Wearable inertial sensors (WIS) facilitate the preservation of the athlete-environment relationship by allowing measurement outside the laboratory. WIS systems should be validated for team sports movements before they are used in sports performance and injury prevention research. The aim of the present study was to investigate the concurrent validity of a wearable inertial sensor system in quantifying joint kinematics during team sport movements. Ten recreationally active participants performed change-of-direction (single-leg deceleration and sidestep cut) and jump-landing (single-leg hop, single-leg crossover hop, and double-leg vertical jump) tasks while motion was recorded by nine inertial sensors (Noraxon MyoMotion, Noraxon USA Inc.) and eight motion capture cameras (Vicon Motion Systems Ltd). Validity of lower-extremity joint kinematics was assessed using measures of agreement (cross-correlation: XCORR) and error (root mean square deviation; and amplitude difference). Excellent agreement (XCORR >0.88) was found for sagittal plane kinematics in all joints and tasks. Highly variable agreement was found for frontal and transverse plane kinematics at the hip and ankle. Errors were relatively high in all planes. In conclusion, the WIS system provides valid estimates of sagittal plane joint kinematics in team sport movements. However, researchers should correct for offsets when comparing absolute joint angles between systems.
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The aim of this explorative study was to determine the key inertial measurement unit-based wheelchair mobility performance components during a wheelchair tennis match. A total of 64 wheelchair tennis matches were played by 15 wheelchair tennis players (6 women, 5 men, 4 juniors). All individual tennis wheelchairs were instrumented with inertial measurement units, two on the axes of the wheels and one on the frame. A total of 48 potentially relevant wheelchair tennis outcome variables were initially extracted from the sensor signals, based on previous wheelchair sports research and the input of wheelchair tennis experts (coaches, embedded scientists). A principal component analysis was used to reduce this set of variables to the most relevant outcomes for wheelchair tennis mobility. Results showed that wheelchair mobility performance in wheelchair tennis can be described by six components: rotations to racket side in (1) curves and (2) turns; (3) linear accelerations; (4) rotations to non-racket side in (4) turns and (5) curves; and finally, (6) linear velocities. One or two outcome variables per component were selected to allow an easier interpretation of results. These key outcome variables can be used to adequately describe the wheelchair mobility performance aspect of wheelchair tennis during a wheelchair tennis match and can be monitored during training.
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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.
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An important performance determinant in wheelchair sports is the power exchanged between the athletewheelchair 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.
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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.
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Quantifying measures of physical loading has been an essential part of performance monitoring within elite able-bodied sport, facilitated through advancing innovative technology. In wheelchair court sports (WCS) the inter-individual variability of physical impairments in the athletes increases the necessity for accurate load and performance measurements, while at the same time standard load monitoring methods (e.g. heart-rate) often fail in this group and dedicated WCS performance measurement methods are scarce. The objective of this review was to provide practitioners and researchers with an overview and recommendations to underpin the selection of suitable technologies for a variety of load and performance monitoring purposes specific to WCS. This review explored the different technologies that have been used for load and performance monitoring in WCS. During structured field testing, magnetic switch based devices, optical encoders and laser systems have all been used to monitor linear aspects of performance. However, movement in WCS is multidirectional, hence accelerations, decelerations and rotational performance and their impact on physiological responses and determination of skill level, is also of interest. Subsequently both for structured field testing as well as match-play and training, inertial measurement units mounted on wheels and frame have emerged as an accurate and practical option for quantifying linear and non-linear movements. In conclusion, each method has its place in load and performance measurement, yet inertial sensors seem most versatile and accurate. However, to add context to load and performance metrics, position-based acquisition devices such as automated image-based processing or local positioning systems are required.
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Athlete impairment level is an important factor in wheelchair mobility performance (WMP) in sports. Classification systems, aimed to compensate impairment level effects on performance, vary between sports. Improved understanding of resemblances and differences in WMP between sports could aid in optimizing the classification methodology. Furthermore, increased performance insight could be applied in training and wheelchair optimization. The wearable sensor-based wheelchair mobility performance monitor (WMPM) was used to measure WMP of wheelchair basketball, rugby and tennis athletes of (inter-)national level during match-play. As hypothesized, wheelchair basketball athletes show the highest average WMP levels and wheelchair rugby the lowest, whereas wheelchair tennis athletes range in between for most outcomes. Based on WMP profiles, wheelchair basketball requires the highest performance intensity, whereas in wheelchair tennis, maneuverability is the key performance factor. In wheelchair rugby, WMP levels show the highest variation comparable to the high variation in athletes’ impairment levels. These insights could be used to direct classification and training guidelines, with more emphasis on intensity for wheelchair basketball, focus on maneuverability for wheelchair tennis and impairment-level based training programs for wheelchair rugby. Wearable technology use seems a prerequisite for further development of wheelchair sports, on the sports level (classification) and on individual level (training and wheelchair configuration).
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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.
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Within this study the aim is to measure running workload and relevant running technique key points on varying cadence in recreational runners using a custom build sensor system ‘Nodes’. Seven participants ran on a treadmill at a self-chosen comfortable speed. Cadence was randomly guided by a metronome using 92%, 96%, 100%, 104%, and 108% of the preferred cadence in 2-min trials. Workload was measured by collecting the heart rate and the rating of perceived exertion (RPE 1 to 10) scores. Heart rate data shows that the 100% cadence trial was most economical with a relative heart rate of 99.2%. The 108% cadence trial had the lowest relative RPE score with 96.2%. The sample rate of the Nodes system during this experiment was too low to analyze the key points. Three requirements are proposed for the further engineering of a wearable running system, (i) sampling frequency of minimal 50 Hz, (ii) step-by-step analysis, and (iii) collecting workload in the heart rate and RPE.
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Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion systemcan give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.
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