Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing

Research output: Contribution to journalConference articleContributedpeer-review

Contributors

Abstract

Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction. Portable devices such as inertial measurement units (IMUs) are applicable to monitor human motions, while in practice often limited computational power is available locally. The human motion in task space coordinates requires not only the human joint motion but also the nonlinear coordinate transformation depending on the parameters such as human limb length. In most applications, measuring these kinematics parameters for each individual requires undesirably high effort. Therefore, it is desirable to estimate both, the human motion and kinematic parameters from IMUs. In this work, we propose a novel computational framework for dual estimation in real-time exploiting in-network computational resources. We adopt the concept of field Kalman filtering, where the dual estimation problem is decomposed into a fast state estimation process and a computationally expensive parameter estimation process. In order to further accelerate the convergence, the parameter estimation is progressively computed on multiple networked computational nodes. The superiority of our proposed method is demonstrated by a simulation of a human arm, where the estimation accuracy is shown to converge faster than with conventional approaches.

Details

Original languageEnglish
Pages (from-to)8875-8882
Number of pages8
Journal IFAC-PapersOnLine
Volume56
Issue number2
Publication statusPublished - 1 Jul 2023
Peer-reviewedYes

Conference

Title22nd World Congress of the International Federation of Automatic Control
Abbreviated titleIFAC 2023
Conference number22
Duration9 - 14 July 2023
Website
Degree of recognitionInternational event
LocationPacific Convention Plaza Yokohama
CityYokohama
CountryJapan

External IDs

ORCID /0000-0001-7008-1537/work/158767450
ORCID /0000-0001-8469-9573/work/161891086

Keywords

ASJC Scopus subject areas

Keywords

  • dual estimation, human motion estimation, IMU, Kalman filtering, networked system, progressive algorithm