A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty

Li, Wanting; Liu, Keping; Sun, Zhongbo; Li, Chunxu; Chai, Yuanyuan; Gu, Jian

doi:10.1016/j.bspc.2021.103115

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…ET performs best, with 93.1% accuracy and an F1 of 85.3%. Paper [155] aims to employ sEMG from leg muscles to predict hip and knee angles for human walking. Physiological and correlation analyses are utilized to select and evaluate two sEMG signals from seven muscles during walking, which are filtered and normalized.…”

Section: G Abnormal Lower Limb Movementsmentioning

confidence: 99%

Review and Analysis of Patients’ Body Language From an Artificial Intelligence Perspective

et al. 2023

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Body language is a nonverbal communication process consisting of movements, postures, gestures, and expressions of the body or body parts. Body language expresses human feelings, thoughts, and intentions. It also reveals physical and psychological health conditions: abnormal activities inform peoples' health conditions, facial expressions indicate their emotional states and abnormal body actions convey specific diseases' external signs and symptoms. We can observe the importance of studying the body language of people with health conditions through many reports in literature written by healthcare (medical) and artificial intelligence researchers. This paper comprehensively reviews artificial intelligence-based articles that have studied patients' body language. We also conduct different descriptive and exploratory examinations of the findings using data analysis techniques, which provide more authentic domain knowledge of abnormal activities, abnormal body actions, and more precise analysis of methodologies used in machine learning tasks for studying these abnormalities. The paper's results are essential for developing intelligent automated systems that accurately evaluate patients' physical and psychological conditions, precisely identify external signs and symptoms of diseases, and adequately monitor patients' health conditions.

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Section: G Abnormal Lower Limb Movementsmentioning

confidence: 99%

Review and Analysis of Patients’ Body Language From an Artificial Intelligence Perspective

et al. 2023

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“…Li et al. [19] constructed a prediction model using both a fuzzy neural network and a ZNN, demonstrating superior results from various perspectives. Data‐driven models reflect a greater advantage in this regard.…”

Section: Introductionmentioning

confidence: 99%

“…Alsuwian et al [18] elaborated on Anti-Surge Control (ASC) and FTC systems for compressors from the integration point of view to improve the system component failure reliability in case of system component failure. Li et al [19] constructed a prediction model using both a fuzzy neural network and a ZNN, demonstrating superior results from various perspectives. Data-driven models reflect a greater advantage in this regard.…”

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confidence: 99%

Aviation fuel pump health state assessment based on evidential reasoning and random forests

Zhang,

Chen,

et al. 2024

Electronics Letters

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As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

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The application of SOFNN based on PSO-ILM algorithm in nonlinear system modeling

et al. 2022

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A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty

Cited by 16 publications

References 31 publications

Review and Analysis of Patients’ Body Language From an Artificial Intelligence Perspective

Review and Analysis of Patients’ Body Language From an Artificial Intelligence Perspective

Aviation fuel pump health state assessment based on evidential reasoning and random forests

The application of SOFNN based on PSO-ILM algorithm in nonlinear system modeling

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