Upper limb functional deficits, including increased muscle tone and experience of pain and weakness during directed arm motion, are often associated with patients following acquired brain injury. Stroke is the most prevailing cause of disability in the UK and US, being most common in the elderly population.
Upper limb movement deficits also occur in younger people, often due to peripheral nerve injury in traffic related accidents as well as in military personnel. A loss of functional upper limb motor control can have a bigger impact on this population, as most patients are still in working age.
The overarching goal of this project is to use accessible technologies, such as motion capture and virtual reality, to design an assessment and training tool to support standard rehabilitation interventions in clinic and at home. We aim to achieve this by quantifying movement performance parameters to inform patients about their recovery and clinicians about the effects of their interventions.
However, this study narrows the focus to test the feasibility of such a setup. As a result, before testing patients, we aim to answer a few simpler questions:
Can we use a few kinematic parameters, such as positional error and velocity, to show the effects of a mechanical elbow constraint on healthy participants? Is such artificial elbow restriction representative of patient behavior, who often experience difficulties to extend their arms outward during reaching movements?
Patient movement range and quality is limited. As an attempt to simulate this limitation we used a constraint condition in a within-subject target tracking design to test the effects of limited motion on healthy participants. As the constraint we used a reinforced elbow splint to limit elbow flexion-extension motions. We expect this intervention will have a negative effect on movement performance, shown in a lower velocity and higher positional tracking error, compared to the unconstrained, control condition. Each condition was blocked and randomized across participants. Target motions were oval in shape at an average velocity of 10cm/s and followed the two-third power law. We programmed the target trajectories in either a frontal or transverse plane within a limited space of a 30x30x30cm cube. We present data collected from 11 healthy post graduate students, who were asked to follow the target motions as accurately and as quickly as possible.
Figure 1: Upper limb movement assessment setup with marker based motion capture cameras and a head-mounted-display (HMD) to present moving targets in a immersive virtual environment.
As shown in figure 1 and 2, we used a target tracking protocol to assess movement range and tracking performance by extracting kinematic parameters, figure 3, such as velocity, acceleration and positional accuracy.
Figure 2: Virtual environment with target and hand positions represented by simple geometrical shapes (cubes). The background objects and landscape serve as visual reference points.
Figure 3A: The positional error is defined as the distance between the center of the hand (yellow frame) and the center of the target (red cube). B: Excluding the initial error period, defined as the first two seconds, this error was averaged to represent the overall error for the individual trial.
In contrast to our expectations, we found a significantly reduced positional error in the constrained condition compared to the control, n=11, M=67.8, SD=4.8 mm/s, p < 0.05. And for tangential velocity we did not find any difference, figure 4.
Figure 4A: Position error averaged over all trials and participants (n=11) with a significant difference between the unconstrained (M = 76.16, SD = 11.24) and constrained (M = 67.76, SD = 4.79) conditions, p < 0.05. B: Average tangential velocity (n=11) for the unconstrained (M = 0.07, SD = 0.08) and constrained (M = 1.1, SD = 0.09) conditions, p > 0.05.
We can positively answer the first of the initial two questions, i.e. can we use kinematic parameters to show any difference in our intervention? However, it seems we have the opposite effect to our initial expectations, because the constrained has significantly improved positional error. This also answers our second question: Our elbow constraint is not representative of the movements deficits observed in the patient population.
Figure A1 A: Typical path taken to a target (point-to-point) by a participant compared to a straight line. B: Tangential velocity profile for a point-to-point motion