Human Modelling

A cartoon of a person standing. The limb segments are shown in different colours, with the corresponding masses and inertias shown. These parameters can be estimated via allometric scaling or estimated using Dynamic Identification Modelling (DIM).

Everyone is unique. This can be seen in subtle variations in limb length, increased strength due to exercise, or limitations in mobility due to illness and injury. People change over the course of their lifetimes, leading to nuances in their performance. By tracking and understanding these nuances, it is possible to individualise clinical interventions, track intervention efficacy, and prescribe the design of assistive devices.


[2018] Tracking Kinematic and Kinetic Measures of Sit to Stand Using an Instrumented Spine Orthosis (accepted)
Robert, Peter Matthew*, Sarah Seko, Jeannie Bailey, Ruzena Bajcsy, Jeffrey Lotz

[2018] A functional method for generating individualized spine models from motion-capture data (accepted)
Sarah Seko*, Robert Peter Matthew, Ruzena Bajcsy, Jeffrey Lotz

[2018] A sEMG Classification Framework with Less Training Data (accepted)
Daisuke Kaneishi*, Robert Peter Matthew, Masayoshi Tomizuka

[2017] Fusing motion-capture and inertial measurements for improved joint state recovery: An application for sit-to-stand actions (link)
Robert Peter Matthew, Sarah Seko, Ruzena Bajcsy

[2016] Generating physically realistic kinematic and dynamic models from small data sets: An application for sit-to-stand actions (link)
Robert Peter Matthew
, Victor Shia, Gentiane Venture, Ruzena Bajcsy

[2015] Optimal design for individualised passive assistance (link)
Robert Peter Matthew, Victor Shia, Masayoshi Tomizuka, Ruzena Bajcsy

[2014] Calculating Reachable Workspace Volume for use in Quantitative Medicine (link)
Robert Peter Matthew, Gregorij Kurillo, Jay J Han, Ruzena Bajcsy