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Related Experiment Video

Updated: Jun 25, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Motion segmentation method for hybrid characteristic on human motion.

Newman Lau¹, Ben Wong, Daniel Chow

¹Multimedia Innovation Centre, School of Design, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

Journal of Biomechanics

|February 10, 2009

Summary

This study introduces a novel two-pass algorithm for segmenting human joint motion without prior knowledge. The method effectively classifies diverse motion types for improved ergonomic and safety analysis.

Related Experiment Videos

Last Updated: Jun 25, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Area of Science:

Biomechanics
Human-Computer Interaction
Ergonomics

Background:

Repetitive motion is linked to musculoskeletal disorders, necessitating accurate motion analysis for safety.
Existing motion segmentation methods often require prior knowledge of motion characteristics, limiting their application.
There is a need for versatile motion segmentation techniques applicable to various human movements.

Purpose of the Study:

To develop and present a novel two-pass algorithm for segmenting human joint motion.
To enable motion classification and analysis without requiring prior knowledge of motion properties.
To provide parameters for ergonomic evaluation, postural safety, and human factors studies.

Main Methods:

A two-pass algorithm is employed for motion segmentation.
Recursive Least Square (RLS) is used in the first pass to estimate motion segments.
Period identification and further segmentation refine the segments, which are then modeled using a damped harmonic model.

Main Results:

The method successfully segments human joint motion without prior assumptions on its characteristics.
The algorithm can handle periodic, random, and mixed motion patterns.
Extracted parameters (frequency, amplitude, duration) are suitable for ergonomic and safety evaluations.

Conclusions:

The proposed method offers a robust approach to segmenting and analyzing human motion across various types.
This technique has potential applications in repetitive workflow analysis, sports movement analysis, and task safety evaluation.
The ability to analyze motion without prior knowledge enhances the applicability of motion segmentation in human factors research.