ArticleSensors (Basel, Switzerland)2022
Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke.
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 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applications.Oxford open neuroscience · 2026Article
- 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
- Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics.Entropy (Basel, Switzerland) · 2024Article
- Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis.Biomolecules · 2023Article
- Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains.Sensors (Basel, Switzerland) · 2022Article
- 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
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many recent studies have highlighted that the harmony of physiological walking is based on a specific proportion between the durations of the phases of the gait cycle. When this proportion is close to the so-called golden ratio (about 1.618), the gait cycle assumes an autosimilar fractal structure. In stroke patients this harmony is altered, but it is unclear which factor is associated with the ratios between gait phases because these relationships are probably not linear. We used an artificial neural network to determine the weights associable to each factor for determining the ratio between gait phases and hence the harmony of walking. As expected, the gait ratio obtained as the ratio between stride duration and stance duration was found to be associated with walking speed and stride length, but also with hip muscle forces. These muscles could be important for exploiting the recovery of energy typical of the pendular mechanism of walking. Our study also highlighted that the results of an artificial neural network should be associated with a reliability analysis, being a non-deterministic approach. A good level of reliability was found for the findings of our study.
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Registered trials
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