Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction
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 associate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/7960 |
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| author | Biao Chen Chaoyang Chen Jie Hu Zain Sayeed Jin Qi Hussein F. Darwiche Bryan E. Little Shenna Lou Muhammad Darwish Christopher Foote Carlos Palacio-Lascano |
| author_facet | Biao Chen Chaoyang Chen Jie Hu Zain Sayeed Jin Qi Hussein F. Darwiche Bryan E. Little Shenna Lou Muhammad Darwish Christopher Foote Carlos Palacio-Lascano |
| author_sort | Biao Chen |
| collection | DOAJ |
| description | 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<sup>®</sup> 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. |
| first_indexed | 2024-03-09T19:30:25Z |
| format | Article |
| id | doaj.art-16188743fcfe49c9835ea9e8bb9f5df1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:25Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-16188743fcfe49c9835ea9e8bb9f5df12023-11-24T02:29:22ZengMDPI AGSensors1424-82202022-10-012220796010.3390/s22207960Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall PredictionBiao Chen0Chaoyang Chen1Jie Hu2Zain Sayeed3Jin Qi4Hussein F. Darwiche5Bryan E. Little6Shenna Lou7Muhammad Darwish8Christopher Foote9Carlos Palacio-Lascano10State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, ChinaOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USAState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, ChinaOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USAState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, ChinaOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USAOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USASouth Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USASouth Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USASouth Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USASouth Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USABackground: 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<sup>®</sup> 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.https://www.mdpi.com/1424-8220/22/20/7960machine learninggaitpattern recognitionconvolutional neural networksupport vector machinelong short-time memory |
| spellingShingle | Biao Chen Chaoyang Chen Jie Hu Zain Sayeed Jin Qi Hussein F. Darwiche Bryan E. Little Shenna Lou Muhammad Darwish Christopher Foote Carlos Palacio-Lascano Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction Sensors machine learning gait pattern recognition convolutional neural network support vector machine long short-time memory |
| title | Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction |
| title_full | Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction |
| title_fullStr | Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction |
| title_full_unstemmed | Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction |
| title_short | Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction |
| title_sort | computer vision and machine learning based gait pattern recognition for flat fall prediction |
| topic | machine learning gait pattern recognition convolutional neural network support vector machine long short-time memory |
| url | https://www.mdpi.com/1424-8220/22/20/7960 |
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