ArticleSensors (Basel, Switzerland)2024
Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Multimodal Dataset for Clinical Gait Analysis compromising Optical Motion Capture and Textual Gait Analyses from Patients undergoing Total Knee Arthroplasty.Scientific data · 2026Article
- FPGA-Based Weighted DTW Framework with Hybrid Gait Symmetry Index for Real-Time Wearable Gait Classification.Sensors (Basel, Switzerland) · 2026Article
- A Lightweight Machine Learning Framework for Post-Stroke Gait Abnormality Classification Using Wearable Gyroscope Features.Sensors (Basel, Switzerland) · 2026Article
- Recognition of Gait Alterations Induced by Alcohol-Impairment Simulation Goggles Using Smartphone Accelerometer Signals.Sensors (Basel, Switzerland) · 2026Article
- Wearable Technologies for Clinical Assessment and Monitoring in Pediatric Neurodevelopmental Disorders : A Narrative Review.Journal of Korean Neurosurgical Society · 2026Article
- Smart Sensor-Driven Gait Rehabilitation Walker Using Machine Learning for Predictive Home-Based Therapy.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review.Journal of functional morphology and kinesiology · 2026Review
- IMU-based gait analysis methods: a systematic review of techniques for different body locations.Frontiers in sports and active living · 2026Review
- Balance and fall-risk assessment in older adults using wearable plantar pressure and semi-supervised learning.Frontiers in bioengineering and biotechnology · 2025Article
- Detecting environmental barriers affecting older adult pedestrians via Gramian angular field-based CNN of smartphone sensor data.Frontiers in public health · 2025Article
- Prediction of Margin of Gait Stability by Using Six-DoF Motion of Pelvis.Sensors (Basel, Switzerland) · 2024Article
- A narrative review of AI monitoring in postoperative pain management and functional rehabilitation for spinal cord injury.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.
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Registered trials
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