Assistive Force Cycle Prediction Method for Lower Limb Exoskeleton Based on Independent Attitude Processing Sensor

Sun, Lei; Wang, Hao; Lu, Rundong

doi:10.1109/jsen.2024.3363439

IEEE Sensors J.

2024

DOI: 10.1109/jsen.2024.3363439

|View full text |Cite

Assistive Force Cycle Prediction Method for Lower Limb Exoskeleton Based on Independent Attitude Processing Sensor

Lei Sun,

Hao Wang,

Rundong Lu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

Cui,

Chipusu,

Ashraf

et al. 2024

Symmetry

View full text Add to dashboard Cite

This paper introduces a novel methodology for mitigating undesired oscillations in overhead crane systems used in material handling operations in the industry by leveraging Long Short-Term Memory (LSTM)-based Recurrent Neural Networks (RNNs). Oscillations during material transportation, particularly at the end location, pose safety risks and prolong carrying times. The methodology involves collecting sensor data from an overhead crane system, preprocessing the data, training an LSTM-based RNN model that incorporates symmetrical features, and integrating the model into a control algorithm. The control algorithm utilizes swing angle predictions from the symmetry-enhanced LSTM-based RNN model to dynamically adjust crane motion in real time, minimizing oscillations. Symmetry in this framework refers to the balanced and consistent handling of oscillatory data, ensuring that the model can generalize better across different scenarios and load conditions. The LSTM-based RNN model accurately predicts swing angles, enabling proactive control actions to be taken. Experimental validation demonstrates the effectiveness of the proposed approach, achieving an accuracy of approximately 98.6% in swing angle prediction. This innovative approach holds promise for transforming material transportation processes in industrial settings, enhancing operational safety, and optimizing efficiency.

show abstract

Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

Cui,

Chipusu,

Ashraf

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Assistive Force Cycle Prediction Method for Lower Limb Exoskeleton Based on Independent Attitude Processing Sensor

Cited by 1 publication

References 37 publications

Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

Contact Info

Product

Resources

About