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Optimizing Sensor and Data Selection on Lower Limbs via Deep Learning for Real-Time Human Activity Recognition.

Zihang You1, Neethan Ratnakumar1, Bo Shen2

  • 1Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA.

IEEE Transactions on Human-Machine Systems
|June 4, 2026
PubMed
Summary

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This summary is machine-generated.

Optimizing human activity recognition (HAR) for exoskeletons requires balancing accuracy and sensor complexity. Bilateral joint angles and inertial sensors offer high accuracy for real-time control.

Area of Science:

  • Robotics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Real-time human activity recognition (HAR) is vital for adaptive control in lower limb exoskeletons.
  • Current systems face challenges in balancing accuracy, latency, and sensor complexity.
  • Deep learning models offer potential for advanced HAR but require careful sensor selection.

Purpose of the Study:

  • To systematically evaluate sensor combinations and data modalities for real-time HAR.
  • To assess the trade-offs between accuracy, latency, and model complexity using deep neural networks.
  • To establish design principles for HAR-driven control in assistive robotics and mobile health.

Main Methods:

  • Utilized a dataset from 21 subjects performing six locomotion activities.
Keywords:
Human activity recognitiondeep learninglocomotion modeneural networksensor selection

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  • Employed deep learning models including MLP, LSTM, and CNN-LSTM with 50 ms sliding windows.
  • Assessed various sensor combinations: joint angles, derived angular velocities, and inertial measurements.

    • Main Results:

    • Bilateral joint angles (hip, knee, ankle) achieved 98.98% accuracy, outperforming unilateral setups.
    • Adding a thigh-mounted IMU increased accuracy to 99.23% through multimodal sensor fusion.
    • Derived joint angular velocities enhanced accuracy by up to 15%, with minimal configurations (bilateral hip + velocities) reaching over 94%.

    Conclusions:

    • Bilateral sensor configurations, especially with derived angular velocities, provide high accuracy for HAR.
    • Multimodal sensor fusion, including IMUs, further improves recognition performance.
    • Findings offer practical solutions and design principles for low-power, HAR-driven assistive robotic systems.