TY - JOUR
T1 - Artificial neural network based ankle joint angle estimation using instrumented foot insoles
AU - Sivakumar, Saaveethya
AU - Gopalai, Alpha Agape
AU - Lim, King Hann
AU - Gouwanda, Darwin
N1 - Funding Information:
This work is supported by the Ministry of Science, Technology and Innovation (MOSTI) Malaysia under the project number 06-02-10-SF0289 and Advanced Engineering Platform Monash University Malaysia .
Funding Information:
This work is supported by the Ministry of Science, Technology and Innovation (MOSTI) Malaysia under the project number 06-02-10-SF0289 and Advanced Engineering Platform Monash University Malaysia.
Publisher Copyright:
© 2019 Elsevier Ltd
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/9
Y1 - 2019/9
N2 - Current trends for long term gait monitoring relies on estimations made via machine learning. As such, this work investigates the viability of feedforward neural network (FFNN) to estimate ankle angles using ground reaction forces (GRFs) acquired from a wearable foot insole system. Inputs from nine salient gait events were selected for network training. These nine gait events are loading response (LR), pre-initial single support (pre-ISS), initial single support (ISS), post-initial single support (post-ISS), mid single support (MSS), pre-terminal single support (pre-TSS), terminal single support (TSS), post-terminal single support (post-TSS) and pre swing (PSW). Ankle angles are estimated with ρ¯>0.95 and NRMSE¯: 5.475 ± 1.34% for left leg in-sample estimations, 5.614 ± 1.1% for right leg in-sample estimations, 5.745 ± 1.642% for left leg out-sample estimations and 6.536 ± 0.9798% for right leg out-sample estimations. This method potentially eliminates the need of multiple wearable sensors and allow ankle angle estimation for long term basis with the aid of a simpler sensor layout. Therefore, the proposed work investigates the feasibility of using ANN for lower limb angle estimations from ground reaction forces measured using wearable foot insoles.
AB - Current trends for long term gait monitoring relies on estimations made via machine learning. As such, this work investigates the viability of feedforward neural network (FFNN) to estimate ankle angles using ground reaction forces (GRFs) acquired from a wearable foot insole system. Inputs from nine salient gait events were selected for network training. These nine gait events are loading response (LR), pre-initial single support (pre-ISS), initial single support (ISS), post-initial single support (post-ISS), mid single support (MSS), pre-terminal single support (pre-TSS), terminal single support (TSS), post-terminal single support (post-TSS) and pre swing (PSW). Ankle angles are estimated with ρ¯>0.95 and NRMSE¯: 5.475 ± 1.34% for left leg in-sample estimations, 5.614 ± 1.1% for right leg in-sample estimations, 5.745 ± 1.642% for left leg out-sample estimations and 6.536 ± 0.9798% for right leg out-sample estimations. This method potentially eliminates the need of multiple wearable sensors and allow ankle angle estimation for long term basis with the aid of a simpler sensor layout. Therefore, the proposed work investigates the feasibility of using ANN for lower limb angle estimations from ground reaction forces measured using wearable foot insoles.
KW - Artificial neural networks
KW - Gait
KW - Kinematics
KW - Kinetics
KW - Wearable foot insoles
UR - http://www.scopus.com/inward/record.url?scp=85073705460&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2019.101614
DO - 10.1016/j.bspc.2019.101614
M3 - Article
AN - SCOPUS:85073705460
SN - 1746-8094
VL - 54
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 101614
ER -