Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use

Abstract

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.

How Was Studied the Effect of Manual Wheelchair Configuration on Propulsion Biomechanics: A Systematic Review on Methodologies

“…Along with the various experimental environments, a wide variety of measurement systems were used, from optoelectronic motion capture systems to IMUs, EMGs, pressure sheets, video cameras, force plates and instrumented wheels; each coming with its own pros and cons. For instance, IMUs allow to overcome the spatial restriction imposed by optoelectronic motion capture systems, enabling field measurements, but are less accurate to assess body orientation ( 81 ). Ideally, beyond their level of accuracy, measurement systems would not noticeably impact the subject's propulsion and the MWC characteristics.…”

Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling—A Review

“…Moreover, in some cases, measuring forces might be redundant with the use of IMUs. For instance, in wheelchair propulsion, it might be possible to estimate rolling resistance by placing an IMU on the wheelchair and one on the trunk [ 30 ]. Force on the rear and caster wheels could be modelled as a function of trunk angle and in combination with coast down tests for rolling resistance coefficients, rolling resistance could be estimated.…”

Estimating ski orientation using IMUs in alpine skiing

“…A recent study demonstrated the estimation of EAs with the application of inertial measurement units (IMU) at the back of the ski boots (Snyder et al, 2020). IMUs have many advantages over motion capture systems, as they are small, wearable, affordable and can easily be used outside the laboratory (van Dijk et al, 2021). While IMUs can be used to calculate the global orientation of objects they are attached to, the reference to the snow surface is more relevant to understand ski-snow interaction (e.g.…”