Paper Info
Title
Decoding Movement Intent Patterns Based On Spatiotemporal And Adaptive Filtering Method Towards Active Motor Training In Stroke Rehabilitation Systems
Abstract
Upper extremity (UE) neuromuscular dysfunction critically affects post-stroke patients from performing activities of daily life. In this regard, various rehabilitation robotics have been developed for providing assistive and/or resistive forces that allow stroke survivors to train their arms towards regaining the lost arm function. However, most of the rehabilitation systems function in a passively such that they only allow patients navigate already-defined trajectories that often does not align with their UE movement intention, thus hindering adequate motor function recovery. One possible way to address this problem is to use a decoded UE motion intent to trigger active and intuitive motor training for the patients, which would help restore their UE arm functions. In this study, a new approach based on spatiotemporal neuromuscular descriptor and adaptive filtering technique (STD-AFT) is proposed to optimally characterize multiple patterns of UE movements in post-stroke patients towards providing inputs for intelligently driven motor training in the rehabilitation robotic systems. The proposed STD-AFT performance was systematically investigated and assessed in comparison with commonly adopted methods via high-density surface electromyogram recordings obtained from post-stroke survivors who performed 21 distinct classes of pre-defined limb movements. Furthermore, the movement intent decoding was done using four different classification algorithms. The experimental results showed that the proposed STD-AFT achieved significant improvement of up to 13.36% (p<0.05) in characterizing the multiple patterns of movement intents with relatively lower standard-error value even in the presence of the external interference in form of noise compared to the existing benchmark methods. Also, the STD-AFT showed obvious pattern seperability for individual movement class in a two-dimensional space. The outcomes of this study suggest that the proposed STD-AFT could provide potential inputs for active and intuitive motor training in robotic systems targeted towards stroke-rehabilitation.
Year
DOI
Venue
2021
10.1007/s00521-020-05536-9
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Neuromuscular function, Artificial neural networks, Pattern recognition, High-density surface electromyogram, Rehabilitation robotics for stroke patients
Journal
33
Issue
ISSN
Citations 
10
0941-0643
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
O. W. Samuel116122.87
Mojisola Grace Asogbon2206.33
Yanjuan Geng3327.76
Naifu Jiang400.68
Deogratias Mzurikwao500.34
Yue Zheng67010.70
Kelvin Kian Loong Wong7269.54
Luca Vollero800.34
Guanglin Li96214.18