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In wheelchair sports, there is an increasing need to monitor mechanical power in the field. When rolling resistance is known, inertial measurement units (IMUs) can be used to determine mechanical power. However, upper body (i.e., trunk) motion affects the mass distribution between the small front and large rear wheels, thus affecting rolling resistance. Therefore, drag tests – which are commonly used to estimate rolling resistance – may not be valid. The aim of this study was to investigate the influence of trunk motion on mechanical power estimates in hand-rim wheelchair propulsion by comparing instantaneous resistance-based power loss with drag test-based power loss. Experiments were performed with no, moderate and full trunk motion during wheelchair propulsion. During these experiments, power loss was determined based on 1) the instantaneous rolling resistance and 2) based on the rolling resistance determined from drag tests (thus neglecting the effects of trunk motion). Results showed that power loss values of the two methods were similar when no trunk motion was present (mean difference [MD] of 0.6 ± 1.6 %). However, drag test-based power loss was underestimated up to −3.3 ± 2.3 % MD when the extent of trunk motion increased (r = 0.85). To conclude, during wheelchair propulsion with active trunk motion, neglecting the effects of trunk motion leads to an underestimated mechanical power of 1 to 6 % when it is estimated with drag test values. Depending on the required accuracy and the amount of trunk motion in the target group, the influence of trunk motion on power estimates should be corrected for., Biomechanical Engineering, Biomechatronics & Human-Machine Control

Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation., Biomechatronics & Human-Machine Control

An important performance determinant in wheelchair sports is the power exchanged between the athlete-wheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9–1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations., Biomechatronics & Human-Machine Control

Accurate knowledge of wheelchair kinematics during a match could be a significant factor in performance improvement in wheelchair basketball. To date, most systems for measuring wheelchair kinematics are not suitable for match applications or lack detail in key kinematic outcomes. This study describes the construction of wheel skid correction algorithms when using a three inertial measurement unit (IMUs) configuration for estimating wheelchair kinematics. The reliability of the skid corrected outcomes was assessed in wheelchair basketball match-like conditions. Twenty participants performed a series of tests reflecting different motion aspects of wheelchair basketball. IMU based estimations were compared to the outcomes of a 24-camera optical motion analysis system serving as gold standard. Once the skid correction algorithms were applied, estimation errors were reduced up to 4% of their original magnitude. Calculated Root Mean Square Errors (RMSE) showed good estimates for frame displacement (RMSE? 0.05m) and speed (RMSE? 0.1m/s) except for three truly vigorous tests. Estimates of horizontal frame rotation (RMSE<3°) and rotational speed (RMSE<7°/s) were very accurate in all conditions. Differences in calculated instantaneous rotation centers (IRC) were small, but somewhat larger in tests performed at high speed (RMSE up to 0.19m). Average test outcomes for linear speed (ICCs > 0.90), rotational speed (ICC>0.99) and IRC (ICC> 0.90) showed high correlations between IMU data and gold standard. Results indicate that wheel skid correction is a prerequisite to reliably measure wheelchair kinematics in sports conditions. Once applied, this method using cheap and affordable sensors, might enable prospective research in wheelchair basketball match conditions and contribute to individual support of athletes in everyday sports practice., Biomechanical Engineering, Mechanical, Maritime and Materials Engineering

Accurate knowledge of wheelchair kinematics during a match could be a significant factor in performance improvement in wheelchair basketball. To date, most systems for measuring wheelchair kinematics are not suitable for match applications or lack detail in key kinematic outcomes. This study describes the construction of wheel skid correction algorithms when using a three inertial measurement unit (IMUs) configuration for estimating wheelchair kinematics. The reliability of the skid corrected outcomes was assessed in wheelchair basketball match-like conditions. Twenty participants performed a series of tests reflecting different motion aspects of wheelchair basketball. IMU based estimations were compared to the outcomes of a 24-camera optical motion analysis system serving as gold standard. Once the skid correction algorithms were applied, estimation errors were reduced up to 4% of their original magnitude. Calculated Root Mean Square Errors (RMSE) showed good estimates for frame displacement (RMSE? 0.05m) and speed (RMSE? 0.1m/s) except for three truly vigorous tests. Estimates of horizontal frame rotation (RMSE<3°) and rotational speed (RMSE<7°/s) were very accurate in all conditions. Differences in calculated instantaneous rotation centers (IRC) were small, but somewhat larger in tests performed at high speed (RMSE up to 0.19m). Average test outcomes for linear speed (ICCs > 0.90), rotational speed (ICC>0.99) and IRC (ICC> 0.90) showed high correlations between IMU data and gold standard. Results indicate that wheel skid correction is a prerequisite to reliably measure wheelchair kinematics in sports conditions. Once applied, this method using cheap and affordable sensors, might enable prospective research in wheelchair basketball match conditions and contribute to individual support of athletes in everyday sports practice., Biomechanical Engineering, Mechanical, Maritime and Materials Engineering

Finger dexterity tests are generally used to assess performance decrease due to gloves, cold and pathology. It is generally assumed that the O’Connor and Purdue Pegboard test yield similar results. In this experiment we compared these two tests for dry conditions without gloves, and for dry and wet conditions with two types of Nytril gloves. In line with previous observations, wearing gloves caused a decrease in performance of about 12% for the O’Connor test and 9% for the Purdue test. Wetting the gloves prior to the test had no effect on the Purdue score. However, wetting the gloves increased the O’Connor performance significantly by 11%. The results show that the O’Connor and Purdue tests do not yield similar results and should be used selectively for specific tasks.

To evaluate the propulsive forces in front crawl arm swimming, derived from a three-dimensional kinematic analysis, these values were compared with mean drag forces. The propulsive forces during front crawl swimming using the arms only were calculated using three-dimensional kinematic analysis combined with lift and drag coefficients obtained in fluid laboratories. Since, for any constant swimming speed, the mean propulsive force should be equal to the mean drag force acting on the body of the swimmer, mean values of the calculated propulsive forces were compared with the mean drag forces obtained from measurements on a Measuring Active Drag (MAD) system. The two methods yielded comparable results, the mean difference between them being only 5% (2 N). We conclude that propulsive forces obtained from three-dimensional kinematic analysis provide realistic values. The calculation of the propulsive force appears to be rather sensitive to the point on the hand at which the velocity is estimated and less sensitive to the orientation of the hand.

To evaluate the propulsive forces in front crawl arm swimming, derived from a three-dimensional kinematic analysis, these values were compared with mean drag forces. The propulsive forces during front crawl swimming using the arms only were calculated using three-dimensional kinematic analysis combined with lift and drag coefficients obtained in fluid laboratories. Since, for any constant swimming speed, the mean propulsive force should be equal to the mean drag force acting on the body of the swimmer, mean values of the calculated propulsive forces were compared with the mean drag forces obtained from measurements on a Measuring Active Drag (MAD) system. The two methods yielded comparable results, the mean difference between them being only 5% (2 N). We conclude that propulsive forces obtained from three-dimensional kinematic analysis provide realistic values. The calculation of the propulsive force appears to be rather sensitive to the point on the hand at which the velocity is estimated and less sensitive to the orientation of the hand.