title: Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network.
abstract: In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840-0.989 and NRMSE% = 10.693-15.894%; normal group: r = 0.847-0.988 and NRMSE% = 10.920-19.216%; fast group: r = 0.823-0.953 and NRMSE% = 12.009-20.182%; healthy group: r = 0.836-0.976 and NRMSE% = 12.920-18.088%; and AIS group: r = 0.917-0.993 and NRMSE% = 7.914-15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.
authors.forename: Taeyong
authors.initials: T
authors.lastname: Sim
authors.forename: Hyunbin
authors.initials: H
authors.lastname: Kwon
authors.forename: Seung Eel
authors.initials: SE
authors.lastname: Oh
authors.forename: Su-Bin
authors.initials: SB
authors.lastname: Joo
authors.forename: Ahnryul
authors.initials: A
authors.lastname: Choi
authors.forename: Hyun Mu
authors.initials: HM
authors.lastname: Heo
authors.forename: Kisun
authors.initials: K
authors.lastname: Kim
authors.forename: Joung Hwan
authors.initials: JH
authors.lastname: Mun
mesh_terms: D000293:Adolescent; D001696:Biomechanical Phenomena; D005260:Female; D005528:Foot / Q000502:physiology* / Q000503:physiopathology; D005684:Gait; D006801:Humans; D008297:Male; D055595:Mechanical Phenomena; D016571:Neural Networks, Computer; D011312:Pressure; D025341:Principal Component Analysis; D012600:Scoliosis / Q000503:physiopathology; D058067:Wavelet Analysis; D055815:Young Adult