# Laboratory Assessment of Manual Wheelchair Propulsion
## 2 Experimental Testing Methods
### Test Stand and Method of Motion Capture  
The kinematics of human body segments is one of the most essential biomechanical parameters during wheelchair propulsion [35; 36; 37; 38; 39]. The propulsion cycle of a manual wheelchair can be divided into two phases, i.e., the push phase and the recovery phase [40]. During the push phase, mechanical energy is transferred to the wheel's push-rim through hand contact with the rim; during the recovery phase, the hand is displaced in preparation for the next push phase. During the push phase, the hand moves along with the rim. In the recovery phase, the hand can move along different paths, which can be classified based on the shape of the hand projection on the sagittal plane [41]. The literature distinguishes four stroke patterns: semicircular, single-looping, arc, and double-looping [35]. These stroke patterns are shown in Figure 5.  
Analyzing the kinematics of body segments of a person driving a wheelchair requires the development of a measurement stand and a data processing method. A method, along with the stand, is described in the publication _The method of measuring motion capture in wheelchairs during actual use-description of the method and model of measuring signal processing_[42]. The method employed an OpenCV algorithm called AruCo to locate markers (QR codes) placed on the filmed upper limb. By utilizing readily available algorithms, the authors developed a low-cost and user-friendly test apparatus consisting of a modular attachment that can be integrated with a wheelchair. This module, shown in Figure 6, consists of a GoPro HERO 7 camera (a) and a light (b) mounted on a boom (c) permanently connected to the wheelchair frame. During the tests, the human body was equipped with markers (d), and, in addition, it was possible to use electrodes that measured the EMG signal (e). The apparatus designed in this way makes it possible to relate the measured muscle activity to the kinematics of the human body.  
Figure 4: Wheelchair secured to the stationary roller dynamometer (source: own study): (**A**)—traction rollers, (**B**)—weight-scale lever, (**C**)—safety pin, (**D**)—clamps securing the wheelchair frame, (**E**)—lever arm height adjuster, (**F**)—fastened wheelchair frame.  
Due to the implementation of OpenCV class algorithms [43; 44] in a new application and the prototype nature of the test apparatus, the correctness of the operation of this apparatus was first verified, and its accuracy was determined. The results of this research were published in a paper titled _The effects of AraCo marker velocity and size on motion capture detection and accuracy in the context of human body kinematics analysis_[45]. This study found that a potential error in marker detection was related to the quality of the camera and the speed of marker movement. At the same time, the speed of movement of the marker had the most dominant influence on the value of the error and the risk of its occurrence. This relationship is shown in Figure 7.  
Based on analysis of the test results, both the apparatus and the data acquisition method were valid, as the relative error for different hand speeds during wheelchair propulsion did not exceed 6% [45].  
Figure 5: Stroke patterns of the wheelchair propulsion (based on the methodology outlined in [35]).  
Figure 6: Measurement apparatus used in the study: (**a**)—camera, (**b**)—illuminating lamp, (**c**)—boom, (**d**)—AruCo marker, (**e**)—EMG device (source: [42]).