Limits...
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

Summary of the movement metrics. Panel A shows the throughput for each subject, in the two experiment phases: IMU-control (IMU, dark bars), and ANN-control phase (ANN, light bars). Panel B shows the mean throughput for each experiment phase. Panel C shows the overshoot per subject, and Panel D shows the mean for each experiment phase. Similarly, Panel E shows the path efficiency per subject, and Panel F shows the mean for each experiment phase.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4940208&req=5

f0025: Summary of the movement metrics. Panel A shows the throughput for each subject, in the two experiment phases: IMU-control (IMU, dark bars), and ANN-control phase (ANN, light bars). Panel B shows the mean throughput for each experiment phase. Panel C shows the overshoot per subject, and Panel D shows the mean for each experiment phase. Similarly, Panel E shows the path efficiency per subject, and Panel F shows the mean for each experiment phase.

Mentions: Fig. 5 summarises the three movement metrics for each subject during the two phases of the experiment: the IMU-control phase (IMU, dark bars), and the ANN-control phase (ANN, light bars). Throughput (panels A and B) was generally low, since it was limited by the values of Index of Difficulty, and the self-selected movement speed (median for IMU: 0.74 bits/s, interquartile range: 0.65–0.84 bits/s, median for ANN: 0.55 bits/s, interquartile range: 0.55–0.62 bits/s, Wilcoxon rank sum test ). Overshoot (panels C and D) was near zero, suggesting good control of movement speed (median for IMU: 0.015, interquartile range: 0.011–0.019, median for ANN: 0.015, interquartile range: 0.003–0.033, Wilcoxon rank sum test ). Lastly, when the participants controlled the virtual forearm with the IMU they showed better path efficiency than the ANN controller (panels E and F, median for IMU: 0.78, interquartile range: 0.69–0.83, median for ANN: 0.58, interquartile range: 0.55–0.70, Wilcoxon rank sum test ).


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)

Summary of the movement metrics. Panel A shows the throughput for each subject, in the two experiment phases: IMU-control (IMU, dark bars), and ANN-control phase (ANN, light bars). Panel B shows the mean throughput for each experiment phase. Panel C shows the overshoot per subject, and Panel D shows the mean for each experiment phase. Similarly, Panel E shows the path efficiency per subject, and Panel F shows the mean for each experiment phase.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0025: Summary of the movement metrics. Panel A shows the throughput for each subject, in the two experiment phases: IMU-control (IMU, dark bars), and ANN-control phase (ANN, light bars). Panel B shows the mean throughput for each experiment phase. Panel C shows the overshoot per subject, and Panel D shows the mean for each experiment phase. Similarly, Panel E shows the path efficiency per subject, and Panel F shows the mean for each experiment phase.
Mentions: Fig. 5 summarises the three movement metrics for each subject during the two phases of the experiment: the IMU-control phase (IMU, dark bars), and the ANN-control phase (ANN, light bars). Throughput (panels A and B) was generally low, since it was limited by the values of Index of Difficulty, and the self-selected movement speed (median for IMU: 0.74 bits/s, interquartile range: 0.65–0.84 bits/s, median for ANN: 0.55 bits/s, interquartile range: 0.55–0.62 bits/s, Wilcoxon rank sum test ). Overshoot (panels C and D) was near zero, suggesting good control of movement speed (median for IMU: 0.015, interquartile range: 0.011–0.019, median for ANN: 0.015, interquartile range: 0.003–0.033, Wilcoxon rank sum test ). Lastly, when the participants controlled the virtual forearm with the IMU they showed better path efficiency than the ANN controller (panels E and F, median for IMU: 0.78, interquartile range: 0.69–0.83, median for ANN: 0.58, interquartile range: 0.55–0.70, Wilcoxon rank sum test ).

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