Lower extremity fatigue has been associated with decline in postural stability, alteration of normal walking patterns and increased fall risk. Effects of lower extremity fatigue on amount of movement variability as assessed by linear variability such as standard deviation and root mean square is well known but there is lack of information about how fatigue influences nonlinear temporal structure of variability in healthy human gait. In this study ten subjects (5 males and 5 females) were asked to perform treadmill walking for three minutes with an Inertial Measurement Unit (IMU) sensor affixed at their trunk level, thereafter the participants conducted squatting exercises and fatigue was induced as per standard fatigue protocol. The participants were asked to walk again on treadmill at their preferred walking speed for three minutes. The signals derived from the inertial sensor were used to compute stride interval time series (SIT) and signal magnitude difference (SMD) time series signals. These SIT and SMD signals were analyzed for non-linear variability such as complexity (approximate entropy and multiscale entropy) and Detrended Fluctuation Analysis (DFA). It was found that that there was significantly higher complexity in SMD signals due to fatigue inducement (p=0.04). Similarly, it was also found that fatigue significantly decreased fractal properties of SMD signals (p=0.013). In conclusion, lower extremity localized muscle fatigue influences magnitude of kinematic variability and induced anti-persistence in the trunk kinematics. In future, more work is needed to understand how kinematic variability in angular velocities due to fatigue may affect fall risk in healthy adults.
Keywords: Lower extremity fatigue; complexity; inertial sensors.