Coordination of trunk and foot acceleration during gait is affected by walking velocity and fall history in elderly adults

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Background: Falling is a significant concern for many elderly adults but identifying individuals at risk of falling is difficult, and it is not clear how elderly adults adapt to challenging walking. Aims: The aim of the current study was to determine the effects of walking at non-preferred speeds on the coordination between foot and trunk acceleration variability in healthy elderly adults with and without fall history compared to healthy young adults. Methods: Subjects walked on a treadmill at 80% to 120% of their preferred walking speed while trunk and foot accelerations were recorded with wireless inertial sensors. Variability of accelerations were measured by root mean square, range, sample entropy, and Lyapunov exponent. The gait stability index was calculated using each variability metric in the frontal and sagittal plane by taking the ratio of trunk acceleration variability divided by foot acceleration variability. Results: Healthy young adults demonstrated larger trunk accelerations relative to foot accelerations at faster walking speeds compared to elderly adults, but both young and elderly adults show similar adaption to their acceleration regularity. Between group differences showed that elderly adult fallers coordinate acceleration variability between the trunk and feet differently compared to elderly non-fallers and young adults. Discussion: The current results indicate that during gait, elderly fallers demonstrate more constrained, less adaptable trunk movement relative to their foot movement and this pattern is different compared to elderly non-fallers and healthy young. Conclusions: Coordination between trunk and foot acceleration variability plays an important role in maintaining stability during gait.NIH T32 HD057850Frontiers Pilot and Collaborative Studies Funding Program (UL1TR000001)School of Health Professions Pilot Research Gran

The influence of peripheral neuropathy on walking kinematics and physical function

The 108th Congress (2005) has reported that 20 million U.S. citizens suffer from Peripheral Neuropathy (PN). Characterized by sensory nerve deterioration, PN reduces somatosensation (Padua et al., 2005) and increases the risk of fall-related injury (Richardson et al., 1992). The purpose of this dissertation was to provide insight into 1) the effects of acute loss of foot sole sensation on locomotor system health, 2) the effects of PN on locomotor system health, and 3) the underlying impairments associated with reduced physical function within the older adult and PN populations. Locomotor system health was assessed by the magnitude of stride-to-stride variability and local instability contained in the kinematics of treadmill walking. In healthy young adults, ice-induced reduction of foot sole sensation did not alter the magnitude of stride-to-stride variability during treadmill walking. It did, however, increase lower-extremity joint local instability, or the sensitivity to small scale perturbations. Compared to controls, individuals with PN walked with similar local instability yet increased variability, at relatively slow speeds. When walking at relatively fast speeds, individuals with PN exhibited exaggerated increases in local instability. In healthy older adults, locomotion-based physical function (LBPF), as defined by 6-minute walk and Timed Up-and-Go performance, was correlated to leg strength and measures of locomotor system health. However, only measures of locomotor system health provided independent predictive information of LBPF. The PN group exhibited reduced LBPF. As opposed to healthy old adults, correlates of LBPF were not leg strength but instead standing balance variables. Multiple variables of leg strength, standing balance, and locomotor system health provided independently predictive information regarding each test of LBPF. The opposing effects of ice-induced reduction in foot sole sensation and PN on locomotor system health suggest that the chronic nature of PN allows for the implementation of partially effective compensatory strategies. Yet, the inability to adapt to relatively fast speeds suggests that falls likely occur during challenging situations. The fundamentally different correlates and predictors of LBPF between older adults and those with PN highlight the uniqueness of the movement disorder associated with PN

