ArticleSensors (Basel, Switzerland)2022
Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Balance Assessment Using Gamified Digital Technology in Community-Dwelling Older Adults: Mixed Methods Validation Study and Randomized Controlled Trial.JMIR serious games · 2026Article
- Development of Control Algorithms for an Adaptive Running Platform for a Musculoskeletal Rehabilitation System.Sensors (Basel, Switzerland) · 2025Article
- Gait analysis for Parkinson's disease using multiscale entropy.Neurodegenerative disease management · 2025Review
- Vision-Based Assessment of Skeletal Muscle Decline: Correlating Gait Variance with SPPB Performance.Healthcare (Basel, Switzerland) · 2025Article
- Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors.Sensors (Basel, Switzerland) · 2025Article
- Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis.Journal of functional morphology and kinesiology · 2025Article
- Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.Sensors (Basel, Switzerland) · 2025Review
- Estimating hip impact velocity and acceleration from video-captured falls using a pose estimation algorithm.Scientific reports · 2025Article
- EgoFall: Real-Time Privacy-Preserving Fall Risk Assessment With a Single On-Body Tracking Camera.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Risk Prediction and Interpretation for Fall Events Using Explainable AI and Large Language Models.Proceedings of the 2025 9th International Conference on Medical and Health Informatics. International Conference on Medical and Health Informatics (9th : 2025 : Kyoto, Japan) · 2025Article
- Article
- Wearable sensors and machine learning fusion-based fall risk prediction in covert cerebral small vessel disease.Frontiers in neuroscience · 2025Article
- Application of multidimensional gait feature fusion algorithm in gait assessment for patients with knee osteoarthritis.Frontiers in bioengineering and biotechnology · 2025Article
- Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures.Journal of imaging · 2024Review
- Machine Learning for Movement Pattern Changes during Kinect-Based Mixed Reality Exercise Programs in Women with Possible Sarcopenia: Pilot Study.Annals of geriatric medicine and research · 2024Article
- Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults.Sensors (Basel, Switzerland) · 2024Article
- Predicting factors for extremity fracture among border-fall patients using machine learning computing.Heliyon · 2024Article
- Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.Bioengineering (Basel, Switzerland) · 2024Article
- CNN-Based Self-Attention Weight Extraction for Fall Event Prediction Using Balance Test Score.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
backgroundGait 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.
methodsWe used the Kinect
resultsThe 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%.
conclusionsThis 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.