Normal human walking typically consists of phases during which the body is statically unbalanced while maintaining dynamic stability. Quantifying the dynamic characteristics of human walking can provide better understanding of gait principles. We introduce a novel quantitative index, the dynamic gait measure (DGM), for comprehensive gait cycle. The DGM quantifies the effects of inertia and the static balance instability in terms of zero-moment point and ground projection of center of mass and incorporates the time-varying foot support region (FSR) and the threshold between static and dynamic walking. Also, a framework of determining the DGM from experimental data is introduced, in which the gait cycle segmentation is further refined. A multisegmental foot model is integrated into a biped system to reconstruct the walking motion from experiments, which demonstrates the time-varying FSR for different subphases. The proof-of-concept results of the DGM from a gait experiment are demonstrated. The DGM results are analyzed along with other established features and indices of normal human walking. The DGM provides a measure of static balance instability of biped walking during each (sub)phase as well as the entire gait cycle. The DGM of normal human walking has the potential to provide some scientific insights in understanding biped walking principles, which can also be useful for their engineering and clinical applications.

Understanding and mimicking human gait is essential for design and control of biped walking robots. The unique characteristics of normal human gait are described as passive dynamic walking, whereas general human gait is neither completely passive nor always dynamic. To study various walking motions, it is important to quantify the different levels of passivity and dynamicity, which have not been addressed in the current literature. In this paper, we introduce the initial formulations of Passive Gait Measure (PGM) and Dynamic Gait Measure (DGM) that quantify passivity and dynamicity, respectively, of a given biped walking motion, and the proposed formulations will be demonstrated for proof-of-concepts using gait simulation and analysis. The PGM is associated with the optimality of natural human walking, where the passivity weight functions are proposed and incorporated in the minimization of physiologically inspired weighted actuator torques. The PGM then measures the relative contribution of the stance ankle actuation. The DGM is associated with the gait stability, and quantifies the effects of inertia in terms of the Zero-Moment Point and the ground projection of center of mass. In addition, the DGM takes into account the stance foot dimension and the relative threshold between static and dynamic walking. As examples, both human-like and robotic walking motions during single support phase are generated for a planar biped system using the passivity weights and proper gait parameters. The calculated PGM values show more passive nature of human-like walking as compared with the robotic walking. The DGM results verify the dynamic nature of normal human walking with anthropomorphic foot dimension. In general, the DGMs for human-like walking are greater than those for robotic walking. The resulting DGMs also demonstrate their dependence on the stance foot dimension as well as the walking motion; for a given walking motion, smaller foot dimension results in increased dynamicity. Future work on experimental validation and demonstration will involve actual walking robots and human subjects. The proposed results will benefit the human gait studies and the development of walking robots.

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