Incorporating Manual and Robotic Locomotor Training into Clinical Practice: Suggestions for Clinical Decision Making
Deborah Backus, Candace Tefertiller
- Year
- 2008
- Citations
- 11
- Access
- Open access
Abstract
Technological advances afford individuals with spinal cord injury (SCI) the ability to improve locomotor function through the use of body weight—supported (BWS) treadmill training. Rehabilitation providers now have the choice of BWS manual and robotic (i.e., Lokomat/Autoambulator) locomotor training (LT) systems to provide training in a safe treadmill environment. Several lines of evidence suggest that individuals with motor incomplete SCI who participate in intense LT can improve walking abilities and that these changes can be maintained for several months to years post LT. Health-related benefits of LT include increases in cross-sectional area of muscle, improved perception of health and physical capacity, and ultimately improved quality of life. Clinicians are challenged in making sound clinical decisions about which device to use, or in which order if both are available, due to the paucity of research comparing the manual and robotic systems for neural, functional, or health-related benefits after SCI. This article discusses evidence related to the manual and robotic systems for LT and describes the application of this evidence in developing a clinical decision-making algorithm for the use of manual and robotic LT in a postacute rehabilitation program for individuals with incomplete SCI.
Keywords
Related papers
Artificial intelligence: a modern approach
1995
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
2012
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham +17 more
2016
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller +1 more
2013