It is essential for any highly trained cyclist to optimize his pedalling movement in order to maximize the performance and minimize the risk of injuries. Current techniques rely on bicycle fitting and off-line laboratory measurements. These techniques do not allow the assessment of the kinematics of the cyclist during training and competition, when fatigue may alter the ability of the cyclist to apply forces to the pedals and thus induce maladaptive joint loading. We propose a radically different approach that focuses on determining the actual status of the cyclist’s lower limb segments in real-time and real-life conditions. Our solution is based on body area wireless motion sensor nodes that can collaboratively process the sensory information and provide the cyclists with immediate feedback about their pedalling movement. In this paper, we present a thorough study of the accuracy of our system with respect to the gold standard motion capture system. We measure the knee and ankle angles, which influence the performance as well as the risk of overuse injuries during cycling. The results obtained from a series of experiments with nine subjects show that the motion sensors are within 2.2° to 6.4° from the reference given by the motion capture system, with a correlation coefficient above 0.9. The wireless characteristics of our system, the energy expenditure, possible improvements and usability aspects are further analysed and discussed.

Foot strike mode and footwear features are known to affect ankle joint kinematics and loading patterns, but how those factors are related to the ankle dynamic properties is less clear. In our study, two distinct samples of experienced long-distance runners: habitual rearfoot strikers (n = 10) and habitual forefoot strikers (n = 10), were analysed while running at constant speed on an instrumented treadmill in three footwear conditions. The joint dynamic stiffness was analysed for three subphases of the moment–angle plot: early rising, late rising and descending. Habitual rearfoot strikers displayed a statistically (p < 0.05) higher ankle dynamic stiffness in all combinations of shoes and subphases, except in early stance in supportive shoes. In minimal-supportive shoes, both groups had the lowest dynamic stiffness values for early and late rising (initial contact through mid-stance), whilst the highest stiffness values were at late rising in minimal shoes for both rearfoot and forefoot strikers (0.21 ± 0.04, 0.24 ± 0.06 (Nm/kg/°∙100), respectively). In conclusion, habitual forefoot strikers may have access to a wider physiological range of the muscle torque and joint angle. This increased potential may allow forefoot strikers to adapt to different footwear by regulating ankle dynamic stiffness depending upon the motor task.

Toe flexor strength is a pivotal biomechanical contributor for effecting balance and gait. However, there are limited reports that evaluate measurement accuracy and repeatability of this important attribute. Dynamometers are designed to measure force which can be used to derive joint torque if the perpendicular distance to the joint axis is known. However, an accurate and reliable measurement method to assess the ability of the toe flexor muscles to produce torque, is lacking. Here we describe a new device and method, designed to quantify the toe flexor torque developed at the metatarsal phalangeal joint. We evaluate measurement bias and the ability of the instrument to consistently measure what it is supposed to measure (Interclass Correlation Coefficient). Results suggest that our device is an accurate tool for measuring angle and torque with a small (0.10° and 0.07 Nm, respectively) bias. When tested for reliability and repeatability in measuring toe flexor torque (n = 10), our device showed high interclass correlation (ICC = 0.99), small bias (-1.13 Nm) and small repeatability coefficient (CR = 3.9). We suggest mean bias and CR to be reported for future measurement methods and our protocol used as standard approach to measure maximal toe flexor torque.

(2017). Foot strike classification: a comparison of methodologies. Footwear Science: Vol. 9, Proceedings of the Thirteenth Footwear Biomechanics Symposium (Gold Coast, Australia 2017), pp. S129-S130.