R.M.A. van der Slikke
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3 records found
1
Towards Evidence-Based Classification in Wheelchair Sports
A Study on Trunk Kinematics and Mobility Performance
Objective: This study aimed to validate the use of Inertial Measurement Units (IMUs) for quantifying trunk motion in wheelchair sports and explore the relationship between trunk motion, wheelchair mobility performance, and classification level in elite wheelchair basketball athletes.
Methods: Fourteen participants (seven elite wheelchair athletes and seven non-WC users) completed eight standardized wheelchair mobility tests. Trunk motion was measured using IMUs and compared with a 3D motion capture (MOCAP) system. Trunk kinematics and wheelchair performance metrics were derived and analysed for correlation in the elite wheelchair group.
Results: IMUs, especially those mounted on the upper back, showed excellent agreement with 3D motion capture data (ICCs > 0.90; RMSE < 8%). Mean trunk angle correlated with wheelchair linear velocity during manoeuvring and sprinting tasks (r > 0.75), highlighting the role of dynamic trunk use in enhancing propulsion. Additionally, trunk kinematics, particularly range of motion, mean tilt angle, and forward lean time, showed strong correlations with classification level (r > 0.79), especially during the straight push test. Performance metrics demonstrated weaker correlation with classification (r < 0.47), suggesting that impairment may not directly translate into measurable differences in mobility performance in this sample.
Conclusion: Trunk rotation angles derived from IMUs provide a valid and practical tool for measuring trunk motion. Their integration could support more transparent and impairment-focussed classification frameworks. ...
Methods: Fourteen participants (seven elite wheelchair athletes and seven non-WC users) completed eight standardized wheelchair mobility tests. Trunk motion was measured using IMUs and compared with a 3D motion capture (MOCAP) system. Trunk kinematics and wheelchair performance metrics were derived and analysed for correlation in the elite wheelchair group.
Results: IMUs, especially those mounted on the upper back, showed excellent agreement with 3D motion capture data (ICCs > 0.90; RMSE < 8%). Mean trunk angle correlated with wheelchair linear velocity during manoeuvring and sprinting tasks (r > 0.75), highlighting the role of dynamic trunk use in enhancing propulsion. Additionally, trunk kinematics, particularly range of motion, mean tilt angle, and forward lean time, showed strong correlations with classification level (r > 0.79), especially during the straight push test. Performance metrics demonstrated weaker correlation with classification (r < 0.47), suggesting that impairment may not directly translate into measurable differences in mobility performance in this sample.
Conclusion: Trunk rotation angles derived from IMUs provide a valid and practical tool for measuring trunk motion. Their integration could support more transparent and impairment-focussed classification frameworks. ...
Objective: This study aimed to validate the use of Inertial Measurement Units (IMUs) for quantifying trunk motion in wheelchair sports and explore the relationship between trunk motion, wheelchair mobility performance, and classification level in elite wheelchair basketball athletes.
Methods: Fourteen participants (seven elite wheelchair athletes and seven non-WC users) completed eight standardized wheelchair mobility tests. Trunk motion was measured using IMUs and compared with a 3D motion capture (MOCAP) system. Trunk kinematics and wheelchair performance metrics were derived and analysed for correlation in the elite wheelchair group.
Results: IMUs, especially those mounted on the upper back, showed excellent agreement with 3D motion capture data (ICCs > 0.90; RMSE < 8%). Mean trunk angle correlated with wheelchair linear velocity during manoeuvring and sprinting tasks (r > 0.75), highlighting the role of dynamic trunk use in enhancing propulsion. Additionally, trunk kinematics, particularly range of motion, mean tilt angle, and forward lean time, showed strong correlations with classification level (r > 0.79), especially during the straight push test. Performance metrics demonstrated weaker correlation with classification (r < 0.47), suggesting that impairment may not directly translate into measurable differences in mobility performance in this sample.
Conclusion: Trunk rotation angles derived from IMUs provide a valid and practical tool for measuring trunk motion. Their integration could support more transparent and impairment-focussed classification frameworks.
Methods: Fourteen participants (seven elite wheelchair athletes and seven non-WC users) completed eight standardized wheelchair mobility tests. Trunk motion was measured using IMUs and compared with a 3D motion capture (MOCAP) system. Trunk kinematics and wheelchair performance metrics were derived and analysed for correlation in the elite wheelchair group.
