ArticleSensors (Basel, Switzerland)2021
Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Clinical Effect Analysis of Wearable Sensor Technology-Based Gait Function Analysis in Post-Transcranial Magnetic Stimulation Stroke Patients.Sensors (Basel, Switzerland) · 2024Trial
- Article
- SCGait a novel method for person identification applied to legged robots.Scientific reports · 2026Article
- Identifying key gait features in stroke patients using wearable inertial sensors and supervised and unsupervised machine learning.Scientific reports · 2026Article
- High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study.Sensors (Basel, Switzerland) · 2024Observational
- Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.Sensors (Basel, Switzerland) · 2024Article
- The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection.Cureus · 2024Review
- Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.Sensors (Basel, Switzerland) · 2023Article
- The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.Journal of neuroengineering and rehabilitation · 2023Article
- A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- Interpretable evaluation for the Brunnstrom recovery stage of the lower limb based on wearable sensors.Frontiers in neuroinformatics · 2022Article
- Assessment of Agreement Between a New Application to Compute the Wisconsin Gait Score and 3-Dimensional Gait Analysis, and Reliability of the Application in Stroke Patients.Frontiers in human neuroscience · 2022Article
- Classification of Ankle Joint Stiffness during Walking to Determine the Use of Ankle Foot Orthosis after Stroke.Brain sciences · 2021Article
- A Study of Biofeedback Gait Training in Cerebral Stroke Patients in the Early Recovery Phase with Stance Phase as Target Parameter.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.
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Registered trials
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.