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A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition.

David Hollinger1, Ryan S Pollard1, Mark C Schall2

  • 1Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.

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Summary
This summary is machine-generated.

Predicting future joint angles using wearable sensors is improved by using an action-generic model trained on diverse human movements, outperforming hierarchical methods for better movement intent recognition.

Keywords:
accelerometersgyroscopesmovement intent predictionwearable sensors

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Area of Science:

  • Biomechanics and Human Movement Analysis
  • Wearable Sensor Technology
  • Machine Learning for Predictive Modeling

Background:

  • Wearable inertial measurement units (IMUs) are increasingly used for human movement prediction.
  • Current methods often focus on action-level or joint-level motion prediction.
  • Contextual information is crucial for comprehensive movement intent recognition.

Purpose of the Study:

  • To develop and evaluate a novel hierarchical method combining action-level classification and joint-level regression for predicting future joint angles.
  • To compare the performance of a hierarchical approach with a joint-level action-generic model.
  • To assess the efficacy of different machine learning models (KNN, BiLSTM, TCN, Random Forest) for movement prediction.

Main Methods:

  • A hierarchical approach using KNN, BiLSTM, or TCN for action classification and Random Forest for joint-level regression.
  • Development of an action-generic Random Forest model trained on data from multiple actions (backward walking, kneeling, running, walking).
  • Prediction of joint angles 100 ms into the future using IMU data.

Main Results:

  • The action-generic model demonstrated lower prediction error for specific actions (backward walking, kneeling down, kneeling up) compared to the hierarchical approach.
  • While TCN and BiLSTM achieved high classification accuracies, they did not outperform the combined action-specific Random Forest model.
  • The action-generic model's superior performance may be attributed to training on a larger, more diverse dataset.

Conclusions:

  • Leveraging large, disparate data sources in an action-generic model offers advantages over hierarchical approaches for joint-level prediction.
  • An IMU-driven, task-agnostic model is effective for predicting future joint angles across various human movements.
  • This study highlights the potential of generalized models for advanced human movement intent recognition.