Dataset Information

Power output and fatigue properties using spatially distributed sequential stimulation in a dynamic knee extension task.

ABSTRACT: The low power output and fatigue resistance during functional electrical stimulation (FES) limits its use for functional applications. The aim of this study was to compare the power output and fatigue properties of spatially distributed sequential stimulation (SDSS) against conventional single electrode stimulation (SES) in an isokinetic knee extension task simulating knee movement during recumbent cycling.M. vastus lateralis and m. vastus medialis of eight able-bodied subjects were stimulated for 6 min on both legs with both setups. In the SES setup, target muscles were each stimulated by a pair of electrodes. In SDSS, four small electrodes replaced the SES active electrodes, but reference electrodes were the same. Torque was measured during knee extension movement by a dynamometer at an angular velocity of 110°/s. Mean power (P mean) was calculated from stimulated extensions for the first 10 extensions, the final 20 extensions and overall. Fatigue is presented as an index, calculated as the decrease with respect to initial power.P mean was significantly higher for SDSS than for SES in the final phase (9.9 ± 4.0 vs. 7.4 ± 4.3 W, p = 0.035) and overall (11.5 ± 4.0 vs. 9.2 ± 4.5 W, p = 0.037). With SDSS, the reduction in P mean was significantly smaller compared to SES (from 14.9 to 9.9 vs. 14.6 to 7.4 W, p = 0.024). The absolute mean pulse width was substantially lower with SDSS (62.5 vs. 90.0 µs).Although less stimulation was applied, SDSS showed a significantly higher mean power output than SES. SDSS also had improved fatigue resistance when compared to conventional stimulation. The SDSS approach may provide substantial performance benefits for cyclical FES applications.

SUBMITTER: Laubacher M

PROVIDER: S-EPMC5556133 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:ObjectiveThe objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algorithm that guarantees the convergence to a local minimum.Existing challengesDue to the nonconvexity of the problem, the search for a solution to OPF problems is not scalable, which makes the OPF highly limited for the system operation of large-scale real-world power grids-"the curse of dimensionality". The recent attempts at distributed computation aim for a scalable and efficient algorithm by reducing the computational cost per iteration in exchange of increased communication costs.MotivationA new network model allows for efficient computation without increasing communication costs. With the network model, recent advancements in distributed computation make it possible to develop an efficient and scalable algorithm suitable for large-scale OPF optimizations.MethodsWe propose a new network model in which all nodes are directly connected to the center node to keep the communication costs manageable. Based on the network model, we suggest a nodal distributed algorithm and direct communication to all nodes through the center node. We demonstrate that the suggested algorithm converges to a local minimum rather than a point, satisfying the first optimality condition.ResultsThe proposed algorithm identifies solutions to OPF problems in various IEEE model systems. The solutions are identical to those using a centrally optimized and heuristic approach. The computation time at each node does not depend on the system size, and Niter does not increase significantly with the system size.ConclusionOur proposed network model is a star network for maintaining the shortest node-to-node distances to allow a linear information exchange. The proposed algorithm guarantees the convergence to a local minimum rather than a maximum or a saddle point, and it maintains computational efficiency for a large-scale OPF, scalable algorithm.

Dataset Information

Power output and fatigue properties using spatially distributed sequential stimulation in a dynamic knee extension task.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets