Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization
The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the asse...
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1709 |
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| author | Marija Radmilović Djordje Urukalo Milica M. Janković Suzana Dedijer Dujović Tijana J. Dimkić Tomić Maja Trumić Kosta Jovanović |
| author_facet | Marija Radmilović Djordje Urukalo Milica M. Janković Suzana Dedijer Dujović Tijana J. Dimkić Tomić Maja Trumić Kosta Jovanović |
| author_sort | Marija Radmilović |
| collection | DOAJ |
| description | The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (<i>Stiffness</i>) based on the estimated joint torque and the estimated elbow angle. Finally, novel <i>Stiffness</i> scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general <i>Stiffness</i> patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders. |
| first_indexed | 2024-03-11T09:23:59Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:23:59Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4027fe4130504289a1656bddf65f43532023-11-16T18:05:03ZengMDPI AGSensors1424-82202023-02-01233170910.3390/s23031709Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic OptimizationMarija Radmilović0Djordje Urukalo1Milica M. Janković2Suzana Dedijer Dujović3Tijana J. Dimkić Tomić4Maja Trumić5Kosta Jovanović6Institute Mihailo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, SerbiaInstitute Mihailo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, SerbiaSchool of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, SerbiaClinic for Rehabilitation “Dr. Miroslav Zotović”, Faculty of Medicine, University of Belgrade, 11000 Belgrade, SerbiaClinic for Rehabilitation “Dr. Miroslav Zotović”, Faculty of Medicine, University of Belgrade, 11000 Belgrade, SerbiaSchool of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, SerbiaSchool of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, SerbiaThe ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (<i>Stiffness</i>) based on the estimated joint torque and the estimated elbow angle. Finally, novel <i>Stiffness</i> scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general <i>Stiffness</i> patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.https://www.mdpi.com/1424-8220/23/3/1709agonist–antagonistelectromyographygenetic algorithmpassive joint structureWolf Motor Function Test |
| spellingShingle | Marija Radmilović Djordje Urukalo Milica M. Janković Suzana Dedijer Dujović Tijana J. Dimkić Tomić Maja Trumić Kosta Jovanović Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization Sensors agonist–antagonist electromyography genetic algorithm passive joint structure Wolf Motor Function Test |
| title | Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization |
| title_full | Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization |
| title_fullStr | Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization |
| title_full_unstemmed | Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization |
| title_short | Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization |
| title_sort | elbow joint stiffness functional scales based on hill s muscle model and genetic optimization |
| topic | agonist–antagonist electromyography genetic algorithm passive joint structure Wolf Motor Function Test |
| url | https://www.mdpi.com/1424-8220/23/3/1709 |
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