EMG and IMU Data Fusion for Locomotion Mode Classification in Transtibial Amputees.
Despite recent advancements in prosthetic technology, lower-limb amputees often remain limited to passive prostheses, which leads to an asymmetric gait and increased energy expenditure. Developing active prostheses with effective control systems is important to improve mobility for these individuals...
| Publicado en: | Prosthesis (2673-1592) Vol. 5; no. 4; pp. 1232 - 1257 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
MDPI
Dec2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174460419&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174460419 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26731592 MVQF jtl: Prosthesis (2673-1592) issn: 26731592 maglogo: N pubinfo: dt: Dec2023 vid: 5 iid: 4 pid: 97109 pub: MDPI artinfo: ui: 174460419 174460419 174460419 10.3390/prosthesis5040085 174460419 ppf: 1232 ppct: 25 formats: tig: atl: EMG and IMU Data Fusion for Locomotion Mode Classification in Transtibial Amputees. aug: au: Gonzales-Huisa, Omar A. Oshiro, Gonzalo Abarca, Victoria E. Chavez-Echajaya, Jorge G. Elias, Dante A. affil: Faculty of Science and Engineering, Electronic Engineering, Pontificia Universidad Católica del Perú, Lima 15088, Peru sug: subj: Below-Knee Amputation Amputees Psychosocial Factors Electromyography Wearable Sensors Locomotion Classification Machine Learning Human Male Female Adult Middle Age Algorithms Support Vector Machine Memory, Short Term Limb Prosthesis Analysis of Variance Post Hoc Analysis Data Analysis Software Descriptive Statistics Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Despite recent advancements in prosthetic technology, lower-limb amputees often remain limited to passive prostheses, which leads to an asymmetric gait and increased energy expenditure. Developing active prostheses with effective control systems is important to improve mobility for these individuals. This study presents a machine-learning-based approach to classify five distinct locomotion tasks: ground-level walking (GWL), ramp ascent (RPA), ramp descent (RPD), stairs ascent (SSA), and stairs descent (SSD). The dataset comprises fused electromyographic (EMG) and inertial measurement unit (IMU) signals from twenty non-amputated and five transtibial amputated participants. EMG sensors were strategically positioned on the thigh muscles, while IMU sensors were placed on various leg segments. The performance of two classification algorithms, support vector machine (SVM) and long short-term memory (LSTM), were evaluated on segmented data. The results indicate that SVM models outperform LSTM models in accuracy, precision, and F1 score in the individual evaluation of amputee and non-amputee datasets for 80–20 and 50–50 data distributions. In the 80–20 distribution, an accuracy of 95.46% and 95.35% was obtained with SVM for non-amputees and amputees, respectively. An accuracy of 93.33% and 93.30% was obtained for non-amputees and amputees by using LSTM, respectively. LSTM models show more robustness and inter-population generalizability than SVM models when applying domain-adaptation techniques. Furthermore, the average classification latency for SVM and LSTM models was 19.84 ms and 37.07 ms, respectively, within acceptable limits for real-time applications. This study contributes to the field by comprehensively comparing SVM and LSTM classifiers for locomotion tasks, laying the foundation for the future development of real-time control systems for active transtibial prostheses. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|