Lower limb motion recognition based on surface electromyography decoding using S-transform energy concentration Article Swipe
Baoyu Li
,
Guanghau Xu
,
Jinju Pei
,
Dan Luo
,
Hui Li
,
Chenghang Du
,
Sicong Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4405403/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4405403/v1
Lower limb motion recognition of based surface electromyography (EMG) aims to provide a more natural and effective human-computer interaction for intelligent prostheses. Accurate motion recognition relies on high-quality EMG decoding, and the key to improving the efficiency of EMG pattern recognition is to optimize signal feature extraction. The phase information of the EMG signal cannot be neglected for motion pattern recognition. Therefore, we proposed a decoding scheme for surface EMG signals based on S-transform energy concentration for lower limb motion recognition, including level walk, stair ascent, stair descent, and crossing obstacles. First, six-channel lower limb EMG signals of the 10 subjects during four kinds of movements were experimentally acquired, and the correlation of multi-channel EMG signals was analyzed to find the best combination of the EMG signal for exploring the classification effect based on the support vector machine (SVM) between single-channel signals and multi-channel signals fusion. The results showed that based on the simple time-frequency domain features with better motion recognition are the semi-tendon muscle and the rectus femoris muscle, and based on the S-transform energy concentration with better motion recognition is the medial gastrocnemius muscle and the rectus femoris muscle. Finally, taking the rectus femoris signal as an example, the motion recognition accuracy of 10 subjects under the two schemes was calculated. The mean value of motion recognition accuracy based on simple time-frequency domain features was 80.71%, and the mean value of motion recognition accuracy based on S-transformed energy concentration was 93.70%. It validated that the S-transform energy concentration scheme has a better recognition effect, and the accuracy of multi-channel signal fusion pattern recognition based on S-transform energy concentration is higher than 96%, which is beneficial to promote the practical application of EMG signals in motion recognition. It has the potential to improve the adaptive human-robot interaction control of the prosthesis.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4405403/v1
- https://www.researchsquare.com/article/rs-4405403/latest.pdf
- OA Status
- gold
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399756871
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399756871Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-4405403/v1Digital Object Identifier
- Title
-
Lower limb motion recognition based on surface electromyography decoding using S-transform energy concentrationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-17Full publication date if available
- Authors
-
Baoyu Li, Guanghau Xu, Jinju Pei, Dan Luo, Hui Li, Chenghang Du, Sicong ZhangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4405403/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4405403/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-4405403/latest.pdfDirect OA link when available
- Concepts
-
Electromyography, Decoding methods, Motion (physics), Artificial intelligence, Computer science, Pattern recognition (psychology), Computer vision, Speech recognition, Physical medicine and rehabilitation, Medicine, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4399756871 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-4405403/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-4405403/v1 |
| ids.openalex | https://openalex.org/W4399756871 |
| fwci | |
| type | preprint |
| title | Lower limb motion recognition based on surface electromyography decoding using S-transform energy concentration |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10784 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9521999955177307 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Muscle activation and electromyography studies |
| topics[1].id | https://openalex.org/T11398 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9265999794006348 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1709 |
| topics[1].subfield.display_name | Human-Computer Interaction |
| topics[1].display_name | Hand Gesture Recognition Systems |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2777515770 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7508329749107361 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q507369 |
| concepts[0].display_name | Electromyography |
| concepts[1].id | https://openalex.org/C57273362 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6179121136665344 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q576722 |
| concepts[1].display_name | Decoding methods |
| concepts[2].id | https://openalex.org/C104114177 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5456084609031677 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q79782 |
| concepts[2].display_name | Motion (physics) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4935966730117798 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.4793999195098877 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.39176130294799805 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C31972630 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3906536102294922 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[6].display_name | Computer vision |
| concepts[7].id | https://openalex.org/C28490314 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3551814556121826 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[7].display_name | Speech recognition |
| concepts[8].id | https://openalex.org/C99508421 |
| concepts[8].level | 1 |
| concepts[8].score | 0.34619975090026855 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2678675 |
| concepts[8].display_name | Physical medicine and rehabilitation |
| concepts[9].id | https://openalex.org/C71924100 |
| concepts[9].level | 0 |
| concepts[9].score | 0.17681410908699036 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[9].display_name | Medicine |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.10189324617385864 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| keywords[0].id | https://openalex.org/keywords/electromyography |
| keywords[0].score | 0.7508329749107361 |
| keywords[0].display_name | Electromyography |
| keywords[1].id | https://openalex.org/keywords/decoding-methods |
| keywords[1].score | 0.6179121136665344 |
| keywords[1].display_name | Decoding methods |
| keywords[2].id | https://openalex.org/keywords/motion |
| keywords[2].score | 0.5456084609031677 |
| keywords[2].display_name | Motion (physics) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.4935966730117798 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.4793999195098877 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.39176130294799805 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/computer-vision |
| keywords[6].score | 0.3906536102294922 |
| keywords[6].display_name | Computer vision |
| keywords[7].id | https://openalex.org/keywords/speech-recognition |
| keywords[7].score | 0.3551814556121826 |
| keywords[7].display_name | Speech recognition |
| keywords[8].id | https://openalex.org/keywords/physical-medicine-and-rehabilitation |
| keywords[8].score | 0.34619975090026855 |
| keywords[8].display_name | Physical medicine and rehabilitation |
| keywords[9].id | https://openalex.org/keywords/medicine |
| keywords[9].score | 0.17681410908699036 |
| keywords[9].display_name | Medicine |
| keywords[10].id | https://openalex.org/keywords/algorithm |
| keywords[10].score | 0.10189324617385864 |
| keywords[10].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-4405403/v1 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-4405403/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-4405403/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5003929707 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5499-9700 |
| authorships[0].author.display_name | Baoyu Li |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[0].institutions[0].id | https://openalex.org/I87445476 |
| authorships[0].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Baoyu Li |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[1].author.id | https://openalex.org/A5102077262 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Guanghau Xu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].raw_affiliation_string | Jiaotong University |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I87445476 |
| authorships[1].affiliations[1].raw_affiliation_string | Xi'an Jiaotong University |
| authorships[1].institutions[0].id | https://openalex.org/I87445476 |
| authorships[1].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Guanghau Xu |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Jiaotong University, Xi'an Jiaotong University |
| authorships[2].author.id | https://openalex.org/A5030209576 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Jinju Pei |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[2].institutions[0].id | https://openalex.org/I87445476 |
| authorships[2].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jinju Pei |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[3].author.id | https://openalex.org/A5101580826 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0663-1706 |
| authorships[3].author.display_name | Dan Luo |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[3].institutions[0].id | https://openalex.org/I87445476 |
| authorships[3].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Dan Luo |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[4].author.id | https://openalex.org/A5101677633 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0752-5710 |
| authorships[4].author.display_name | Hui Li |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[4].institutions[0].id | https://openalex.org/I87445476 |
| authorships[4].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hui Li |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[5].author.id | https://openalex.org/A5103015825 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-9938-2173 |
| authorships[5].author.display_name | Chenghang Du |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[5].affiliations[0].raw_affiliation_string | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[5].institutions[0].id | https://openalex.org/I87445476 |
| authorships[5].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Chenghang Du |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[6].author.id | https://openalex.org/A5101933468 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-0279-8939 |
| authorships[6].author.display_name | Sicong Zhang |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| authorships[6].institutions[0].id | https://openalex.org/I87445476 |
| authorships[6].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Sicong Zhang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-4405403/latest.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Lower limb motion recognition based on surface electromyography decoding using S-transform energy concentration |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10784 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9521999955177307 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Muscle activation and electromyography studies |
| related_works | https://openalex.org/W2376139493, https://openalex.org/W2911808920, https://openalex.org/W2141253262, https://openalex.org/W3156756500, https://openalex.org/W4251090744, https://openalex.org/W2386293158, https://openalex.org/W2433638048, https://openalex.org/W2006061919, https://openalex.org/W2391009298, https://openalex.org/W1558657480 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-4405403/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-4405403/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-4405403/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-4405403/v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-4405403/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-4405403/v1 |
| publication_date | 2024-06-17 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2012158430, https://openalex.org/W2083852388, https://openalex.org/W2115200309, https://openalex.org/W3171781543, https://openalex.org/W2115881480, https://openalex.org/W2990072603, https://openalex.org/W2966844138, https://openalex.org/W3174953597, https://openalex.org/W2050381884, https://openalex.org/W3046866666, https://openalex.org/W2906499678, https://openalex.org/W1022507185, https://openalex.org/W4246178447, https://openalex.org/W3032981784, https://openalex.org/W1986754283, https://openalex.org/W2008230146, https://openalex.org/W4226101141, https://openalex.org/W2021518667, https://openalex.org/W3043659444, https://openalex.org/W2099297971 |
| referenced_works_count | 20 |
| abstract_inverted_index.a | 13, 65, 253 |
| abstract_inverted_index.10 | 100, 206 |
| abstract_inverted_index.It | 244, 289 |
| abstract_inverted_index.an | 199 |
| abstract_inverted_index.as | 198 |
| abstract_inverted_index.be | 56 |
| abstract_inverted_index.in | 286 |
| abstract_inverted_index.is | 42, 182, 271, 276 |
| abstract_inverted_index.of | 5, 38, 51, 98, 105, 113, 124, 205, 217, 233, 260, 283, 300 |
| abstract_inverted_index.on | 27, 73, 134, 152, 173, 222, 238, 267 |
| abstract_inverted_index.to | 11, 34, 43, 119, 278, 293 |
| abstract_inverted_index.we | 63 |
| abstract_inverted_index.EMG | 29, 39, 53, 70, 96, 115, 126, 284 |
| abstract_inverted_index.The | 48, 147, 214 |
| abstract_inverted_index.and | 16, 31, 89, 110, 143, 166, 171, 187, 229, 257 |
| abstract_inverted_index.are | 162 |
| abstract_inverted_index.for | 20, 58, 68, 77, 128 |
| abstract_inverted_index.has | 252, 290 |
| abstract_inverted_index.key | 33 |
| abstract_inverted_index.the | 32, 36, 52, 99, 111, 121, 125, 130, 135, 153, 163, 167, 174, 183, 188, 194, 201, 209, 230, 247, 258, 280, 291, 295, 301 |
| abstract_inverted_index.two | 210 |
| abstract_inverted_index.was | 117, 212, 227, 242 |
| abstract_inverted_index.96%, | 274 |
| abstract_inverted_index.aims | 10 |
| abstract_inverted_index.best | 122 |
| abstract_inverted_index.find | 120 |
| abstract_inverted_index.four | 103 |
| abstract_inverted_index.limb | 2, 79, 95 |
| abstract_inverted_index.mean | 215, 231 |
| abstract_inverted_index.more | 14 |
| abstract_inverted_index.than | 273 |
| abstract_inverted_index.that | 150, 246 |
| abstract_inverted_index.were | 107 |
| abstract_inverted_index.with | 158, 178 |
| abstract_inverted_index.(EMG) | 9 |
| abstract_inverted_index.(SVM) | 139 |
| abstract_inverted_index.Lower | 1 |
| abstract_inverted_index.based | 6, 72, 133, 151, 172, 221, 237, 266 |
| abstract_inverted_index.kinds | 104 |
| abstract_inverted_index.level | 83 |
| abstract_inverted_index.lower | 78, 94 |
| abstract_inverted_index.phase | 49 |
| abstract_inverted_index.stair | 85, 87 |
| abstract_inverted_index.under | 208 |
| abstract_inverted_index.value | 216, 232 |
| abstract_inverted_index.walk, | 84 |
| abstract_inverted_index.which | 275 |
| abstract_inverted_index.First, | 92 |
| abstract_inverted_index.better | 159, 179, 254 |
| abstract_inverted_index.cannot | 55 |
| abstract_inverted_index.domain | 156, 225 |
| abstract_inverted_index.during | 102 |
| abstract_inverted_index.effect | 132 |
| abstract_inverted_index.energy | 75, 176, 240, 249, 269 |
| abstract_inverted_index.fusion | 263 |
| abstract_inverted_index.higher | 272 |
| abstract_inverted_index.medial | 184 |
| abstract_inverted_index.motion | 3, 24, 59, 80, 160, 180, 202, 218, 234, 287 |
| abstract_inverted_index.muscle | 165, 186 |
| abstract_inverted_index.rectus | 168, 189, 195 |
| abstract_inverted_index.relies | 26 |
| abstract_inverted_index.scheme | 67, 251 |
| abstract_inverted_index.showed | 149 |
| abstract_inverted_index.signal | 45, 54, 127, 197, 262 |
| abstract_inverted_index.simple | 154, 223 |
| abstract_inverted_index.taking | 193 |
| abstract_inverted_index.vector | 137 |
| abstract_inverted_index.80.71%, | 228 |
| abstract_inverted_index.93.70%. | 243 |
| abstract_inverted_index.ascent, | 86 |
| abstract_inverted_index.between | 140 |
| abstract_inverted_index.control | 299 |
| abstract_inverted_index.effect, | 256 |
| abstract_inverted_index.feature | 46 |
| abstract_inverted_index.femoris | 169, 190, 196 |
| abstract_inverted_index.fusion. | 146 |
| abstract_inverted_index.improve | 294 |
| abstract_inverted_index.machine | 138 |
| abstract_inverted_index.muscle, | 170 |
| abstract_inverted_index.muscle. | 191 |
| abstract_inverted_index.natural | 15 |
| abstract_inverted_index.pattern | 40, 60, 264 |
| abstract_inverted_index.promote | 279 |
| abstract_inverted_index.provide | 12 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.schemes | 211 |
| abstract_inverted_index.signals | 71, 97, 116, 142, 145, 285 |
| abstract_inverted_index.support | 136 |
| abstract_inverted_index.surface | 7, 69 |
| abstract_inverted_index.Accurate | 23 |
| abstract_inverted_index.Finally, | 192 |
| abstract_inverted_index.accuracy | 204, 220, 236, 259 |
| abstract_inverted_index.adaptive | 296 |
| abstract_inverted_index.analyzed | 118 |
| abstract_inverted_index.crossing | 90 |
| abstract_inverted_index.decoding | 66 |
| abstract_inverted_index.descent, | 88 |
| abstract_inverted_index.example, | 200 |
| abstract_inverted_index.features | 157, 226 |
| abstract_inverted_index.optimize | 44 |
| abstract_inverted_index.proposed | 64 |
| abstract_inverted_index.subjects | 101, 207 |
| abstract_inverted_index.acquired, | 109 |
| abstract_inverted_index.decoding, | 30 |
| abstract_inverted_index.effective | 17 |
| abstract_inverted_index.exploring | 129 |
| abstract_inverted_index.improving | 35 |
| abstract_inverted_index.including | 82 |
| abstract_inverted_index.movements | 106 |
| abstract_inverted_index.neglected | 57 |
| abstract_inverted_index.potential | 292 |
| abstract_inverted_index.practical | 281 |
| abstract_inverted_index.validated | 245 |
| abstract_inverted_index.Therefore, | 62 |
| abstract_inverted_index.beneficial | 277 |
| abstract_inverted_index.efficiency | 37 |
| abstract_inverted_index.obstacles. | 91 |
| abstract_inverted_index.S-transform | 74, 175, 248, 268 |
| abstract_inverted_index.application | 282 |
| abstract_inverted_index.calculated. | 213 |
| abstract_inverted_index.combination | 123 |
| abstract_inverted_index.correlation | 112 |
| abstract_inverted_index.extraction. | 47 |
| abstract_inverted_index.human-robot | 297 |
| abstract_inverted_index.information | 50 |
| abstract_inverted_index.intelligent | 21 |
| abstract_inverted_index.interaction | 19, 298 |
| abstract_inverted_index.prostheses. | 22 |
| abstract_inverted_index.prosthesis. | 302 |
| abstract_inverted_index.recognition | 4, 25, 41, 161, 181, 203, 219, 235, 255, 265 |
| abstract_inverted_index.semi-tendon | 164 |
| abstract_inverted_index.six-channel | 93 |
| abstract_inverted_index.high-quality | 28 |
| abstract_inverted_index.recognition, | 81 |
| abstract_inverted_index.recognition. | 61, 288 |
| abstract_inverted_index.S-transformed | 239 |
| abstract_inverted_index.concentration | 76, 177, 241, 250, 270 |
| abstract_inverted_index.gastrocnemius | 185 |
| abstract_inverted_index.multi-channel | 114, 144, 261 |
| abstract_inverted_index.classification | 131 |
| abstract_inverted_index.experimentally | 108 |
| abstract_inverted_index.human-computer | 18 |
| abstract_inverted_index.single-channel | 141 |
| abstract_inverted_index.time-frequency | 155, 224 |
| abstract_inverted_index.electromyography | 8 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5102077262 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I87445476 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.10343772 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |