ArticleFrontiers in neurology2021
Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work.
Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in rehabilitation: a living systematic mapping review - first release.European journal of physical and rehabilitation medicine · 2025Pooled it
- Identifying key gait features in stroke patients using wearable inertial sensors and supervised and unsupervised machine learning.Scientific reports · 2026Article
- Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applications.Oxford open neuroscience · 2026Article
- Development of an IMU-Based Post-Stroke Gait Data Acquisition and Analysis System for the Gait Assessment and Intervention Tool.Sensors (Basel, Switzerland) · 2025Article
- Steps to Facilitate the Use of Clinical Gait Analysis in Stroke Patients: The Validation of a Single 2D RGB Smartphone Video-Based System for Gait Analysis.Sensors (Basel, Switzerland) · 2024Article
- The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study.Journal of clinical medicine · 2024Article
- Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach.Journal of functional morphology and kinesiology · 2024Article
- Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study.Journal of orthopaedics · 2024Article
- Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis.Journal of clinical medicine · 2024Article
- Embodimetrics: A Principal Component Analysis Study of the Combined Assessment of Cardiac, Cognitive and Mobility Parameters.Sensors (Basel, Switzerland) · 2024Article
- Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort.Journal of occupational rehabilitation · 2023Article
- Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2023Article
- Virtual Art Therapy: Application of Michelangelo Effect to Neurorehabilitation of Patients with Stroke.Journal of clinical medicine · 2023Article
- Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis.Biomolecules · 2023Article
- Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature.Diagnostics (Basel, Switzerland) · 2022Review
- Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics.Biomedicines · 2022Review
- Identification of Determinants of Biofeedback Treatment's Efficacy in Treating Migraine and Oxidative Stress by ARIANNA (ARtificial Intelligent Assistant for Neural Network Analysis).Healthcare (Basel, Switzerland) · 2022Article
- Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke.Sensors (Basel, Switzerland) · 2022Article
- Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis.Frontiers in artificial intelligence · 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
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke.
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