Results: IMUs, especially those mounted on the upper back, showed excellent agreement with 3D motion capture data (ICCs > 0.90; RMSE < 8%). Mean trunk angle correlated with wheelchair linear velocity during manoeuvring and sprinting tasks (r > 0.75), highlighting the role of dynamic trunk use in enhancing propulsion. Additionally, trunk kinematics, particularly range of motion, mean tilt angle, and forward lean time, showed strong correlations with classification level (r > 0.79), especially during the straight push test. Performance metrics demonstrated weaker correlation with classification (r < 0.47), suggesting that impairment may not directly translate into measurable differences in mobility performance in this sample.
Conclusion: Trunk rotation angles derived from IMUs provide a valid and practical tool for measuring trunk motion. Their integration could support more transparent and impairment-focussed classification frameworks.
This thesis discusses the potential of the RHIDE system, a device designed to measure
in-field wheelchair handrim push characteristics. The research question is if the RHIDE
can accurately discriminate propulsion/recovery and determine contact/release angles. This
research question is answered by determining push time (τ ), contact angle (α), release angle
(β) and push time (∆) for each push. To validate the RHIDE, these biomechanics are
measured with two different systems, one being the RHIDE and the other being the MarkerLess and Machine Learning Vision System (MLVS). Validation is conducted using data
from an experiment comprising 12 wheelchair propulsion-related tests on an ergometer, with
variations in speed (S1 and S2) and resistance (R1 and R2). For every push, τ , α, β and
∆ are extracted with both systems and the reliability of the RHIDE is calculated using an
ICC(2,1) on absolute agreement of the biomechanics for all pushes. For τ , ICC values are
0.65 for S1R1, 0.88 for S1R2, 0.98 S2R1 and 0.96 for S2R2 therefore reliability is considered
moderate, good, excellent and excellent respectively. For α, ICC values are 0.67 for S1R1,
0.59 for S1R2, 0.56 S2R1 and 0.51 for S2R2 giving moderate reliability for all tests. The
β ICC values are 0.57 for S1R1, 0.34 for S1R2, 0.35 S2R1 and 0.26 for S2R2 giving poor
reliability for all tests except S1R1. At last, ∆ ICC values are 0.17 for S1R1, 0.52 for S1R2,
0.25 S2R1 and 0.14 for S2R2, giving poor reliability for all tests except S1R2. Overall,
the RHIDE is able to accurately discriminate propulsion/recovery phases. Touch/release
angles are not reliable between systems. There are two potential causes for this. Firstly,
the RHIDE could improve its accuracy by increasing sample rate, streamline data pathways
and the introduction of a calibration protocol. Secondly, both systems measure different
contact/release angles and are therefore not comparable in this matter.
Overall, RHIDE has the potential to occupy a unique position in wheelchair propulsion
analysis by enabling the measurement of daily activities. With RHIDE, it would be feasible
to (1) provide information about propulsion technique by supplying push/release angles and
push times and (2) offer insights into daily wheelchair use over extended periods. ...
in-field wheelchair handrim push characteristics. The research question is if the RHIDE
can accurately discriminate propulsion/recovery and determine contact/release angles. This
research question is answered by determining push time (τ ), contact angle (α), release angle
(β) and push time (∆) for each push. To validate the RHIDE, these biomechanics are
measured with two different systems, one being the RHIDE and the other being the MarkerLess and Machine Learning Vision System (MLVS). Validation is conducted using data
from an experiment comprising 12 wheelchair propulsion-related tests on an ergometer, with
variations in speed (S1 and S2) and resistance (R1 and R2). For every push, τ , α, β and
∆ are extracted with both systems and the reliability of the RHIDE is calculated using an
ICC(2,1) on absolute agreement of the biomechanics for all pushes. For τ , ICC values are
0.65 for S1R1, 0.88 for S1R2, 0.98 S2R1 and 0.96 for S2R2 therefore reliability is considered
moderate, good, excellent and excellent respectively. For α, ICC values are 0.67 for S1R1,
0.59 for S1R2, 0.56 S2R1 and 0.51 for S2R2 giving moderate reliability for all tests. The
β ICC values are 0.57 for S1R1, 0.34 for S1R2, 0.35 S2R1 and 0.26 for S2R2 giving poor
reliability for all tests except S1R1. At last, ∆ ICC values are 0.17 for S1R1, 0.52 for S1R2,
0.25 S2R1 and 0.14 for S2R2, giving poor reliability for all tests except S1R2. Overall,
the RHIDE is able to accurately discriminate propulsion/recovery phases. Touch/release
angles are not reliable between systems. There are two potential causes for this. Firstly,
the RHIDE could improve its accuracy by increasing sample rate, streamline data pathways
and the introduction of a calibration protocol. Secondly, both systems measure different
contact/release angles and are therefore not comparable in this matter.
