scholarly journals Effects of Gait Training Using Functional Electrical Stimulation of the Tibialis Anterior Muscle on the Gait Pattern of Convalescent Hemiplegic Stroke Patients

2021 ◽  
Vol 36 (1) ◽  
pp. 119-123
Author(s):  
Mizuho OTA ◽  
Jun AOKI ◽  
Chihiro FUJII ◽  
Tatsuki YAMADA ◽  
Makoto TAMARI
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Tibialis Anterior ◽  
Tibialis Anterior Muscle ◽  
Gait Training ◽  
Gait Pattern ◽  
Stroke Patients ◽  
Stimulation Of

scholarly journals Influence of electrical stimulation of the tibialis anterior muscle in paraplegic subjects. 2. Morphological and histochemical properties

Spinal Cord ◽  
1995 ◽  
Vol 33 (9) ◽  
pp. 514-522 ◽  
Author(s):  
L Rochester ◽  
M J Barron ◽  
C S Chandler ◽  
R A Sutton ◽  
S Miller ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Tibialis Anterior ◽  
Tibialis Anterior Muscle ◽  
Stimulation Of

scholarly journals Influence of electrical stimulation of the tibialis anterior muscle in paraplegic subjects. 1. Contractile properties

Spinal Cord ◽  
1995 ◽  
Vol 33 (8) ◽  
pp. 437-449 ◽  
Author(s):  
L Rochester ◽  
C S Chandler ◽  
M A Johnson ◽  
R A Sutton ◽  
S Miller
Keyword(s):  
Electrical Stimulation ◽  
Tibialis Anterior ◽  
Tibialis Anterior Muscle ◽  
Contractile Properties ◽  
Stimulation Of

scholarly journals Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3399
Author(s):  
Andreas Schicketmueller ◽  
Juliane Lamprecht ◽  
Marc Hofmann ◽  
Michael Sailer ◽  
Georg Rose
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Gait Training ◽  
Patient Specific ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Rehabilitation Process ◽  
Type 1 Error ◽  
Movement Data ◽  
Measurement Units

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

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