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Human Posture Transition-Time Detection Based upon Inertial Measurement Unit and Long Short-Term Memory Neural

Chun-Ting Kuo1, Jun-Ji Lin1, Kuo-Kuang Jen2

  • 1Department of Mechanical Engineering, National Taiwan University, Taipei 106319, Taiwan.

Biomimetics (Basel, Switzerland)
|October 27, 2023
PubMed
Summary
This summary is machine-generated.

This study used deep learning models, including Long Short-Term Memory (LSTM), to detect human posture changes and transitions. The LSTM model demonstrated high accuracy and speed, paving the way for advanced human-robot interaction applications.

Keywords:
deep learningfeedforward neural network (FNN)human activity recognition (HAR)human posture change detectioninertial measurement unit (IMU)internal sensinglong short-term memory (LSTM)

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Machine Learning and Deep Learning
  • Biomedical Engineering and Wearable Technology

Background:

  • Human-robot interaction is growing in industrial and clinical fields, increasing the need for precise human posture detection.
  • Existing research primarily focuses on recognizing human actions, largely overlooking the critical transitions between postures.
  • Detecting posture changes and transitions is vital for seamless human-robot collaboration and understanding human intention.

Purpose of the Study:

  • To investigate the efficacy of deep learning methods, specifically Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), for detecting human posture changes.
  • To analyze the capability of these models in identifying transition stages between different human movements (standing, walking, sitting).
  • To evaluate the impact of sampling rates on the performance of the LSTM network for posture change detection.

Main Methods:

  • Two deep learning models, FNN and LSTM, were employed to detect posture changes.
  • An inertial measurement unit (IMU) was worn on the subject's leg to collect joint parameter data for training and testing.
  • The study introduced transition stages as distinct features and examined various sampling rates for the LSTM model.

Main Results:

  • Both FNN and LSTM models achieved high detection accuracies for human posture changes.
  • The LSTM model significantly outperformed the FNN in both speed and accuracy, reaching 95% accuracy at 100 Hz sampling rate.
  • The trained LSTM network showed effectiveness in detecting posture changes across different subjects, indicating potential for generalized models.

Conclusions:

  • Deep learning models, particularly LSTM, are highly effective for rapid and accurate detection of human posture changes and transitions.
  • The study successfully demonstrated the feasibility of using IMU data and deep learning for human intention detection in real-time applications.
  • These findings provide a foundational contribution for engineering applications such as digital twins, exoskeletons, and advanced human intention control systems.