| Title |
Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements
|
|---|---|
| Published in |
Journal of NeuroEngineering and Rehabilitation, July 2017
|
| DOI | 10.1186/s12984-017-0284-4 |
| Pubmed ID | |
| Authors |
Agamemnon Krasoulis, Iris Kyranou, Mustapha Suphi Erden, Kianoush Nazarpour, Sethu Vijayakumar |
| Abstract |
Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a simultaneous requirement for a large number of electromyogram (EMG) electrodes. We propose to address these two issues by using a multi-modal approach which combines surface electromyography (sEMG) with inertial measurements (IMs) and an appropriate training data collection paradigm. We demonstrate that this can significantly improve classification performance as compared to conventional techniques exclusively based on sEMG signals. We collected and analyzed a large dataset comprising recordings with 20 able-bodied and two amputee participants executing 40 movements. Additionally, we conducted a novel real-time prosthetic hand control experiment with 11 able-bodied subjects and an amputee by using a state-of-the-art commercial prosthetic hand. A systematic performance comparison was carried out to investigate the potential benefit of incorporating IMs in prosthetic hand control. The inclusion of IM data improved performance significantly, by increasing classification accuracy (CA) in the offline analysis and improving completion rates (CRs) in the real-time experiment. Our findings were consistent across able-bodied and amputee subjects. Integrating the sEMG electrodes and IM sensors within a single sensor package enabled us to achieve high-level performance by using on average 4-6 sensors. The results from our experiments suggest that IMs can form an excellent complimentary source signal for upper-limb myoelectric prostheses. We trust that multi-modal control solutions have the potential of improving the usability of upper-extremity prostheses in real-life applications. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 57% |
| United States | 2 | 29% |
| Canada | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 43% |
| Members of the public | 3 | 43% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 251 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 251 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 55 | 22% |
| Student > Master | 44 | 18% |
| Student > Bachelor | 24 | 10% |
| Researcher | 23 | 9% |
| Professor | 9 | 4% |
| Other | 29 | 12% |
| Unknown | 67 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 131 | 52% |
| Computer Science | 17 | 7% |
| Neuroscience | 9 | 4% |
| Medicine and Dentistry | 7 | 3% |
| Nursing and Health Professions | 3 | 1% |
| Other | 9 | 4% |
| Unknown | 75 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 December 2021.
All research outputs
#3,672,392
of 22,759,618 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#204
of 1,278 outputs
Outputs of similar age
#65,835
of 311,829 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#3
of 27 outputs
Altmetric has tracked 22,759,618 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,278 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,829 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.