Gait variability between younger and older adults: An equality of variance analysis.

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Abstract

[Indexed for MEDLINE] 11. Sensors (Basel). 2024 Dec 24;25(1):30. doi: 10.3390/s25010030. Instrumenting Parkrun: Usefulness and Validity of Inertial Sensors. Mason R(1), Celik Y(2), Barry G(1), Godfrey A(2), Stuart S(1)(3). Author information: (1)Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. (2)Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. (3)Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA. 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. DOI: 10.3390/s25010030 PMCID: PMC11723058