Daily wheelchair ambulation is seen as a risk factor for shoulder problems, which are prevalent in manual wheelchair users. To examine the long-term effect of shoulder load from daily wheelchair ambulation on shoulder problems, quantification is required in real-life settings. In this study, we describe and validate a comprehensive and unobtrusive methodology to derive clinically relevant wheelchair mobility metrics (WCMMs) from inertial measurement systems (IMUs) placed on the wheelchair frame and wheel in real-life settings. The set of WCMMs includes distance covered by the wheelchair, linear velocity of the wheelchair, number and duration of pushes, number and magnitude of turns and inclination of the wheelchair when on a slope. Data are collected from ten able-bodied participants, trained in wheelchair-related activities, who followed a 40 min course over the campus. The IMU-derived WCMMs are validated against accepted reference methods such as Smartwheel and video analysis. Intraclass correlation (ICC) is applied to test the reliability of the IMU method. IMU-derived push duration appeared to be less comparable with Smartwheel estimates, as it measures the effect of all energy applied to the wheelchair (including thorax and upper extremity movements), whereas the Smartwheel only measures forces and torques applied by the hand at the rim. All other WCMMs can be reliably estimated from real-life IMU data, with small errors and high ICCs, which opens the way to further examine real-life behavior in wheelchair ambulation with respect to shoulder loading. Moreover, WCMMs can be applied to other applications, including health tracking for individual interest or in therapy settings.

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.

In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1°/s for wheelchair angular velocity when compared with the reference system. The two-IMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0°/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available.

More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and the optimal combination differs between sports and depends on the intended aim, determining the best setup for a given sport can be challenging. This review aims to provide an overview and discussion of the present methods to estimate in-field mechanical power in different cyclical sports. Overall, in-field mechanical power estimation can be complex, such that methods are often simplified to improve feasibility. For example, for some sports, power meters exist that use the main propulsive force for mechanical power estimation. Another non-invasive method usable for in-field mechanical power estimation is the use of inertial measurement units (IMUs). These wearable sensors can either be used as stand-alone approach or in combination with force sensors. However, every method has consequences for interpretation of power values. Based on the findings of this review, recommendations for mechanical power measurement and interpretation in kayaking, rowing, wheelchair propulsion, speed skating, and cross-country skiing are done.

The dive start is an important component of competitive swimming, especially at shorter race distances. Previous research has suggested that start performance depends on kinematic variables pertaining to the swimmer at water entry, notably the distance from the block, the horizontal velocity of the centre of mass and the angle between body and water surface. However, the combined and relative contributions of these variables to start performance remain to be determined. The aim of the present study was therefore to develop a model to predict start performance (time from take-off to reaching the 15-m line) from a set of kinematic variables that collectively define the swimmer’s entry state. To obtain an appropriate database for this purpose, fifteen well-trained, (sub-)elite swimmers performed dive starts under different instructions intended to induce substantial variation in entry state. Kinematic data were extracted from video recordings of these starts, optimised and analysed statistically. A mixed effects analysis of the relation between entry state and start performance was conducted, which revealed a significant and robust dependence of start performance on entry state (χ2(3) = 88, p < .001), explaining 86.1% of the variance. Start time was reduced by 0.6 s (p < .001) when the horizontal displacement at water entry was 1 m further, by 0.3 s (p < .001) when the horizontal velocity of the centre of mass was 1 m/s higher, and by 0.5 s (p < .01) when the entry angle was 1 radian flatter. The robustness of the analysis was confirmed by a similar mixed effects analysis of the relation between entry state and time to the 5-m line. In conclusion, dive start performance can be predicted to a considerable extent from the swimmer’s state at water entry. The implications of those findings for studying and improving block phase kinetics are discussed.

Objective: This cross-sectional study examined associations between wheelchair sprint and anaerobic power (measured in the lab) and wheelchair mobility performance (measured in the field) among two groups of wheelchair tennis players. Additionally, construct validity was assessed for both lab and field tests. Design: Nine amateur and nine elite wheelchair tennis players performed a Sprint and Wingate test on a wheelchair ergometer in the lab and a Sprint, Illinois, and Spider test in the field, with inertial measurement units on their wheelchairs. Associations were assessed using regression analyses, and construct validity was assessed with an independent t test (elite vs. amateur). Results: The strongest associations were observed between lab outcomes and field sprint power (R² > 90%), followed by peak linear velocity and test duration (R² = 77%–85%), while peak rotational velocity showed the lowest associations with lab outcomes (R² = 69%–80%). The elite group outperformed the amateur group on all test outcomes. Conclusions: Despite differences in lab- and field-testing methodologies (e.g., trunk influence, linear/rotational components), the strong associations indicate overlap in measured constructs. Field testing offers valuable insight into practical performance, whereas lab testing enables in-depth biomechanical and physiological analyses. All tests effectively discriminate between elite and amateur wheelchair tennis players.