Overall, RHIDE has the potential to occupy a unique position in wheelchair propulsion
analysis by enabling the measurement of daily activities. With RHIDE, it would be feasible
to (1) provide information about propulsion technique by supplying push/release angles and
push times and (2) offer insights into daily wheelchair use over extended periods. ...
This thesis discusses the potential of the RHIDE system, a device designed to measure
in-field wheelchair handrim push characteristics. The research question is if the RHIDE
can accurately discriminate propulsion/recovery and determine contact/release angles. This
research question is answered by determining push time (τ ), contact angle (α), release angle
(β) and push time (∆) for each push. To validate the RHIDE, these biomechanics are
measured with two different systems, one being the RHIDE and the other being the MarkerLess and Machine Learning Vision System (MLVS). Validation is conducted using data
from an experiment comprising 12 wheelchair propulsion-related tests on an ergometer, with
variations in speed (S1 and S2) and resistance (R1 and R2). For every push, τ , α, β and
∆ are extracted with both systems and the reliability of the RHIDE is calculated using an
ICC(2,1) on absolute agreement of the biomechanics for all pushes. For τ , ICC values are
0.65 for S1R1, 0.88 for S1R2, 0.98 S2R1 and 0.96 for S2R2 therefore reliability is considered
moderate, good, excellent and excellent respectively. For α, ICC values are 0.67 for S1R1,
0.59 for S1R2, 0.56 S2R1 and 0.51 for S2R2 giving moderate reliability for all tests. The
β ICC values are 0.57 for S1R1, 0.34 for S1R2, 0.35 S2R1 and 0.26 for S2R2 giving poor
reliability for all tests except S1R1. At last, ∆ ICC values are 0.17 for S1R1, 0.52 for S1R2,
0.25 S2R1 and 0.14 for S2R2, giving poor reliability for all tests except S1R2. Overall,
the RHIDE is able to accurately discriminate propulsion/recovery phases. Touch/release
angles are not reliable between systems. There are two potential causes for this. Firstly,
the RHIDE could improve its accuracy by increasing sample rate, streamline data pathways
and the introduction of a calibration protocol. Secondly, both systems measure different
contact/release angles and are therefore not comparable in this matter.
Overall, RHIDE has the potential to occupy a unique position in wheelchair propulsion
analysis by enabling the measurement of daily activities. With RHIDE, it would be feasible
to (1) provide information about propulsion technique by supplying push/release angles and
push times and (2) offer insights into daily wheelchair use over extended periods.
in-field wheelchair handrim push characteristics. The research question is if the RHIDE
can accurately discriminate propulsion/recovery and determine contact/release angles. This
research question is answered by determining push time (τ ), contact angle (α), release angle
(β) and push time (∆) for each push. To validate the RHIDE, these biomechanics are
measured with two different systems, one being the RHIDE and the other being the MarkerLess and Machine Learning Vision System (MLVS). Validation is conducted using data
from an experiment comprising 12 wheelchair propulsion-related tests on an ergometer, with
variations in speed (S1 and S2) and resistance (R1 and R2). For every push, τ , α, β and
∆ are extracted with both systems and the reliability of the RHIDE is calculated using an
ICC(2,1) on absolute agreement of the biomechanics for all pushes. For τ , ICC values are
0.65 for S1R1, 0.88 for S1R2, 0.98 S2R1 and 0.96 for S2R2 therefore reliability is considered
moderate, good, excellent and excellent respectively. For α, ICC values are 0.67 for S1R1,
0.59 for S1R2, 0.56 S2R1 and 0.51 for S2R2 giving moderate reliability for all tests. The
β ICC values are 0.57 for S1R1, 0.34 for S1R2, 0.35 S2R1 and 0.26 for S2R2 giving poor
reliability for all tests except S1R1. At last, ∆ ICC values are 0.17 for S1R1, 0.52 for S1R2,
0.25 S2R1 and 0.14 for S2R2, giving poor reliability for all tests except S1R2. Overall,
the RHIDE is able to accurately discriminate propulsion/recovery phases. Touch/release
angles are not reliable between systems. There are two potential causes for this. Firstly,
the RHIDE could improve its accuracy by increasing sample rate, streamline data pathways
and the introduction of a calibration protocol. Secondly, both systems measure different
contact/release angles and are therefore not comparable in this matter.
