基于计算机视觉和机器学习的步态模式识别在平足跌倒预测中的应用

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

机构信息

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

Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA.

出版信息

Sensors (Basel). 2022 Oct 19;22(20):7960. doi: 10.3390/s22207960.

DOI:10.3390/s22207960

PMID:36298311

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9612353/

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.

METHODS

We used the Kinect Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation.

RESULTS

The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%.

CONCLUSIONS

This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.

摘要

背景

步态识别已应用于预测老年人平地跌倒的概率、康复期间的功能评估以及下肢运动功能障碍患者的训练。区分与不同病理实体相关的看似相似的运动学模式是临床医生面临的挑战。如何实现异常步态的自动识别和判断是临床实践中的一个重大挑战。我们的长期目标是使用人工智能（AI）和机器学习（ML）计算开发步态识别计算机视觉系统。本研究旨在寻找一种使用计算机视觉技术和下肢测量变量的最佳 ML 算法，以对健康人的步态模式进行分类。本研究的目的是确定计算机视觉和机器学习（ML）计算在区分与平地跌倒相关的不同步态模式方面的可行性。

方法

我们使用 Kinect 运动系统从 7 名健康受试者的 3 次步行试验中捕获时空步态数据，包括正常步态、骨盆倾斜步态和膝关节过伸步态。使用包括卷积神经网络（CNN）、支持向量机（SVM）、K 近邻（KNN）和长短期记忆（LSTM）神经网络在内的四种不同分类方法自动对三种步态模式进行分类。总共采集了 750 组数据，数据集分为 80%用于算法训练，20%用于评估。