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Related Experiment Video

Updated: Aug 22, 2025

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning.

Mohamed Karakish1,2, Moustafa A Fouz1, Ahmed ELsawaf1

  • 1Mechanical Engineering Department, College of Engineering and Technology, Cairo Campus, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo 11757, Egypt.

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

Simple Artificial Neural Networks (ANNs) were deployed on a microcontroller for active prosthesis control. Convolutional Neural Networks (CNNs) showed better accuracy with limited data, while Multi-Layered Perceptrons (MLPs) offered faster processing.

Keywords:
CNNESP32MLPTensorFlow Lite microdeep learningembedded systemgait trajectory predictionmicrocontroller

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Area of Science:

  • Robotics and Artificial Intelligence
  • Biomedical Engineering
  • Embedded Systems

Background:

  • Active prosthesis control is complex due to gait trajectory kinematics.
  • Artificial Neural Networks (ANNs) are effective for gait trajectory generation but computationally intensive.
  • Deploying complex ANNs on embedded systems requires mobility and reliability solutions.

Purpose of the Study:

  • Investigate simpler, reliable ANNs for active prosthesis control on a low-cost microcontroller.
  • Evaluate Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN) performance for gait trajectory prediction.
  • Enable system mobility by deploying ANN algorithms on a single embedded device.

Main Methods:

  • Trained MLP and CNN models on shank and foot Inertial Measurement Unit (IMU) data from four subjects.
  • Tested models on a fifth subject to predict gait trajectory 200 ms ahead, with and without current gait phase information.
  • Deployed trained models on a low-cost ESP32 microcontroller for real-time performance evaluation.

Main Results:

  • CNN achieved a higher correlation coefficient (0.973) than MLP (0.945) with limited data (excluding gait phase).
  • Both MLP and CNN achieved high correlation coefficients (approx. 0.98) when current gait phase was provided.
  • MLP execution time (2.4 ms) was significantly faster than CNN (142 ms) on the microcontroller.

Conclusions:

  • CNN is more efficient for gait prediction with scarce data, offering acceptable execution times.
  • MLP provides relative accuracy with low execution time when sufficient training data is available.
  • The study demonstrates the feasibility of deploying simplified ANNs on microcontrollers for mobile active prosthesis control.