Quantifying Dynamic Characteristics of Human Walking for Comprehensive Gait Cycle

Abstract

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.

Instantaneous Metabolic Cost of Walking: Joint-Space Dynamic Model with Subject-Specific Heat Rate

“…As another validation aspect, the consistently smaller COT in SS than that in DS is also in agreement with the trade-off between efficiency (as the inverse of COT) and stability of normal human walking [50] in the sagittal plane, where the SS phases are relatively unstable with respect to static [37] and dynamic balance [89]. The contrasting features of the efficiency-stability compromise in the relatively unstable SS versus the relatively stable DS were not available through the percent MEE rates (Fig 5) alone, but through (the inverse of) the phase-specific COT.…”

“…Furthermore, the maximum-torque-angle-velocity relationships in joint space [33,34] are simpler than the maximum-force-length-velocity relationships in muscle space [24,27]. For these reasons, joint-space formulations are commonly used in inverse dynamics for motion analysis [26,35–37] and computational simulation and optimization [38–40]. Several recent studies have demonstrated that MEE during walking can be predicted using joint-space kinematic and kinetic data [41,42].…”

Gait in children and adolescents with idiopathic musculoskeletal pain

“…Dynamic baropodometry, NWS system, allows precise identification of a series of gait abnormalities. For this reason, it has been used in the evaluation of patients with varied diseases, such as neurological, orthopedic, rheumatic, and even physiological, as in the case of longevity [9,[15][16][17][18][19][20][21]. However, some authors have evaluated the gait of individuals with musculoskeletal pain with other reliable methods, especially fibromyalgia patients.…”

Improved determination of dynamic balance using the centre of mass and centre of pressure inclination variables in a complete golf swing cycle

“…Recently, COM-COP inclination angle, which is determined as the instantaneous orientation of the line connecting the COM and COP with respect to a vertical line through COP, has been proposed for the quantification of dynamic stability during a gait (Lee & Chou, 2006). Although the COM trajectories could leave the foot support region that always includes the COP location during a dynamic task Mummolo, Mangialardi, & Kim, 2013), greater COM-COP inclination angle allow increased moment arm to generate inertia and momentum of the body segments for balance recovery (Hsue, Miller, & Su, 2009b). It has been identified as a valuable parameter in a variety of studies (Chien, Lu, & Liu, 2013Hsu et al, 2010;Huang, Lu, Chen, Wang, & Chou, 2008).…”