Literature DB >> 31170062

FES Cycling in Stroke: Novel Closed-Loop Algorithm Accommodates Differences in Functional Impairments.

Courtney A Rouse, Ryan J Downey, Chris M Gregory, Christian A Cousin, Victor H Duenas, Warren E Dixon.   

Abstract

OBJECTIVE: The objective of this paper was to develop and test a novel control algorithm that enables stroke survivors to pedal a cycle in a desired cadence range despite varying levels of functional abilities after stroke.
METHODS: A novel algorithm was developed which automatically adjusts 1) the intensity of functional electrical stimulation (FES) delivered to the leg muscles, and 2) the current delivered to an electric motor. The algorithm automatically switches between assistive, uncontrolled, and resistive modes to accommodate for differences in functional impairment, based on the mismatch between the desired and actual cadence. Lyapunov-based methods were used to theoretically prove that the rider's cadence converges to the desired cadence range. To demonstrate the controller's real-world performance, nine chronic stroke survivors performed two cycling trials: 1) volitional effort only and 2) volitional effort accompanied by the control algorithm assisting and resisting pedaling as needed.
RESULTS: With a desired cadence range of 50-55 r/min, the developed controller resulted in an average rms cadence error of 1.90 r/min, compared to 6.16 r/min during volitional-only trials.
CONCLUSION: Using FES and an electric motor with a two-sided cadence control objective to assist and resist volitional efforts enabled stroke patients with varying strength and abilities to pedal within a desired cadence range. SIGNIFICANCE: A protocol design that constrains volitional movements with assistance and resistance from FES and a motor shows potential for FES cycles and other rehabilitation robots during stroke rehabilitation.

Entities:  

Mesh:

Year:  2019        PMID: 31170062     DOI: 10.1109/TBME.2019.2920346

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  4 in total

1.  Encouraging Volitional Pedaling in Functional Electrical Stimulation-Assisted Cycling Using Barrier Functions.

Authors:  Axton Isaly; Brendon C Allen; Ricardo G Sanfelice; Warren E Dixon
Journal:  Front Robot AI       Date:  2021-11-24

2.  An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton.

Authors:  Vahidreza Molazadeh; Qiang Zhang; Xuefeng Bao; Nitin Sharma
Journal:  IEEE Trans Control Syst Technol       Date:  2021-07-05       Impact factor: 5.418

3.  Muscle Electrical Impedance Properties and Activation Alteration After Functional Electrical Stimulation-Assisted Cycling Training for Chronic Stroke Survivors: A Longitudinal Pilot Study.

Authors:  Chengpeng Hu; Tong Wang; Kenry W C Leung; Le Li; Raymond Kai-Yu Tong
Journal:  Front Neurol       Date:  2021-12-15       Impact factor: 4.003

4.  Motorless cadence control of standard and low duty cycle-patterned neural stimulation intensity extends muscle-driven cycling output after paralysis.

Authors:  Kristen Gelenitis; Kevin Foglyano; Lisa Lombardo; John McDaniel; Ronald Triolo
Journal:  J Neuroeng Rehabil       Date:  2022-08-09       Impact factor: 5.208

  4 in total

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