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

doi:10.3390/s22207960

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

2022-10-19

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Carlos Palacio-Lascano

DOI：10.3390/s22207960

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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 (R) 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.

Keywords:

machine learning

gait

pattern recognition

convolutional neural network

support vector machine

long short-time memory

k nearest neighbor

fall recognition

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

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