Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

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.

Validity of gait parameters of healthy young adults using a motion-sensor-based gait analysis system (ORPHE ANALYTICS) during walking and running

“…As the treadmill speed increased, the ORPHE ANALYTICS-derived stride speed and length tended to be smaller than those calculated using optical motion capture data (Figures 2b, 2d, 3b, 3d). These results are consistent with those reported in previous studies that analysed running using motion sensors[12,25]. Falbriard et al [25] noted certain limitations in estimating stride speed through the simple integration of accelerations during running.…”

“…These results are consistent with those reported in previous studies that analysed running using motion sensors[12,25]. Falbriard et al [25] noted certain limitations in estimating stride speed through the simple integration of accelerations during running. They attempted a correction using a linear function for the stride speed estimated by integrating 500-Hz sampling frequency accelerations.…”

Limitations of Foot-Worn Sensors for Assessing Running Power

“…The work by Austin et al looked at the relation between power and economy as measured by a Stryd sensor, without manually altering economy [ 30 ]. (3) In a third direction of application for foot-worn sensors, Falbriard et al investigated the feasibility of accelerometers towards determining the ground speed of the athlete, again resulting in a strong relation (accuracy = 0.00 ± 0.01 m·s , precision = 0.09 ± 0.06 m·s ) [ 7 ].…”

“…These devices are designed to be independent of factors such as slope, wind resistance, or fatigue. There has been significant prior research related to the use of foot-worn accelerometers for different purposes: they have been used for and are known to correlate with ground speed [ 7 ], running economy [ 8 ], and running power [ 6 , 9 ].…”

Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions

“…To substitute these time intensive and costly systems, the integration of IMUs for diagnostics in gait [15][16][17], runs [18][19][20], or sprinting [13,[21][22][23][24][25] received much attention in the last decade. Various studies introduced new or adapted sprint performance metrics based on data of IMUs.…”