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Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices.

Nili E Krausz1,2, Blair H Hu1,2, Levi J Hargrove1,2,3

  • 1Neural Engineering for Prosthetics and Orthotics Lab (NEPOL), Center of Bionic Medicine, Shirley Ryan AbilityLab (formerly RIC), Chicago, IL 60611, USA.

Sensors (Basel, Switzerland)
|November 14, 2019
PubMed
Summary

Researchers improved powered lower limb prostheses by integrating environmental vision data. This reduces prediction errors caused by subject variability, enhancing safety and performance in prosthetic control systems.

Keywords:
assistive roboticscomputer visionenvironmental sensingintention detectionprostheticssensor fusion

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Human-Computer Interaction

Background:

  • Powered lower limb prostheses aim to improve gait control using predictive algorithms based on electromyography (EMG), kinetics, and kinematics.
  • Current prediction systems suffer from significant errors due to inter- and intra-subject variability in these data sources, potentially causing falls.
  • Integrating environmental data is proposed to reduce variability and improve prediction accuracy.

Purpose of the Study:

  • To investigate the impact of incorporating environmental data on the prediction accuracy of powered lower limb prostheses.
  • To analyze the variability of different sensor modalities (kinetics, kinematics, EMG, environmental vision) across subjects and activities.
  • To determine the optimal combination of sensor data for robust prosthetic control.

Main Methods:

  • Analyzed intra-activity and intra-subject variability of normalized sensor data, including EMG, kinetics, kinematics, and environmental data from a depth sensor.
  • Computed measures of separability, repeatability, clustering, and overall desirability for each sensor modality.
  • Evaluated feature combinations for their effectiveness in prosthetic control prediction.

Main Results:

  • Environmental vision data exhibited lower variability across trials and subjects compared to kinetics, kinematics, and EMG.
  • Combining features from Vision, EMG, Inertial Measurement Unit (IMU), and Goniometer yielded the highest separability, repeatability, clustering, and desirability.
  • The proposed multimodal approach demonstrated superior performance across diverse subjects and activities.

Conclusions:

  • Environmental vision data significantly reduces prediction errors in powered lower limb prostheses by mitigating subject-specific variability.
  • A multimodal sensor fusion approach, integrating Vision, EMG, IMU, and Goniometer data, is highly effective for robust prosthetic control.
  • This approach offers a promising pathway for developing more reliable and safer powered lower limb prostheses and exoskeletons.