PMID- 36298311
OWN - NLM
STAT- MEDLINE
DCOM- 20221028
LR  - 20221030
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 20
DP  - 2022 Oct 19
TI  - Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall 
      Prediction.
LID - 10.3390/s22207960 [doi]
LID - 7960
AB  - 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.
FAU - Chen, Biao
AU  - Chen B
AD  - State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong 
      University, Shanghai 200240, China.
FAU - Chen, Chaoyang
AU  - Chen C
AD  - Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 
      48201, USA.
FAU - Hu, Jie
AU  - Hu J
AD  - State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong 
      University, Shanghai 200240, China.
FAU - Sayeed, Zain
AU  - Sayeed Z
AD  - Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 
      48201, USA.
FAU - Qi, Jin
AU  - Qi J
AUID- ORCID: 0000-0002-4085-5041
AD  - State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong 
      University, Shanghai 200240, China.
FAU - Darwiche, Hussein F
AU  - Darwiche HF
AD  - Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 
      48201, USA.
FAU - Little, Bryan E
AU  - Little BE
AD  - Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 
      48201, USA.
FAU - Lou, Shenna
AU  - Lou S
AD  - South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
FAU - Darwish, Muhammad
AU  - Darwish M
AD  - South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
FAU - Foote, Christopher
AU  - Foote C
AD  - South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
FAU - Palacio-Lascano, Carlos
AU  - Palacio-Lascano C
AD  - South Texas Health System-McAllen Department of Trauma, McAllen, TX 78503, USA.
LA  - eng
PT  - Journal Article
DEP - 20221019
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Humans
MH  - Aged
MH  - *Artificial Intelligence
MH  - *Gait
MH  - Support Vector Machine
MH  - Machine Learning
MH  - Computers
PMC - PMC9612353
OTO - NOTNLM
OT  - convolutional neural network
OT  - fall recognition
OT  - gait
OT  - k nearest neighbor
OT  - long short-time memory
OT  - machine learning
OT  - pattern recognition
OT  - support vector machine
COIS- The authors declare no conflict of interest.
EDAT- 2022/10/28 06:00
MHDA- 2022/10/29 06:00
PMCR- 2022/10/19
CRDT- 2022/10/27 01:56
PHST- 2022/09/21 00:00 [received]
PHST- 2022/10/12 00:00 [revised]
PHST- 2022/10/14 00:00 [accepted]
PHST- 2022/10/27 01:56 [entrez]
PHST- 2022/10/28 06:00 [pubmed]
PHST- 2022/10/29 06:00 [medline]
PHST- 2022/10/19 00:00 [pmc-release]
AID - s22207960 [pii]
AID - sensors-22-07960 [pii]
AID - 10.3390/s22207960 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Oct 19;22(20):7960. doi: 10.3390/s22207960.