English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The behavioral richness exhibited in natural humanmotion results from the complex interplay of biomechanical and neurological factors. The biomechanical factors involve the kinematics and dynamics of the musculoskeletal system while the neurological factors involve the sensorimotor integration performed by the central nervous system (CNS). An adequate understanding of these factors is a prerequisite to understanding the overall effect on human motion as well as providing a means for synthesizing human motion. The fields of neuroscience, biomechanics, robotics, and computer graphics provide motivation, as well as tools, for understanding human motion. In neuroscience, fundamental scientific understanding drives the motivation to understand human motion, whereas, in biomechanics, clinical applications often form the driving motivation. These clinical applications involve the use of movement analysis and simulation tools to help direct patient rehabilitation as well as predict the effects of surgery on movement. In addition to the clinical desire to analyze movement there has been an emerging desire in recent years to synthetically generate human-like motion in both simulated and physical settings. In computer graphics this desire is directed toward autonomously generating realistic motion for virtual actors. The intent is to direct these virtual actors using high-level goal directed commands for which low-level motion control is automatically generated. Motivated by similar desires, the robotics community seeks a high-level control framework for robotic systems. With the recent advent of complex humanoid robots this challenge has grownmore demanding. Consistent with their anthropomorphic design, humanoid robots are intended to operate in a human-like manner within man-made environments and to promote interaction with their biological counterparts. To achieve this, common control strategies have involved generating joint space trajectories or learning specific motions, but these approaches require extensive motion planning computations and do not generalize well to related tasks.

A Task-Level Biomechanical Framework for Motion Analysis and Control Synthesis