The Positioning of Systems Powered by McKibben Type Muscles

Parandyk, Wiktor; Ludwicki, M.; Zagrodny, Bartłomiej; Awrejcewicz, Jan

doi:10.1007/978-3-319-10990-9_13

Cited by 2 publications

(2 citation statements)

References 5 publications

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“… The extensor muscle only generates extension force in response to supplied air pressure [17,18,28,29].  There is no single PAM capable of performing both contraction (decreasing in length) and extension (increasing in length) with reference to its nominal length [30,31].  There is no single PAM able to produce bidirectional (extension and contraction) force [30,32].…”

Section: Novel Extensor-contractor Pneumatic Artificial Muscles (Ecpam)mentioning

confidence: 99%

The Design and Mathematical Model of a Novel Variable Stiffness Extensor-Contractor Pneumatic Artificial Muscle

et al. 2018

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This article presents the design of a novel extensor-contractor pneumatic artificial muscle (ECPAM). This new actuator has numerous advantages over traditional pneumatic artificial muscles. These include the abilities to both contract and extend relative to a nominal initial length, to generate both contraction and extension forces, and to vary stiffness at any actuator length. A kinematic analysis of the ECPAM is presented in this article. A new output force mathematical model has been developed for the ECPAM based on its kinematic analysis and the theory of energy conservation. The correlation between experimental results and the new mathematical model has been investigated and show good correlation. Numerous stiffness experiments have been conducted to validate the variable stiffness ability of the actuator at a series of specific fixed lengths. This has proven that actuator stiffness can be adjusted independently of actuator length. Finally, a stiffness-position controller has been developed to validate the effectiveness of the novel actuator.

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Section: Novel Extensor-contractor Pneumatic Artificial Muscles (Ecpam)mentioning

confidence: 99%

The Design and Mathematical Model of a Novel Variable Stiffness Extensor-Contractor Pneumatic Artificial Muscle

et al. 2018

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“…Several studies used the Moens-Korteweg formula or data-driven modeling using machine learning [10,14,[16][17][18][19][20][21][22][23][24][25]. Among those methods, dependency of PTT only result in a measurement where the variation because of PTT in BP is visible, but the relation is not accurate enough and does not pass any gold standard [13,14,22,26,27].…”

Section: Introductionmentioning

confidence: 99%

Cuff-less blood pressure measurement based on hybrid feature selection algorithm and multi-penalty regularized regression technique

Mamun¹

2021

Biomed. Phys. Eng. Express

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One of the prominent reasons behind the deterioration of cardiovascular conditions is hypertension. Due to lack of specific symptoms, sometimes existing hypertension goes unnoticed until significant damage happens to the heart or any other body organ. Monitoring of BP at a higher frequency is necessary so that we can take early preventive measures to control and keep it within the normal range. The cuff-based method of measuring BP is inconvenient for frequent daily measurements. The cuffless BP measurement method proposed in this paper uses features extracted from the electrocardiogram (ECG) and photoplethysmography (PPG). ECG and PPG both have distinct characteristics, which change with the change of blood pressure levels. Feature extraction and hybrid feature selection algorithms are followed by a generalized penalty-based regression technique led to a new BP measurement process that uses the minimum number of features. The performance of the proposed technique to measure blood pressure was compared to an approach using an ordinary linear regression method with no feature selection and to other contemporary techniques. MIMIC-II database was used to train and test our proposed method. The root mean square error (RMSE) for systolic blood pressure (SBP) improved from 11.2 mmHg to 5.6 mmHg when the proposed technique was implemented and for diastolic blood pressure (DBP) improved from 12.7 mmHg to 6.69 mmHg. The mean absolute error (MAE) was found to be 4.91 mmHg for SBP and 5.77 mmHg for DBP, which have shown improvement over other existing cuffless techniques where the substantial number of patients, as well as feature selection algorithm, were implemented. In addition, according to the British Hypertension Society standard (BHS) standard for cuff-based BP measurement, the criteria for acceptable measurement are to achieve at least grade B; our proposed method also satisfies this criterion.

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The Positioning of Systems Powered by McKibben Type Muscles

Cited by 2 publications

References 5 publications

The Design and Mathematical Model of a Novel Variable Stiffness Extensor-Contractor Pneumatic Artificial Muscle

The Design and Mathematical Model of a Novel Variable Stiffness Extensor-Contractor Pneumatic Artificial Muscle

Cuff-less blood pressure measurement based on hybrid feature selection algorithm and multi-penalty regularized regression technique

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