Objective: The efficacy of arch orthoses in posture adjustment and joint coordination improvement during steady-state gait is well documented; however, the biomechanical changes of gait sub-tasks caused by arch support (AS), especially during gait termination, are poorly understood. Hence, this study aimed to investigate how the acute arch-supporting intervention affects foot–ankle coordination and coordination variability (CV) in individuals with flatfoot during unplanned gait termination (UGT). Methods: Twenty-five male patients with flatfoot were selected as subjects participated in this AS manipulation study. A motion capture system was used for the collection of the metatarsophalangeal joint (MPJ) and ankle kinematics during UGT. MPJ-Ankle coordination and CV were quantified using an optimized vector coding technique during the three sub-phases of UGT. A paired-sample t-test from the one-dimensional statistical parametric mapping of one-dimensional was applied to examine the data significance. Results: Significant differences for the joint kinematics between non-arch-support (NAS) and AS were exhibited only in the MPJ transverse plane during the middle and later periods of UGT (p = 0.04–0.026). Frontal plane MPJ-ankle coordination under AS during stimulus delay significantly decreased from 177.16 ± 27.41° to 157.75 ± 32.54° compared with under NAS (p = 0.026); however, the coordination pattern had not changed. Moreover, no significant difference was found in the coupling angle variability between NAS and AS in three planes during sub-phases of UGT (all p > 0.5). Conclusions: The detailed intrinsic characteristic of AS induced acute changes in lower extremity segment coordination in patients with mild flatfoot has been recorded. This dataset on foot-ankle coordination characteristics during UGT is essential for explaining foot function and injury prediction concerning AS manipulation. Further studies are expected to reflect lower limb inter-joint coordination during gait termination through the long-term effects of AS orthoses.

The treadmill is widely used in running fatigue experiments, and the variation of plantar mechanical parameters caused by fatigue and gender, as well as the prediction of fatigue curves by a machine learning algorithm, play an important role in providing different training programs. This experiment aimed to compare changes in peak pressure (PP), peak force (PF), plantar impulse (PI), and gender differences of novice runners after they were fatigued by running. A support vector machine (SVM) was used to predict the fatigue curve according to the changes in PP, PF, and PI before and after fatigue. 15 healthy males and 15 healthy females completed two runs at a speed of 3.3 m/s ± 5% on a footscan pressure plate before and after fatigue. After fatigue, PP, PF, and PI decreased at hallux (T1) and second-fifth toes (T2–5), while heel medial (HM) and heel lateral (HL) increased. In addition, PP and PI also increased at the first metatarsal (M1). PP, PF, and PI at T1 and T2–5 were significantly higher in females than in males, and metatarsal 3–5 (M3–5) were significantly lower in females than in males. The SVM classification algorithm results showed the accuracy was above average level using the T1 PP/HL PF (train accuracy: 65%; test accuracy: 75%), T1 PF/HL PF (train accuracy: 67.5%; test accuracy: 65%), and HL PF/T1 PI (train accuracy: 67.5%; test accuracy: 70%). These values could provide information about running and gender-related injuries, such as metatarsal stress fractures and hallux valgus. Application of the SVM to the identification of plantar mechanical features before and after fatigue. The features of the plantar zones after fatigue can be identified and the learned algorithm of plantar zone combinations with above-average accuracy (T1 PP/HL PF, T1 PF/HL PF, and HL PF/T1 PI) can be used to predict running fatigue and supervise training. It provided an important idea for the detection of fatigue after running.