Abstract
The overground speed is a key component of running analysis. Today, most speed estimation wearable systems are based on GNSS technology. However, these devices can suffer from sparse communication with the satellites and have a high-power consumption. In this study, we propose three different approaches to estimate the overground speed in running based on foot-worn inertial sensors and compare the results against a reference GNSS system. First, a method is proposed by direct strapdown integration of the foot acceleration. Second, a feature-based linear model and finally a personalized online-model based on the recursive least squares' method were devised. We also evaluated the performance differences between two sets of features; one automatically selected set (i.e., optimized) and a set of features based on the existing literature. The data set of this study was recorded in a real-world setting, with 33 healthy individuals running at low, preferred, and high speed. The direct estimation of the running speed achieved an inter-subject mean ± STD accuracy of 0.08 ± 0.1 m/s and a precision of 0.16 ± 0.04 m/s. In comparison, the best feature-based linear model achieved 0.00 ± 0.11 m/s accuracy and 0.11 ± 0.05 m/s precision, while the personalized model obtained a 0.00 ± 0.01 m/s accuracy and 0.09 ± 0.06 m/s precision. The results of this study suggest that (1) the direct estimation of the velocity of the foot are biased, and the error is affected by the overground velocity and the slope; (2) the main limitation of a general linear model is the relatively high inter-subject variance of the bias, which reflects the intrinsic differences in gait patterns among individuals; (3) this inter-subject variance can be nulled using a personalized model.
Highlights
The overground speed is the most useful metric in training and performance analysis of running
Two subjects were excluded from the data set; because of the poor quality of the Global Navigation Satellite System (GNSS) measurements or because of an improper fixation of the IMU on the shoe and high Signal to Noise Ratio (SNR) of the kinematic data
The feature selection process stopped at average Mean Square Error (MSE) of 0.0057 m/s (Figure 7), which corresponded to a 1.12% improvement compared to the previous step with 27 features
Summary
The overground speed is the most useful metric in training and performance analysis of running. Researchers have tried for decades to understand the physiological and biomechanical adjustments occurring at different ranges of running speeds (Williams and Cavanagh, 1987; Nummela et al, 2007; Moore, 2016; Thompson, 2017). Most of the existing studies were performed in a controlled environment (i.e., treadmill running inside a laboratory) where the runner has to adapt his gait to run at a constant speed. Change of environment, surface, slope, obstacles, and turns alter the gait and the running speed. Many studies have discussed the IMU-Based Running Speed Estimation biomechanical adaptations associated with running on a treadmill vs running overground (Van Hooren et al, 2019). While standard motion capture (i.e., stereophotogrammetry and force plate) offers accurate measurements in laboratories, the recent emergence of wearable systems is paving the shift toward studies carried overground and in real-world conditions (Benson et al, 2018)
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