Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} < 0.05$ </tex-math></inline-formula> ) 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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} < 0.05$ </tex-math></inline-formula> ). 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.
Highlights
WITH the development of Internet-of-Things technologies, wearable smart sensors, which can finish all the data processing on an edge device, have been applied to various healthcare applications by automatically recording biomedical signals, such as pulse and sweat, in daily living without intentional manipulation [1], [2]
The first metatarsophalangeal angle (FMTPA)-impacted %gait cycle (GC) were found in the linear motion of all directions (Ax, Ay, and Az) and in the rotational motion and sole-to-ground angle (SGA) in the sagittal (Gx and Ex) and coronal (Gy and Ey) planes
We investigated for the first time the correlation between the FMTPA and foot motion, especially inertial signals that only originate from single segment kinematics relative to the global
Summary
WITH the development of Internet-of-Things technologies, wearable smart sensors, which can finish all the data processing on an edge device, have been applied to various healthcare applications by automatically recording biomedical signals, such as pulse and sweat, in daily living without intentional manipulation [1], [2]. The relationship between walking and health has been receiving attention. There are mainly two types of wearable smart sensors believed to have.
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