From biomechanical point of view, strike pattern plays an important role in preventing potential injury risk in running. Traditionally, strike pattern determination was conducted by using 3D motion analysis system with cameras. However, the procedure is costly and not convenient. With the rapid development of technology, sensors have been applied in sport science field lately. Therefore, this study was designed to determine the algorithm that can identify landing strategies with a wearable sensor. Six healthy male participants were recruited to perform heel and forefoot strike strategies at 7, 10, and 13 km/h speeds. The kinematic data were collected by Vicon 3D motion analysis system and 2 inertial measurement units (IMU) attached on the dorsal side of both shoes. The data of each foot strike were gathered for pitch angle and strike index analysis. Comparing the strike index from IMU with the pitch angle from Vicon system, our results showed that both signals exhibited highly correlated changes between different strike patterns in the sagittal plane (r=0.98). Based on the findings, the IMU sensors showed potential capabilities and could be extended beyond the context of sport science to other fields, including clinical applications.

Aging may cause various functional abilities gradually deteriorate. With changes in social forms, the trend of functional fitness decline will change accordingly. Therefore, this study endeavored to identify the trends in functional fitness decline by comparing the differences in the functional fitness of females in various age groups. Thirty six healthy females were divided into 3 age groups: young healthy females (20 to 30 y); middle-age (45 to 55 y); and older (65 to 75 y). Functional fitness test battery included flexibility, muscle strength/endurance, aerobic endurance, balance and agility. The performance in the elderly group was significantly worse (P < .05) in all the tests, whereas the muscle strength and endurance, as well as aerobic endurance for the middle-age group showed significantly lower than young groups (P < .05). The reduction in lower extremity muscle strength occurs in the middle-age group. We recommend that middle-age women be conscious of the reduction in their lower extremity muscle strength and conduct advanced preparations for future aging.

This study aimed to investigate differences in forces and impulses at five rider–bicycle contacts (bilateral grips, saddle, and bilateral pedals) across posture, cadence, and gear ratio. In a randomized, repeated-measures laboratory design, 10 male and 10 female recreational cyclists completed 18 combinations of three seated postures, three cadences, and two gear ratios on a stationary indoor bicycle sensing system (50 Hz). Three-way repeated-measures ANOVAs with Holm correction examined body-mass-normalized vertical peak force, cycle-averaged force, and impulse per cycle at p < 0.05. Posture affected all three outcomes at both grips (p < 0.01, ηp² = 0.360–0.552) but not the saddle or pedals (p = 0.821–1.000, ηp² = 0.027–0.157). Cadence affected grip and saddle impulse and all pedal outcomes (p < 0.01, ηp² = 0.544–0.908); higher cadence reduced impulse at all contacts and increased pedal-force magnitude. Heavy gear increased all pedal outcomes and reduced all grip and saddle outcomes compared with light gear (p < 0.001, ηp² = 0.593–0.981). Interactions were limited, and sensitivity analyses reproduced all main-effect conclusions. Posture effects were concentrated at the handlebar, cadence primarily modified force-time characteristics and pedal-force magnitude, and gear ratio redistributed vertical loading between the pedals and other contacts. These findings support a purpose-dependent framework for region-specific mechanical load management in comparable indoor cycling conditions.