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.

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.