Identification of Motion Controllers In Human Standing And Walking

The method of trajectory optimization with direct collocation has the potential to extract generalized and realistic motion controllers from long duration movement data without requiring extensive measurement equipment. Knowing motion controllers not only can improve clinic assessments on locomotor disabilities, but also can inspire the control of powered exoskeletons and prostheses for better performance. Three aims were included in this dissertation. Aim 1 was to apply and validate the trajectory optimization for identification of the postural controllers in standing balance. The trajectory optimization approach was first validated on the simulated standing balance data and demonstrated that it can extract the correct postural control parameters. Then, six types of postural feedback controllers, from simple linear to complex nonlinear, were identified on six young adults’ motion data that was collected in a standing balance experiment. Results indicated that nonlinear controllers with multiple time delay paths can best explain their balance motions. A stochastic trajectory optimization approach was proposed that can help finding practically stable controllers in the identification process. Aim 2 focused on the foot placement control in walking. Foot placement controllers were successfully identified through the trajectory optimization method on nine young adults’ perturbed walking motions. It was shown that a linear controller with pelvis position and velocity feedback, suggested by the linear inverted pendulum model, was not sufficient to explain their foot placement among multiple walking speeds. Nonlinear controllers or more feedback signals, such as pelvis acceleration, are needed. Foot placement control was applied on a powered leg exoskeleton to control its legs’ swing motion. Two healthy participants were able to achieve stable walking with the controlled exoskeleton. v Results suggested that the foot placement controller helped decelerate the swing motion at late swing. In Aim 3, the trajectory optimization method was used to identify joint impedance properties in walking. Results of the synthetic study showed that relatively close impedance parameters can be identified. Then, a preliminary study was done to identify the ankle joint impedance properties of two participants at two walking speeds. The identified impedance properties were close to previous studies and consistent between different participants and walking speeds

Impaired local dynamic stability during treadmill walking predicts future falls in patients with multiple sclerosis:A prospective cohort study

Background: Falling is a significant problem in patients with multiple sclerosis (MS) and the majority of falls occur during dynamic activities. Recently, there have been evidences focusing on falls and local stability of walking based on dynamic system theory in the elderly as well as patients with cerebral concussion. However, in patient with MS, this relationship has not been fully investigated. The aim of this study was to investigate local stability of walking as a risk factor for falling in patients with MS. Methods: Seventy patients were assessed while walking at their preferred speed on a treadmill under single and dual task conditions. A cognitive task (backward counting) was used to assess the importance of dual tasking to fall risk. Trunk kinematics were collected using a cluster marker over the level of T7 and a 7-camera motion capture system. To quantify local stability of walking, maximal finite-time Lyapunov exponent was calculated from a 12-dimensional state space reconstruction based on 3-dimensional trunk linear and angular velocity time series. Participants were classified as fallers (≥1) and non-fallers based on their prospective fall occurrence. Findings: 30 (43%) participants recorded ≥1 falls and were classified as fallers. The results of multiple logistic regression analysis revealed that short-term local dynamic stability in the single task condition (P < 0.05, odds ratio = 2.214 (1.037–4.726)) was the significant fall predictor. Interpretation: The results may indicate that the assessment of local stability of walking can identify patients who would benefit from gait retraining and fall prevention programs

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

A Method to Concatenate Multiple Short Time Series for Evaluating Dynamic Behaviour During Walking

Gait variability is a sensitive metric for assessing functional deficits in individuals with mobility impairments. To correctly represent the temporal evolution of gait kinematics, nonlinear measures require extended and uninterrupted time series. In this study, we present and validate a novel algorithm for concatenating multiple time-series in order to allow the nonlinear analysis of gait data from standard and unrestricted overground walking protocols. The fullbody gait patterns of twenty healthy subjects were captured during five walking trials (at least 5 minutes) on a treadmill under different weight perturbation conditions. The collected time series were cut into multiple shorter time series of varying lengths and subsequently concatenated using a novel algorithm that identifies similar poses in successive time series in order to determine an optimal concatenation time point. After alignment of the datasets, the approach then concatenated the data to provide a smooth transition. Nonlinear measures to assess stability (Largest Lyapunov Exponent, LyE) and regularity (Sample Entropy, SE) were calculated in order to quantify the efficacy of the concatenation approach using intra-class correlation coefficients, standard error of measurement and paired effect sizes. Our results indicate overall good agreement between the full uninterrupted and the concatenated time series for LyE. However, SE was more sensitive to the proposed concatenation algorithm and might lead to false interpretation of physiological gait signals. This approach opens perspectives for analysis of dynamic stability of gait data from physiological overground walking protocols, but also the re-processing and estimation of nonlinear metrics from previously collected datasets

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller