Prediction of Gait Kinematics and Kinetics : A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy
Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
Bioengineering (Basel, Switzerland) - 10(2023), 10 vom: 04. Okt. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Amrani El Yaakoubi, Nissrin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Electroencephalograms (EEGs) |
|---|
| Anmerkungen: |
Date Revised 30.10.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/bioengineering10101162 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM363849092 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM363849092 | ||
| 003 | DE-627 | ||
| 005 | 20231226094219.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/bioengineering10101162 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1212.xml |
| 035 | |a (DE-627)NLM363849092 | ||
| 035 | |a (NLM)37892892 | ||
| 035 | |a (PII)1162 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Amrani El Yaakoubi, Nissrin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction of Gait Kinematics and Kinetics |b A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 30.10.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a electroencephalograms (EEGs) | |
| 650 | 4 | |a electromyograms (EMGs) | |
| 650 | 4 | |a gait analysis | |
| 650 | 4 | |a joint angles | |
| 650 | 4 | |a joint moments | |
| 650 | 4 | |a joint torque | |
| 650 | 4 | |a kinematics | |
| 650 | 4 | |a kinetics | |
| 650 | 4 | |a neural network | |
| 650 | 4 | |a prediction | |
| 700 | 1 | |a McDonald, Caitlin |e verfasserin |4 aut | |
| 700 | 1 | |a Lennon, Olive |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Bioengineering (Basel, Switzerland) |d 2014 |g 10(2023), 10 vom: 04. Okt. |w (DE-627)NLM259987786 |x 2306-5354 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2023 |g number:10 |g day:04 |g month:10 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/bioengineering10101162 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2023 |e 10 |b 04 |c 10 | ||