright stance based on an optimal control strategy is presented here. In this model, the human body was represented by a simple single-segment inverted pendulum during upright stance, and the neural controller was assumed to be an optimal controller that generates ankle control torques according to a certain performance criterion. This performance criterion was defined by several physical quantities relevant to sway. In order to accurately simulate existing experimental data, an optimization procedure was used to specify the set of model parameters to minimize the scalar error between experimental and simulated sway measures. Thirty-two independent simulations were performed for both younger and older adults. The model's capabilities, in terms of reflecting sway behaviors and identifying aging effects, were then analyzed based on the simulation results. The model was able to accurately predict center-of-pressure-based sway measures, and identify potential changes in balance control mechanisms caused by aging. Correlations between sway measures and model parameters are also discussed.

On use of a nominal internal model to detect a loss of balance in a maximal forward reach

We hypothesize that the CNS detects a loss of balance by comparing outputs predicted by a nominal, forward internal model with actual sensory outputs. When the resulting control error signal reaches an anomalously large value, this control error anomaly (CEA) signals a loss of balance and precedes any observable compensatory response. To test this hypothesis, a multi-input, multi-output internal model of a standing forward reach task was developed that incorporated on-line model identification and a Gaussian failure detection algorithm. Eleven healthy young women were then asked to stand and reach forward to a target positioned from 95 to 125% of their maximum reach distance. Kinematic and kinetic data were recorded at 100 Hz unilaterally from the upper body, leg, and foot. Evidence of successful CEA detection was a compensatory step between 100 ms and 2 s later. The results show that use of a threshold, set at 3 SD from the mean, on error in the control of leg segment acceleration detected a CEA and correctly predicted a compensatory response in 92.6% of 108 trials. Leg acceleration control error was a better predictor than upper body or foot acceleration control error (P = 0.000). CEA detection performed more reliably than loss of balance detection algorithms based on kinematic thresholds (P = 0.000). The results support the hypothesis that a loss of balance may be identified via the use of a nominal forward internal model and probabilistic error monitoring.