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Joint neuromechanics arises from the complex interaction of dynamic, nonlinear elements including muscles, tendons, and neural circuits. The system has limited non-invasive observability and exhibits time-varying behaviour during functional tasks, which f


Joint neuromechanics arises from the complex interaction of dynamic, nonlinear elements including muscles, tendons, and neural circuits. The system has limited non-invasive observability and exhibits time-varying behaviour during functional tasks, which further necessitates the use of advanced data analysis and modeling techniques - reductionist and holistic (or system-level), physics-based and data-driven, time-invariant and time-varying, etc. Therefore, the human movement, and neuromechanics communities have developed and validated multiple softwares for analysis and modeling of their data. However, these tools are fragmented, lack standardization, and are often not intended to be used by the larger community. These limitations slow scientific progress as researchers often find themselves spending a significant amount of time writing the code to replicate the analysis of a scientific paper which is always prone to errors, or to use fragmented pieces of code written in different languages.
In this project we will address these problems by developing the Neuromechanics Identification Movement Analysis toolbox software. We will achieve this through a combination of a) developing new code, b) integrating Python packages that contain the required numerical and data analysis methods, c) porting code from other languages, d) making code customizations to address specific needs of neuromechanics data analysis, e) developing easy to use graphical user interface (GUI), and f) developing a neuromechanics data repository that will serve as a stepping stone to standardize maintenance and sharing of human movement and neuromechanics data for testing, validation and exploration purposes.

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