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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Inertial measurement units (IMUs) allow for measurements of kinematic movements outside the laboratory, persevering the athlete-environment relationship. To use IMUs in a sport-specific setting, it is necessary to validate sport-specific movements. The aim of this study was to assess the concurrent validity of the Xsens IMU system by comparing it to the Vicon optoelectronic motion system for lower-limb joint angle measurements during jump-landing and change-of-direction tasks. Ten recreational athletes performed four tasks; single-leg hop and landing, running double-leg vertical jump landing, single-leg deceleration and push off, and sidestep cut, while kinematics were recorded by 17 IMUs (Xsens Technologies B.V.) and eight motion capture cameras (Vicon Motion Systems, Ltd). Validity of lower-body joint kinematics was assessed using measures of agreement (cross-correlation: XCORR) and error (root mean square deviation and amplitude difference). Excellent agreement was found in the sagittal plane for all joints and tasks (XCORR > 0.92). Highly variable agreement was found for knee and ankle in transverse and frontal plane. Relatively high error rates were found in all joints. In conclusion, this study shows that the Xsens IMU system provides highly comparable waveforms of sagittal lower-body joint kinematics in sport-specific movements. Caution is advised interpreting frontal and transverse plane kinematics as between-system agreement highly varied.
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How to create personas to improve designs for behaviour change strategies in the public domain? Three recent cases illustrate lessons learnt and challenges encountered during persona development in the public domain. Personas were helpful to gain insight into diversity within a target group, to create empathy for its members, and to have a shared understanding when communicating about them. The main challenges encountered were 1) capturing complex behaviour with personas, as the behaviours involved were variable over time, the (legislative) environment in motion, and the target groups diverse; 2) finding the right balance between intuitive vs. evidence-based decision-making, a process we coined “taking a responsible leap of faith”; and 3) transferring personas to third parties, as free sharing of insights and tools is common in the public domain. Validation plays an important role in personas’ transferability. We call for all involved researchers to share experiences with using the persona methodology in the public domain, in order to tackle the challenges, and to create a more standardised way of developing personas.
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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 twoIMU 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.
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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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Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
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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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Background Objective gait analysis that fully captures the multi-segmental foot movement of a clubfoot may help in early identification of a relapse clubfoot. Unfortunately, this type of objective measure is still lacking in a clinical setting and it is unknown how it relates to clinical assessment. Research question The aim of this study was to identify differences in total gait and foot deviations between clubfoot patients with and without a relapse clubfoot and to evaluate their relationship with clinical status. Methods In this study, Ponseti-treated idiopathic clubfoot patients were included and divided into clubfoot patients with and without a relapse. Objective gait analysis was done resulting in total gait and foot scores and clinical assessment was performed using the Clubfoot Assessment Protocol (CAP). Additionally, a new clubfoot specific foot score, the clubFoot Deviation Index (cFDI*), was calculated to better capture foot kinematics of clubfoot patients. Results Clubfoot patients with a relapse show lower total gait quality (GDI*) and lower clinical status defined by the CAP than clubfoot patients without a relapse. Abnormal cFDI* was found in relapse patients, reflected by differences in corresponding variable scores. Moderate relationships were found for the subdomains of the CAP and total gait and foot quality in all clubfoot patients. Significance A new total foot score was introduced in this study, which was more relevant for the clubfoot population. The use of this new foot score (cFDI*) besides the GDI*, is recommended to identify gait and foot motion deviations. Along with clinical assessment, this will give an overview of the overall status of the complex, multi-segmental aspects of a (relapsed) clubfoot. The relationships found in this study suggest that clinical assessment might be indicative of a deviation in total gait and foot pattern, therefore hinting towards personalised screening for better treatment decision making.
MULTIFILE
Background: Development of more effective interventions for nonspecific chronic low back pain (LBP), requires a robust theoretical framework regarding mechanisms underlying the persistence of LBP. Altered movement patterns, possibly driven by pain-related cognitions, are assumed to drive pain persistence, but cogent evidence is missing. Aim: To assess variability and stability of lumbar movement patterns, during repetitive seated reaching, in people with and without LBP, and to investigate whether these movement characteristics are associated with painrelated cognitions. Methods: 60 participants were recruited, matched by age and sex (30 back-healthy and 30 with LBP). Mean age was 32.1 years (SD13.4). Mean Oswestry Disability Index-score in LBP-group was 15.7 (SD12.7). Pain-related cognitions were assessed by the ‘Pain Catastrophizing Scale’ (PCS), ‘Pain Anxiety Symptoms Scale’ (PASS) and the task-specific ‘Expected Back Strain’ scale(EBS). Participants performed a seated repetitive reaching movement (45 times), at self-selected speed. Lumbar movement patterns were assessed by an optical motion capture system recording positions of cluster markers, located on the spinous processes of S1 and T8. Movement patterns were characterized by the spatial variability (meanSD) of the lumbar Euler angles: flexion-extension, lateralbending, axial-rotation, temporal variability (CyclSD) and local dynamic stability (LDE). Differences in movement patterns, between people with and without LBP and with high and low levels of pain-related cognitions, were assessed with factorial MANOVA. Results: We found no main effect of LBP on variability and stability, but there was a significant interaction effect of group and EBS. In the LBP-group, participants with high levels of EBS, showed increased MeanSDlateral-bending (p = 0.004, η2 = 0.14), indicating a large effect. MeanSDaxial-rotation approached significance (p = 0.06). Significance: In people with LBP, spatial variability was predicted by the task-specific EBS, but not by the general measures of pain-related cognitions. These results suggest that a high level of EBS is a driver of increased spatial variability, in participants with LBP.
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Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance. Twenty-five able-bodied participants performed hand-rim wheelchair propulsion on a large motor-driven treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict front wheel load from kinematic data. Based on two inertial sensors (attached to the trunk and wheelchair) and the machine learning model, front wheel load was predicted with a mean absolute error (MAE) of 3.8% (or 1.8 kg). Rolling resistance determined from the predicted load distribution (MAE: 0.9%, mean error (ME): 0.1%) was more accurate than drag test-based rolling resistance (MAE: 2.5%, ME: −1.3%).
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