Normal human gait, described as passive dynamic walking, is neither completely passive nor always dynamic. In this article, we introduce the formulations of Passive Gait Measure (PGM) and Dynamic Gait Measure (DGM) that quantify passivity and dynamicity levels, respectively, of a given biped walking motion. The proposed concepts will be demonstrated through the analysis of human walking experimental data. The PGM measures the relative actuation contribution of the pivot joint of stance leg in the inverted pendulum analogy. The DGM, associated with gait stability, quantifies the effects of inertia in terms of the Zero-Moment Point (ZMP) and the ground projection of center of mass (GCOM). Human walking motion during single and double support phases is reconstructed from raw experimental data, and ZMP and GCOM trajectories during one full step cycle are generated. The calculated PGM values show the passive nature of human walking when the inverted pendulum analogy is adopted. The DGM results verify the dynamic nature of human walking demonstrating their dependence on the walking motion as well as the step phase; the double support phase results a static motion, opposite to the highly dynamic single support phase. The results will benefit the human gait studies and the development of walking robots.
- Dynamic Systems and Control Division
Experimental Analysis for Passive and Dynamic Gait Measures of Biped Walking
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Mummolo, C, Mangialardi, L, & Kim, JH. "Experimental Analysis for Passive and Dynamic Gait Measures of Biped Walking." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 25-32. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8673
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