Foot-Healthcare Application Using Inertial Sensor: Estimating First Metatarsophalangeal Angle From Foot Motion During Walking

• Published in: IEEE sensors journal 2022-02, Vol.22 (3), p.2835-2844
• Main Authors: Huang, Chenhui, Fukushi, Kenichiro, Wang, Zhenwei, Kajitani, Hiroshi, Nihey, Fumiyuki, Pokka, Hannah, Narasaki, Hiroko, Nakano, Hiroaki, Nakahara, Kentaro
• Format: Article
• Language: English
• Subjects: Atmospheric measurements; Correlation coefficients; Estimation; Foot; foot health; foot-motion; Footwear; Gait; gait analysis; hallux valgus; Health care; Inertial sensing devices; Inertial sensor; Intelligent sensors; Legged locomotion; Machine learning; Monitoring; Motion sensors; Particle measurements; Rigid structures; Root-mean-square errors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3138485

Our purpose was to demonstrate the possibility of providing foot-healthcare application by using an in-shoe motion sensor (IMS) through validating the feasibility of applying an IMS for measuring the first metatarsophalangeal angle (FMTPA), which is the most important parameter regarding the common foot problem hallux valgus. Methods: The IMS signals can represent foot motions when the mid-foot and hindfoot were modelled as a rigid body. FMTPAs can be estimated from the foot-motion signals measured using an IMS embedded beneath the foot arch near the calcaneus side using a machine-learning method. The foot-motion signals were collected from 50 participants with different FMTPAs. The true FMTPAs were assessed from digital photography. Correlation-based feature-selection processes (significance level {p} < 0.05 ) were used to search for the predictors from the foot-motion signals. Leave-one-subject-out cross-validation, root mean squared error, and intra-class coefficients were used for FMTPA-estimation model evaluation. Results: Eleven FMTPA-impacted gait-phase clusters, which were used to construct effective foot-motion predictors, were observed in all gait-cycle periods except terminal swing. The range of the foot motion in the sagittal and coronal planes significantly correlated with the FMTPA ( {p} < 0.05 ). Linear regression could be the best method for constructing an FMTPA estimation model with a root mean squared error and intra-class correlation coefficient of 4.2 degrees and 0.789, respectively. Conclusion: The results indicate the reliability of our FMTPA estimation model constructed from foot-motion signals and the possibility to providing foot-healthcare applications by using an IMS.