Abstract
Ensuring robustness of myocontrol algorithms for prosthetic devices is an important challenge. Robustness needs to be maintained under nonstationarities, e.g. due to electrode shifts after donning and doffing, sweating, additional weight or varying arm positions. Such nonstationary behavior changes the signal distributions - a scenario often referred to as covariate shift. This circumstance causes a significant decrease in classification accuracy in daily life applications. Re-training is possible but it is time consuming since it requires a large number of trials. In this paper, we propose to adapt the EMG classifier by a small calibration set only, which is able to capture the relevant aspects of the nonstationarities, but requires re-training data of only very short duration. We tested this strategy on signals acquired across 5 days in able-bodied individuals. The results showed that an estimator that shrinks the training model parameters towards the calibration set parameters significantly increased the classifier performance across different testing days. Even when using only one trial per class as re-training data for each day, the classification accuracy remained > 92% over five days. These results indicate that the proposed methodology can be a practical means for improving robustness in pattern recognition methods for myocontrol.
| Original language | English |
|---|---|
| Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4370-4373 |
| Number of pages | 4 |
| ISBN (Print) | 9781424479290 |
| DOIs | |
| Publication status | Published - 2014 Nov 2 |
| Externally published | Yes |
| Event | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: 2014 Aug 26 → 2014 Aug 30 |
Other
| Other | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 14/8/26 → 14/8/30 |
Fingerprint
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications
- Biomedical Engineering
Cite this
Covariate shift adaptation in EMG pattern recognition for prosthetic device control. / Vidovic, Marina M C; Paredes, Liliana P.; Hwang, Han Jeong; Amsüss, Sebastian; Pahl, Jaspar; Hahne, Janne M.; Graimann, Bernhard; Farina, Dario; Muller, Klaus.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4370-4373 6944592.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Covariate shift adaptation in EMG pattern recognition for prosthetic device control
AU - Vidovic, Marina M C
AU - Paredes, Liliana P.
AU - Hwang, Han Jeong
AU - Amsüss, Sebastian
AU - Pahl, Jaspar
AU - Hahne, Janne M.
AU - Graimann, Bernhard
AU - Farina, Dario
AU - Muller, Klaus
PY - 2014/11/2
Y1 - 2014/11/2
N2 - Ensuring robustness of myocontrol algorithms for prosthetic devices is an important challenge. Robustness needs to be maintained under nonstationarities, e.g. due to electrode shifts after donning and doffing, sweating, additional weight or varying arm positions. Such nonstationary behavior changes the signal distributions - a scenario often referred to as covariate shift. This circumstance causes a significant decrease in classification accuracy in daily life applications. Re-training is possible but it is time consuming since it requires a large number of trials. In this paper, we propose to adapt the EMG classifier by a small calibration set only, which is able to capture the relevant aspects of the nonstationarities, but requires re-training data of only very short duration. We tested this strategy on signals acquired across 5 days in able-bodied individuals. The results showed that an estimator that shrinks the training model parameters towards the calibration set parameters significantly increased the classifier performance across different testing days. Even when using only one trial per class as re-training data for each day, the classification accuracy remained > 92% over five days. These results indicate that the proposed methodology can be a practical means for improving robustness in pattern recognition methods for myocontrol.
AB - Ensuring robustness of myocontrol algorithms for prosthetic devices is an important challenge. Robustness needs to be maintained under nonstationarities, e.g. due to electrode shifts after donning and doffing, sweating, additional weight or varying arm positions. Such nonstationary behavior changes the signal distributions - a scenario often referred to as covariate shift. This circumstance causes a significant decrease in classification accuracy in daily life applications. Re-training is possible but it is time consuming since it requires a large number of trials. In this paper, we propose to adapt the EMG classifier by a small calibration set only, which is able to capture the relevant aspects of the nonstationarities, but requires re-training data of only very short duration. We tested this strategy on signals acquired across 5 days in able-bodied individuals. The results showed that an estimator that shrinks the training model parameters towards the calibration set parameters significantly increased the classifier performance across different testing days. Even when using only one trial per class as re-training data for each day, the classification accuracy remained > 92% over five days. These results indicate that the proposed methodology can be a practical means for improving robustness in pattern recognition methods for myocontrol.
UR - http://www.scopus.com/inward/record.url?scp=84929471501&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929471501&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6944592
DO - 10.1109/EMBC.2014.6944592
M3 - Conference contribution
SN - 9781424479290
SP - 4370
EP - 4373
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -