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Updated: Aug 23, 2025

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Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.

Biao Chen1, Chaoyang Chen2, Jie Hu1

  • 1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
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This summary is machine-generated.

Machine learning algorithms, particularly Support Vector Machines (SVM), show high accuracy in classifying gait patterns for potential fall risk assessment. This technology aids in remote patient evaluation and clinical decision-making.

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Kinesiology

Background:

  • Gait recognition is crucial for predicting elderly falls, evaluating rehabilitation, and training patients with motor dysfunction.
  • Distinguishing subtle gait patterns associated with different pathologies presents a clinical challenge.
  • Developing automated gait identification systems using artificial intelligence (AI) and machine learning (ML) is a significant goal.

Purpose of the Study:

  • To identify an optimal ML algorithm for classifying healthy gait patterns using computer vision.
  • To assess the feasibility of computer vision and ML in discriminating gait patterns relevant to flat-ground falls.

Main Methods:

  • Collected spatiotemporal gait data from seven healthy subjects using the Kinect Motion system.
Keywords:
convolutional neural networkfall recognitiongaitk nearest neighborlong short-time memorymachine learningpattern recognitionsupport vector machine

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  • Classified three gait patterns (normal, pelvic-obliquity-gait, knee-hyperextension-gait) using Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Long Short-Term Memory (LSTM).
  • Utilized an 80% training and 20% evaluation data split for algorithm testing.

    • Main Results:

    • Support Vector Machine (SVM) achieved the highest classification accuracy at 94.9 ± 3.36%.
    • K-nearest Neighbors (KNN) followed with 94.0 ± 4.22% accuracy.
    • Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) showed lower accuracies of 87.6 ± 7.50% and 83.6 ± 5.35%, respectively.

    Conclusions:

    • AI and ML techniques are effective for developing gait biometric systems and machine vision for gait pattern recognition.
    • This approach holds potential for remote evaluation of elderly patients.
    • The findings can assist clinicians in making informed decisions regarding patient care and treatment.