2019
DOI: 10.1080/17483107.2019.1629701
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Lower limb rehabilitation using multimodal measurement of sit-to-stand and stand-to-sit task

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Cited by 10 publications
(6 citation statements)
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References 39 publications
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“…The duration of the Si-St task (1.70s) was found to be less than the St-Si task (1.83s) in the stroke patients, consistent with previous studies (8, 12, 17) , which showed that the duration of the St-Si task was always longer than the Si-St task due to more careful control of eccentric muscle groups during the St-Si tasks (31). The difference in duration may be related to differences in the type of motor control, muscle coordination and sensory inputs associated with each task (32).…”
Section: Discussionsupporting
confidence: 90%
“…The duration of the Si-St task (1.70s) was found to be less than the St-Si task (1.83s) in the stroke patients, consistent with previous studies (8, 12, 17) , which showed that the duration of the St-Si task was always longer than the Si-St task due to more careful control of eccentric muscle groups during the St-Si tasks (31). The difference in duration may be related to differences in the type of motor control, muscle coordination and sensory inputs associated with each task (32).…”
Section: Discussionsupporting
confidence: 90%
“…The classification accuracy can be further improved by considering a multimodal approach. Instead of only using EMG, a combination of EMG with body kinematics may improve the classification accuracy and precision [7].…”
Section: Resultsmentioning
confidence: 99%
“…Naive-Bayes model was used to classify the four different phases of STS task. The phases were determined based upon the angular deviation of the trunk and knee [7]. The four phases were: Phase I (P1) -sitting phase, where the participant sits comfortably on the stool; Phase II (P2) -intention to stand from sitting posture; Phase III (P3) -Transition phase; and Phase IV (P4) -standing phase.…”
Section: Classificationmentioning
confidence: 99%
“…Nazmi et al (2019) proposed a classification approach for the segregation of stance and swing phase (HS and TO detection) from feeding EMG signal to an artificial neural network. EMG has also been used for intent, like sit to stand detection (Rasool et al, 2012;Chorin et al, 2016;Li B. et al, 2016;Roldán Jiménez et al, 2019;Bhardwaj et al, 2021) and quantitative localized muscle fatigue estimation (Boudarham et al, 2014;Ropars et al, 2016;Roldán Jiménez et al, 2019;Parent et al, 2019) during gait. Although fatigue is considered a multidimensional concept involving both physiological and psychological implications, the former dimension of fatigue can be observed in both the central and peripheral system domains (Zwarts et al, 2008) and is a widely accepted tool for fatigue estimation (Al-Mulla et al, 2011).…”
Section: Electromyographymentioning
confidence: 99%