We examined how the application of a forward horizontal force applied at the waist alters the metabolic cost, kinematics, and external work of gait. Horizontal assist forces of 4%, 8% and 12% of a subject’s body weight were applied via our testing apparatus while subjects walked at comfortable walking speed on a level treadmill. Kinematic and metabolic parameters were measured using motion capture and ergospirometry respectively on a group of 10 healthy male subjects. Changes in kinematic and metabolic parameters were quantified and found similar to walking downhill at varying grades. A horizontal assist force of 8% resulted in the greatest reduction of metabolic cost. Changes in recovery factor, external work, and center of mass (COM) movement did not correlate with changes in metabolic rate and therefore were not driving the observed reductions in cost. The assist force may have performed external work by providing propulsion as well as raising the COM as it pivots over the stance leg. Assist forces may decrease metabolic cost by reducing the concentric work required for propulsion while increasing the eccentric work of braking. These findings on the effects of assist forces suggest novel mobility aids for individuals with gait disorders and training strategies for athletes.
Christopher A. Zirker, Bradford C. Bennett, and Mark F. Abel
Travis T. Simpson, Susan L. Wiesner, and Bradford C. Bennett
The current means of locating specific movements in film necessitate hours of viewing, making the task of conducting research into movement characteristics and patterns tedious and difficult. This is particularly problematic for the research and analysis of complex movement systems such as sports and dance. While some systems have been developed to manually annotate film, to date no automated way of identifying complex, full body movement exists. With pattern recognition technology and knowledge of joint locations, automatically describing filmed movement using computer software is possible. This study used various forms of lower body kinematic analysis to identify codified dance movements. We created an algorithm that compares an unknown move with a specified start and stop against known dance moves. Our recognition method consists of classification and template correlation using a database of model moves. This system was optimized to include nearly 90 dance and Tai Chi Chuan movements, producing accurate name identification in over 97% of trials. In addition, the program had the capability to provide a kinematic description of either matched or unmatched moves obtained from classification recognition