New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

Abstract

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a ‘typical’ or ‘stable’ gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2–4 runs were needed to define a stable running pattern for univariate, and 4–5 days were necessary for multivariate analysis across all inclination conditions. Pearson’s correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63–0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual’s stable gait pattern.

Continuous Analysis of Marathon Running Using Inertial Sensors: Hitting Two Walls?

“…Before computing the statistics over the different parts of the race, we targeted only level running by excluding walking and inclined period based on the following criteria: (1) the time of flight greater than 0 seconds (i. e., the subject is running) and ( 2) the slope must be lower than 3 % (▶ Fig. 1), as slopes > 3 % have been considered as gradient [29]. We then computed the mean and standard deviation for each temporal, spatial, and stiffness parameters over bouts of 5 kilometers long and investigated how these features changed throughout the race.…”

Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers

“…An emerging trend, quantifying sport actions with body-worn inertial measurements units (IMU), enables the assessment of athletes in ecologic conditions [17,18]. The metrological issues related to the use of wearable sensors for sport performance assessments have been the focal point of different research works: even though magneto-inertial technology allows monitoring the performance of athletes of all levels, especially when complemented with a sensor fusion network, there is a need for further research on the ease of use and error compensation to provide coaches and practitioners with informative and concise metrics [19,20,21,22]. The use of inertial units also raises technical issues to extract meaningful data from a broad class of signals (acceleration, angular velocity, magnetic field orientation, etc.)…”

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

“…To our point, starting from 2015, some studies have followed athletes for uncontrolled training runs or races [188,[200][201][202]206,207,[210][211][212][213]220,221,[225][226][227]230]. A myriad of external factors, such as weather, traffic, and surface conditions, could influence how someone runs and therefore, it is crucial to capture running patterns in the same settings that runners actually run.…”