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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Related Experiment Video

Updated: Oct 7, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization.

Umme Zakia1, Carlo Menon1,2

  • 1Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary

Transfer learning with a supervised force myography deep transfer learner (SFMG-DTL) model accurately estimates applied forces in human-robot interactions. This approach enhances force estimation when target training data is limited, improving HRI experiences.

Keywords:
applied force estimation in dynamic motiondomain adaptationdomain generalizationforce myography techniquepretrained modeltransfer learning

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Area of Science:

  • Robotics
  • Machine Learning
  • Biomedical Engineering

Background:

  • Force myography (FMG) is effective for estimating applied force in human-robot interactions (HRI).
  • Current data-driven models require extensive training and evaluation within the same session, which is often impractical.
  • Transfer learning offers a promising solution for rapid force estimation through fine-tuning pretrained models.

Purpose of the Study:

  • To develop and evaluate a unified supervised FMG-based deep transfer learner (SFMG-DTL) model.
  • To assess the model's performance in estimating forces across different target domains using supervised domain adaptation (SDA) and supervised domain generalization (SDG).
  • To investigate the effectiveness of fine-tuning with limited target training data.

Main Methods:

  • A CNN-based SFMG-DTL model was pretrained using multi-session FMG source data.
  • The model was evaluated for force estimation in separate target domains under intra-subject (SDA) and cross-subject (SDG) conditions.
  • Supervised domain adaptation and generalization techniques were employed.

Main Results:

  • The SFMG-DTL model achieved high estimation accuracies (R² ≥ 88%) and low errors (NRMSE ≤ 0.6) in both SDA and SDG evaluations.
  • Fine-tuning with minimal target training data effectively calibrated the model for target adaptation.
  • The model demonstrated robust performance across different target domains.

Conclusions:

  • The proposed SFMG-DTL model effectively estimates forces in HRI using transfer learning.
  • This approach significantly improves force estimation in scenarios with limited target training data or when rapid adaptation is needed.
  • The findings suggest transfer learning can enhance daily HRI experiences.