Overall, RHIDE has the potential to occupy a unique position in wheelchair propulsion
analysis by enabling the measurement of daily activities. With RHIDE, it would be feasible
to (1) provide information about propulsion technique by supplying push/release angles and
push times and (2) offer insights into daily wheelchair use over extended periods.
Many wheelchair users actively engage in wheelchair sports, for instance in the Paralympics. Trainers increasingly use the developments in sports technology for analysis. But wheelchair athletes do not benefit as much from these developments. A wheelchair can, for instance, disturb signal transmission. Recently, a set of studies describe the development of a wheelchair measurement system, the Wheelchair Mobility Performance Monitor (WMPM). It monitors wheelchair kinematics based on a wheelchair model and two Inertial Measurement Units (IMUs). The WMPM also gives relative location and orientation i.e. relative pose. But this does not allow for game analysis, which requires the absolute location of an athlete. A way to obtain the absolute location of an athlete is the use of Ultra-wideband (UWB) technology. It is often used due to its relatively high ranging accuracy and affordability. However, UWB signal transmission can be disturbed due to Non-Line-Of-Sight (NLoS) situations, where the body of an athlete, or the wheelchair, is located between two UWB nodes. This reduces the localization precision and accuracy. Game dynamics, i.e. quick changes in direction and velocity, also reduce the UWB localization precision and accuracy. Separately, UWB and the WMPM are not sufficient for proper absolute pose tracking. But a combination of these systems, i.e. sensor fusion, could be a good alternative. To provide trainers with real-time tracking data, the sensor data of these systems need to be fused on an embedded system. This requires an efficient UWB localization solution. A recent algorithm by Larsson formulates a localization problem with an easier-to-compute linear eigenvalue approach. In this work, this approach is validated and compared with a previous implementation via simulations and measurements. We show a execution time that is 5 times faster, without accuracy loss. An Unscented Kalman Filter (UKF) is implemented to fuse WMPM and UWB data and it is improved with a Rauch-Tung-Striebel smoother. An IR-camera system (Optitrack), providing the ground truth, is used to validate the UKF. The solution has a Root Mean Square Error (RMSE) of less than 20 cm in dynamic wheelchair situations. This work provides a basis for further development of a platform where WMPM and UWB are integrated for paralympic sports.
...
Many wheelchair users actively engage in wheelchair sports, for instance in the Paralympics. Trainers increasingly use the developments in sports technology for analysis. But wheelchair athletes do not benefit as much from these developments. A wheelchair can, for instance, disturb signal transmission. Recently, a set of studies describe the development of a wheelchair measurement system, the Wheelchair Mobility Performance Monitor (WMPM). It monitors wheelchair kinematics based on a wheelchair model and two Inertial Measurement Units (IMUs). The WMPM also gives relative location and orientation i.e. relative pose. But this does not allow for game analysis, which requires the absolute location of an athlete. A way to obtain the absolute location of an athlete is the use of Ultra-wideband (UWB) technology. It is often used due to its relatively high ranging accuracy and affordability. However, UWB signal transmission can be disturbed due to Non-Line-Of-Sight (NLoS) situations, where the body of an athlete, or the wheelchair, is located between two UWB nodes. This reduces the localization precision and accuracy. Game dynamics, i.e. quick changes in direction and velocity, also reduce the UWB localization precision and accuracy. Separately, UWB and the WMPM are not sufficient for proper absolute pose tracking. But a combination of these systems, i.e. sensor fusion, could be a good alternative. To provide trainers with real-time tracking data, the sensor data of these systems need to be fused on an embedded system. This requires an efficient UWB localization solution. A recent algorithm by Larsson formulates a localization problem with an easier-to-compute linear eigenvalue approach. In this work, this approach is validated and compared with a previous implementation via simulations and measurements. We show a execution time that is 5 times faster, without accuracy loss. An Unscented Kalman Filter (UKF) is implemented to fuse WMPM and UWB data and it is improved with a Rauch-Tung-Striebel smoother. An IR-camera system (Optitrack), providing the ground truth, is used to validate the UKF. The solution has a Root Mean Square Error (RMSE) of less than 20 cm in dynamic wheelchair situations. This work provides a basis for further development of a platform where WMPM and UWB are integrated for paralympic sports.