Mason Rachel, Celik Yunus, Barry Gill, Godfrey Alan, Stuart Samuel
Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Sensors (Basel). 2024 Dec 24;25(1):30. doi: 10.3390/s25010030.
The analysis of running gait has conventionally taken place within an expensive and restricted laboratory space, with wearable technology offering a practical, cost-effective, and unobtrusive way to examine running gait in more natural environments. This pilot study presents a wearable inertial measurement unit (IMU) setup for the continuous analysis of running gait during an outdoor parkrun (i.e., 5 km). The study aimed to (1) provide analytical validation of running gait measures compared to time- and age-graded performance and (2) explore performance validation. Ten healthy adults (7 females, 3 males, mean age 37.2 ± 11.7 years) participated. The participants wore Axivity AX6 IMUs on the talus joint of each foot, recording tri-axial accelerometer and gyroscope data at 200 Hz. Temporal gait characteristics-gait cycle, ground contact time, swing time, and duty factor-were extracted using zero-crossing algorithms. The data were analyzed for correlations between the running performance, foot strike type, and fatigue-induced changes in temporal gait characteristics. Strong correlations were found between the performance time and both the gait cycle and ground contact time, with weak correlations for foot strike types. The analysis of asymmetry and fatigue highlighted modest changes in gait as fatigue increased, but no significant gender differences were found. This setup demonstrates potential for in-field gait analysis for running, providing insights for performance and injury prevention strategies.
传统上，跑步步态分析是在昂贵且受限的实验室空间内进行的，而可穿戴技术提供了一种实用、经济高效且不引人注目的方式，可在更自然的环境中检查跑步步态。这项初步研究提出了一种可穿戴惯性测量单元（IMU）设置，用于在户外公园跑（即5公里）期间对跑步步态进行连续分析。该研究旨在：（1）与时间和年龄分级表现相比，提供跑步步态测量的分析验证；（2）探索性能验证。十名健康成年人（7名女性，3名男性，平均年龄37.2±11.7岁）参与了研究。参与者在每只脚的距骨关节上佩戴Axivity AX6 IMU，以200赫兹的频率记录三轴加速度计和陀螺仪数据。使用过零算法提取时间步态特征——步态周期、地面接触时间、摆动时间和负荷率。分析了跑步表现、脚着地类型和疲劳引起的时间步态特征变化之间的相关性。发现表现时间与步态周期和地面接触时间之间存在强相关性，而与脚着地类型之间的相关性较弱。不对称性和疲劳分析突出了随着疲劳增加步态的适度变化，但未发现显著的性别差异。这种设置展示了跑步现场步态分析的潜力，为性能和损伤预防策略提供了见解。
