A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Abstract

Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estimation assumes it to have a first order exponential behavior. While this method remains appropriate for maximal sprints, the sprint velocity profile may not always show a first-order exponential behavior. Alternately, velocity profile has been estimated using inertial sensors, with a speed radar, or a smartphone application. Existing methods either relied on the exponential behavior or timing gates for drift removal, or estimated only the mean velocity. Thus, there is a need for a more flexible and appropriate approach, allowing for instantaneous velocity estimation during sprint tests. The proposed method aims to solve this problem using a sensor fusion approach, by combining the signals from wearable Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) sensors. We collected data from nine elite sprinters, equipped with a wearable GNSS-IMU sensor, who ran two trials each of 60 and 30/40 m sprints. We developed an algorithm using a gradient descent-based orientation filter, which simplified our model to a linear one-dimensional model, thus allowing us to use a simple Kalman filter (KF) for velocity estimation. We used two cascaded KFs, to segment the sprint data precisely, and to estimate the velocity and the sprint duration, respectively. We validated the estimated velocity and duration with speed radar and photocell data as reference. The median RMS error for the estimated velocity ranged from 6 to 8%, while that for the estimated sprint duration lied between 0.1 and −6.0%. The Bland–Altman plot showed close agreement between the estimated and the reference values of maximum velocity. Examination of fitting errors indicated a second order exponential behavior for the sprint velocity profile, unlike the first order behavior previously suggested in literature. The proposed sensor-fusion algorithm is valid to compute an accurate velocity profile with respect to the radar; it can compensate for and improve upon the accuracy of the individual IMU and GNSS velocities. This method thus enables the use of wearable sensors in the analysis of sprint test.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

“…The Fieldwiz and Physilog 5 wearable sensors were chosen because they have already been used successfully for continuous analysis of running in the field (Apte et al, 2020;Meyer et al, 2021). Fieldwiz was used with a sampling frequency of 200 Hz for the IMU, 250 Hz for the ECG, and 10 Hz for the GNSS receiver.…”

Biomechanical Response of the Lower Extremity to Running-Induced Acute Fatigue: A Systematic Review

“…However, foot-based IMUs and pressure insoles provide higher accuracy for the estimation of contact time and GRF (Falbriard et al, 2018). It is possible to estimate stride length as a combination of the running speed measured from a shank or an upper back sensor (Yang et al, 2011;Apte et al, 2020), and the cadence from a sacrum or foot sensor (Lee et al, 2010;Falbriard et al, 2018). While previous research has shown the measurement of sagittal hip angles to be possible for fast movements (Fasel et al, 2018), the accuracy of this measurement is susceptible to soft tissue artefacts.…”

Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review

“…IMU solutions use fusion sensing to estimate velocity [ 37 ]. Thus, despite their practical benefits, some issues related to accuracy and precision should be considered.…”