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There remains a pressing need for a stability metric that can reliably identify fall susceptibility during walking, enabling more effective gait rehabilitation for reduced fall incidence. One available metric is the maximum margin of stability (MOSmax), which is calculated using the body’s center of mass (COM) position and velocity along with the location of the maximum center of pressure (COPmax). However, MOSmax has several limitations that may limit stability assessment. Specifically, the assumptions of a fixed COP and constant ground reaction force (GRF) are not applicable to gait. To address these limitations, a modified MOS equation that allows for a variable COP and is not dependent on a constant GRF is presented here. The modified MOS was significantly lower than MOSmax throughout a significant portion of single limb support for normal walking gait. This finding indicates the MOSmax metric may lack sensitivity to instability as it may still be positive when the actual MOS indicates existing or impending instability. This comparison also showed that the MOS might offer additional information about walking stability relevant to gait assessment for fall prevention and rehabilitation. However, like other stability metrics, this capability must be established with further investigations of perturbed and pathological gait.

A number of studies have examined the synchronization of central and peripheral spike trains by applying signal analysis techniques in the time and frequency domains. These analyses can reveal the presence of one or more common neural inputs that produce synchronization. However, synchronization measurements can fluctuate significantly due to the inherent variability of neural discharges and a finite data record length. Moreover, the effect of these natural variations is further compounded by the number of parameters available for calculating coherence in the frequency domain and the number of indices used to quantify short-term synchronization (STS) in the time domain. The computational tool presented here provides the user with an interactive environment that dynamically calculates and displays spike train properties along with STS and coherence indices to show how these factors interact. It is intended for a broad range of users, from those who are new to synchronization to experienced researchers who want to develop more meaningful and effective computational and experimental studies. To ensure this freely available tool meets the needs of all users, there are two versions. The first is a stand-alone version for educational use that can run on any computer. The second version can be modified and expanded by researchers who want to explore more in-depth questions about synchronization. Therefore, the distribution and use of this tool should both improve the understanding of fundamental spike train synchronization dynamics and produce more efficient and meaningful synchronization studies.

We confirm previous findings that as the pension plan size increases, administrative costs per participant and asset fall after controlling for the proportion of retirees in a plan. However, we question the inference that the high costs of administering small plans partly explains why small firms generally do not sponsor pension plans. This study uses Internal Revenue Service annual data to compare the administrative costs of Taft-Hartley plans from 1981 to 1993. Centralization may save costs but also may inhibit formation. Union plans are naturally small; they form at the local level.

Falls during walking are a major contributor to accidental deaths and injuries that can result in debilitating hospitalization costs, lost productivity, and diminished quality of life. To reduce these losses, we must develop a more profound understanding of the characteristic responses to perturbations similar to those encountered in daily life. This study addresses this issue by building on our earlier studies that examined mechanical and visual perturbations in the same environment by applying the same continuous pseudo-random perturbations at multiple (3 mechanical, 5 visual) amplitudes. Walking variability during mechanical perturbations increased significantly with amplitude for all subjects and differences as measured by variabilities of step width, COM position, and COM velocity. These parameters were the only ones sensitive to the presence of visual perturbations, but none of them changed significantly with perturbation amplitude. Additionally, visual perturbation effects were far less consistent across participants, with several who were essentially unaffected by visual perturbations at any level. The homogeneity of the mechanical perturbation effects demonstrates that human responses to mechanical perturbations are similar because they are driven by kinetics that require similar corrections that must be made in order to maintain balance. Conversely, responses to visual perturbations are driven by the perceived need to make corrections and this perception is not accurate enough to produce amplitude-related corrections, even for a single participant, nor is this perception consistent across individuals. This latter finding is likely to be relevant to future visual perturbation studies and the diagnosis and rehabilitation of gait and balance disorders.

Compared to static balance, dynamic balance requires a more complex strategy that goes beyond keeping the center of mass (COM) within the base of support, as established by the range of foot center of pressure (COP) displacement. Instead, neuromechanics must accommodate changing support conditions and inertial effects. Therefore, because they represent body's position and changes in applied moments, relative COM and COP displacements may also reveal dynamic postural strategies. To investigate this concept, kinetics and kinematics were recorded during three 12 cm, 1.25 Hz, sagittal perturbations. Forty-one individual trials were classified according to averaged cross-correlation lag between COM and COP displacement (lag(COM:COP)) and relative head-to-ankle displacement (Δ(head)/Δ(ankle)) using a k-means analysis. This process revealed two dominant patterns, one for which the lag(COM:COP) was positive (Group 1 (n=6)) and another for which it was negative (Group 2 (n=5)) . Group 1 (G1) absorbed power from the platform over most of the cycle, except during transitions in platform direction. Conversely, Group 2 (G2) participants applied power to the platform to maintain a larger margin between COM and COP position and also had larger knee flexion and ankle dorsiflexion, resulting in a lower stance. By the third repetition, the only kinematic differences were a slightly larger G2 linear knee displacement (p=0.008) and an antiphasic relationship of pelvis (linear) and trunk (angular) displacements. Therefore, it is likely that the strategy differences were detected by including COP in the initial screening method, because it reflects the pattern of force application that is not detectable by tracking body movements.