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Anatomical Movements

Anatomical Movements

Anatomical movements refer to the various actions or motions that can be performed by the body's joints and muscles. These movements are described using specific terms to provide a standardized way of discussing and understanding the range of motion at different joints.
Here are some common anatomical movements:
Flexion and extension motions are in the sagittal (anterior–posterior) plane of motion. These movements take place at the shoulder, hip, elbow, knee, wrist,...

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Related Experiment Video

Updated: Oct 25, 2025

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

Functional Data Representation of Inertial Sensor-based Torso-Thigh, Knee, and Ankle Movements during Lifting.

Sol Lim¹, Clive D'Souza²

¹Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ, USA.

Applied Human Factors and Ergonomics Conference

|August 6, 2021

Summary

A sigmoid function accurately models the movement patterns of two-handed lifting. This mathematical approach simplifies analysis of lifting kinematics for machine learning applications.

Keywords:

Curve-fitting Functional data Lifting kinematics Wearable sensing

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Last Updated: Oct 25, 2025

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Area of Science:

Biomechanics
Ergonomics
Human Movement Science

Background:

Two-handed anterior lifting is a common occupational task.
Understanding lifting kinematics is crucial for injury prevention and performance optimization.
Time-series data from motion capture present challenges for analysis.

Purpose of the Study:

To evaluate the goodness-of-fit of a sigmoid function for characterizing time-series angular displacement trajectories during two-handed anterior lifting.
To assess the functional representation of lifting kinematics for data aggregation and feature extraction.

Main Methods:

Twenty-six participants performed two-handed anterior lifts with varying loads (4.5 kg vs. 22.7 kg) and heights (floor vs. knee).
Body-worn inertial sensors captured sagittal plane kinematics: torso-thigh angle, knee flexion-extension (F-E), and ankle F-E angles.
A three-parameter sigmoid function was fitted to the angular displacement data.

Main Results:

The sigmoid function demonstrated a good fit to the measured kinematic trajectories.
Mean Root Mean Square Errors (RMSE) were 3.6±2.9° (torso-thigh), 3.9±4.2° (knee F-E), and 2.7±2.8° (ankle F-E).
These results indicate the sigmoid function adequately describes the shape of lifting kinematics.

Conclusions:

The sigmoid function provides a suitable mathematical model for the time-series angular displacement during two-handed lifting.
Functional data representation using sigmoid functions can enhance data processing for large datasets in motion analysis and machine learning.
This approach facilitates feature extraction and data aggregation in biomechanical studies.