MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning

运动学 肌电图 计算机科学 生物力学 人工智能 物理医学与康复 机器学习 计算机视觉 医学 物理 解剖 经典力学
作者
Ali Nasr,Sydney Bell,Jiayuan He,Rachel L. Whittaker,Ning Jiang,Clark R. Dickerson,John McPhee
出处
期刊:Journal of Neural Engineering [IOP Publishing]
卷期号:18 (4): 0460d3-0460d3 被引量:46
标识
DOI:10.1088/1741-2552/ac1adc
摘要

Objective.This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque.Approach.The regression models, collectively known as MuscleNET, take one of four forms: ANN (forward artificial neural network), RNN (recurrent neural network), CNN (convolutional neural network), and RCNN (recurrent convolutional neural network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model's input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models' inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data.Main results.Results were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals.Significance.All forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
jy完成签到,获得积分20
1秒前
翊悠yo完成签到 ,获得积分10
1秒前
1秒前
希望天下0贩的0应助ghtsmile采纳,获得10
2秒前
3秒前
Dylan发布了新的文献求助20
3秒前
4秒前
柿子发布了新的文献求助10
4秒前
Milktea123发布了新的文献求助10
5秒前
5秒前
CodeCraft应助健忘洋葱采纳,获得10
5秒前
5秒前
VitaminK发布了新的文献求助10
5秒前
汉堡包应助西西笑嘻嘻采纳,获得10
5秒前
5秒前
西西完成签到,获得积分10
6秒前
动容完成签到,获得积分10
6秒前
纯情的浩然完成签到,获得积分10
7秒前
单纯念寒发布了新的文献求助10
7秒前
7秒前
7秒前
Aesias发布了新的文献求助10
8秒前
爆米花应助牛顿怒锤泰勒采纳,获得10
9秒前
深情安青应助百忧解采纳,获得10
9秒前
Felix发布了新的文献求助10
10秒前
于无声处完成签到 ,获得积分10
10秒前
11秒前
SciGPT应助笑点低的静竹采纳,获得10
11秒前
紫杉罗罗发布了新的文献求助10
11秒前
科研通AI2S应助满月寂照采纳,获得10
11秒前
12秒前
12秒前
VitaminK完成签到,获得积分10
12秒前
13秒前
13秒前
15秒前
15秒前
M跃发布了新的文献求助10
16秒前
16秒前
16秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
A Case Study on Hotels as Noncongregate Emergency Living Accommodations for Returning Citizens 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7764187
求助须知:如何正确求助?哪些是违规求助? 9308406
关于积分的说明 20305620
捐赠科研通 7348813
什么是DOI,文献DOI怎么找? 3314276
关于科研通互助平台的介绍 2463843
邀请新用户注册赠送积分活动 2328387