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Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Blana D, Kyriacou T, Lambrecht JM, Chadwick EK - J Electromyogr Kinesiol (2015)

Bottom Line: Transhumeral amputation has a significant effect on a person's independence and quality of life.The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively).During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%).

View Article: PubMed Central - PubMed

Affiliation: Institute for Science and Technology in Medicine, Keele University, UK. Electronic address: d.blana@keele.ac.uk.

No MeSH data available.


Related in: MedlinePlus

The two experimental phases of the study: the IMU-control and ANN-control phase. Shown are the EMG sensors around the circumference of the humerus (grey ovals), and three IMU (orange boxes, 1: thorax, 2: humerus, 3: forearm). Humeral angles are calculated by the combination of signals from the thorax and humerus IMU, and these are used to control the movement of the virtual humerus in the VRE. Similarly, elbow/forearm angles are calculated by the combination of signals from the humerus and forearm IMU, and these are used in the IMU-control phase to control the movement of the virtual forearm in the VRE. These are also used as output training signals for the ANN, while the input training signals are EMG and humerus angular velocity and linear acceleration, calculated from the humerus IMU. In the ANN-control phase, the ANN outputs are used to control the virtual forearm in the VRE instead of the IMU signals.
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f0005: The two experimental phases of the study: the IMU-control and ANN-control phase. Shown are the EMG sensors around the circumference of the humerus (grey ovals), and three IMU (orange boxes, 1: thorax, 2: humerus, 3: forearm). Humeral angles are calculated by the combination of signals from the thorax and humerus IMU, and these are used to control the movement of the virtual humerus in the VRE. Similarly, elbow/forearm angles are calculated by the combination of signals from the humerus and forearm IMU, and these are used in the IMU-control phase to control the movement of the virtual forearm in the VRE. These are also used as output training signals for the ANN, while the input training signals are EMG and humerus angular velocity and linear acceleration, calculated from the humerus IMU. In the ANN-control phase, the ANN outputs are used to control the virtual forearm in the VRE instead of the IMU signals.

Mentions: Fig. 1 describes the method used in this study. Able-bodied individuals performed reaching movements with their right arms that were translated into movements of a virtual arm in a virtual reality environment. During the movements, EMG and kinematic signals from the humerus were recorded, as well as elbow and forearm angles. These data were used offline to train two time-delayed artificial neural networks (ANN) to predict elbow and forearm angles from processed humerus EMG and kinematic signals. Subsequently, the participants performed similar reaching movements, but the elbow and forearm angles of the virtual arm were now controlled by the trained ANN.


Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Blana D, Kyriacou T, Lambrecht JM, Chadwick EK - J Electromyogr Kinesiol (2015)

The two experimental phases of the study: the IMU-control and ANN-control phase. Shown are the EMG sensors around the circumference of the humerus (grey ovals), and three IMU (orange boxes, 1: thorax, 2: humerus, 3: forearm). Humeral angles are calculated by the combination of signals from the thorax and humerus IMU, and these are used to control the movement of the virtual humerus in the VRE. Similarly, elbow/forearm angles are calculated by the combination of signals from the humerus and forearm IMU, and these are used in the IMU-control phase to control the movement of the virtual forearm in the VRE. These are also used as output training signals for the ANN, while the input training signals are EMG and humerus angular velocity and linear acceleration, calculated from the humerus IMU. In the ANN-control phase, the ANN outputs are used to control the virtual forearm in the VRE instead of the IMU signals.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940208&req=5

f0005: The two experimental phases of the study: the IMU-control and ANN-control phase. Shown are the EMG sensors around the circumference of the humerus (grey ovals), and three IMU (orange boxes, 1: thorax, 2: humerus, 3: forearm). Humeral angles are calculated by the combination of signals from the thorax and humerus IMU, and these are used to control the movement of the virtual humerus in the VRE. Similarly, elbow/forearm angles are calculated by the combination of signals from the humerus and forearm IMU, and these are used in the IMU-control phase to control the movement of the virtual forearm in the VRE. These are also used as output training signals for the ANN, while the input training signals are EMG and humerus angular velocity and linear acceleration, calculated from the humerus IMU. In the ANN-control phase, the ANN outputs are used to control the virtual forearm in the VRE instead of the IMU signals.
Mentions: Fig. 1 describes the method used in this study. Able-bodied individuals performed reaching movements with their right arms that were translated into movements of a virtual arm in a virtual reality environment. During the movements, EMG and kinematic signals from the humerus were recorded, as well as elbow and forearm angles. These data were used offline to train two time-delayed artificial neural networks (ANN) to predict elbow and forearm angles from processed humerus EMG and kinematic signals. Subsequently, the participants performed similar reaching movements, but the elbow and forearm angles of the virtual arm were now controlled by the trained ANN.

Bottom Line: Transhumeral amputation has a significant effect on a person's independence and quality of life.The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively).During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%).

View Article: PubMed Central - PubMed

Affiliation: Institute for Science and Technology in Medicine, Keele University, UK. Electronic address: d.blana@keele.ac.uk.

No MeSH data available.


Related in: MedlinePlus