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

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

BACKGROUND: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls.

Authors

  • Biao Chen
    School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Chaoyang Chen
    Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA.
  • Jie Hu
    Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.
  • Zain Sayeed
    Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA.
  • Jin Qi
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
  • Hussein F Darwiche
    Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA.
  • Bryan E Little
    Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA.
  • Shenna Lou
    South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
  • Muhammad Darwish
    South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
  • Christopher Foote
    South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
  • Carlos Palacio-Lascano
    South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.