Literature DB >> 18586610

Automatic synchronization of functional electrical stimulation and robotic assisted treadmill training.

Mark E Dohring1, Janis J Daly.   

Abstract

This work presents a means to automatically synchronize two promising gait training technologies to address gait deficits in stroke survivors: functional electrical stimulation using intramuscular electrodes (FES-IM) and the Lokomat robotic gait orthosis. A system of hardware and software was developed to achieve the automatic synchronization. A series of bench tests were performed to verify the feasibility and reliability of automatic synchronization. The bench tests showed that automatic synchronization of FES-IM to the Lokomat gait cycle was feasible and reliable. Automatic synchronization was more consistent than manually triggered stimulation (10-fold smaller standard deviation of latency), and produced no early or missed stimulations across 634 strides. Automatic synchronization had greater accuracy than manually triggered stimulation, producing stimulation timed to an accuracy of 2.5% of one gait cycle duration (heel strike to heel strike = 100).

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Mesh:

Year:  2008        PMID: 18586610     DOI: 10.1109/TNSRE.2008.920081

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  8 in total

1.  Plantar Pressure Distribution During Robotic-Assisted Gait in Post-stroke Hemiplegic Patients.

Authors:  Jin Kyu Yang; Na El Ahn; Dae Hyun Kim; Deog Young Kim
Journal:  Ann Rehabil Med       Date:  2014-04-29

Review 2.  Rehabilitation of gait after stroke: a review towards a top-down approach.

Authors:  Juan-Manuel Belda-Lois; Silvia Mena-del Horno; Ignacio Bermejo-Bosch; Juan C Moreno; José L Pons; Dario Farina; Marco Iosa; Marco Molinari; Federica Tamburella; Ander Ramos; Andrea Caria; Teodoro Solis-Escalante; Clemens Brunner; Massimiliano Rea
Journal:  J Neuroeng Rehabil       Date:  2011-12-13       Impact factor: 4.262

3.  Feasibility of Using Lokomat Combined with Functional Electrical Stimulation for the Rehabilitation of Foot Drop.

Authors:  Christian B Laursen; Jørgen F Nielsen; Ole K Andersen; Erika G Spaich
Journal:  Eur J Transl Myol       Date:  2016-08-05

4.  Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Authors:  Andreas Schicketmueller; Juliane Lamprecht; Marc Hofmann; Michael Sailer; Georg Rose
Journal:  Sensors (Basel)       Date:  2020-06-16       Impact factor: 3.576

Review 5.  A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation.

Authors:  Bradley Hobbs; Panagiotis Artemiadis
Journal:  Front Neurorobot       Date:  2020-04-15       Impact factor: 2.650

6.  Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training.

Authors:  Andreas Schicketmueller; Georg Rose; Marc Hofmann
Journal:  Sensors (Basel)       Date:  2019-11-05       Impact factor: 3.576

7.  A Deep Learning Model for Stroke Patients' Motor Function Prediction.

Authors:  Abeer Abdulaziz AlArfaj; Hanan A Hosni Mahmoud; Alaaeldin M Hafez
Journal:  Appl Bionics Biomech       Date:  2022-08-05       Impact factor: 1.664

8.  A Decoding Prediction Model of Flexion and Extension of Left and Right Feet from Electroencephalogram.

Authors:  Abeer Abdulaziz AlArfaj; Hanan A Hosni Mahmoud; Alaaeldin M Hafez
Journal:  Behav Sci (Basel)       Date:  2022-08-13
  8 in total

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