Page 1

Australas. Phys. Eng. Sci. Med. Vol. 31, No 4, 2008

EPSM Conference Abstracts

consideration of the capacities and constraints of the human visual and cognitive faculties. Furthermore, in order to assess the usability of these enhanced systems objectively, we must make use of methodologies from human-computer interface design, perceptual testing, and visual-motor paradigms from experimental psychology. Discussion: Our current User-Interface and Psychology research project involves examining individual differences in strategy choice on spatial tasks, their aptitude for spatial reasoning, and how these differences can best be supported when designing computer interfaces for surgical training. We are developing user interface software according to the principle that users may prefer different interaction modes and viewpoints depending on which subtask is performed within an overarching surgical procedure. We are using workflow analysis to rank these subtasks and categorize according to factors of Diagnosis, Planning, Wayfinding, Navigation, Manipulation, and Retrieval. These implicate different usage, and different display modalities, when performing a task using a 'virtualized' interface. We are investigating specific individual differences in spatial reasoning and using the results to design user interfaces which are adaptable to the individual. SUBMOVEMENTS IN REACHING TASKS: THE EFFECT OF PARKINSON'S DISEASE Daniel Myall1,2, Michael MacAskill1,2, Tim Anderson1,2,3 and Richard Jones1,2,4,5 1

Van der Veer Institute for Parkinson's and Brain Research, Christchurch, New Zealand 2 Department of Medicine, University of Otago, Christchurch, New Zealand 3 Department of Neurology, Canterbury District Health Board, Christchurch, New Zealand 4 Department of Medical Physics and Bioengineering, Canterbury District Health Board, Christchurch, New Zealand 5 Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand

Introduction: Movements in people with Parkinsonâ&#x20AC;&#x2122;s disease are often hypometric, although we shown that this was not the case in an experimental visually-guided reaching task. However, these movements may also be intrinsically hypometric but visual feedback may be used to get accurately to the target in a single, albeit complex, movement. This hypometria may be more pronounced in memory-guided movements that involve the basal ganglia to a greater degree. Decomposing movements into individual components should reveal the strategy that is used. A fast movement to a fixed target can be thought of as being composed of one or more submovements, each of which is preprogrammed and generated by an inverse internal model. The first of these is called the initial, or primary, submovement. We wished to explore our hypotheses that (1) people with Parkinson's disease produce hypometric primary submovements but (2) are able to use visual feedback to accurately reach the target in a single overall movement and (3) this effect may be greater in memory-guided tasks in which an internal representation of the target location is used instead of a fixation-centered representation of the target. Methods: A Movement and Virtual Environment (MoVE) system, comprising a calibrated, low-latency, near-field 3D virtual environment with an electromagnetic tracker, was used to run the experiment and record the data. Visually- and memoryguided reaching movements were examined in 22 people with mild to moderate severity Parkinson's disease on medication, along with age-matched and sex-matched controls. Primary submovements were extracted from 5149 movements using a method based upon zero crossings of jerk (3rd derivative of position), with several additional criteria to minimize the detection of submovements due to noise or tremor. The gains of the primary and final movements were then calculated. A linear mixed-effects model was used for the multiple dependent variables, with fixed effects of Group and Task, and a random effect of subject. Results: For the final gain of the movement to the target, there was no Group effect [F(1,42) = 0.834, p = 0.37] but there was a Task effect, with the memory-guided movements shorter than the visually-guided movements [mean gain 0.95 vs 0.92, F(1,42) = 4.0, p < 0.05]. For the primary submovement gain, there was a Group effect with the Parkinsonâ&#x20AC;&#x2122;s disease group having a smaller gain for the primary submovement in the visually-guided movement [mean 0.73 vs 0.85, F(1,42) = 7.6, p < 0.01]. There was a Task effect [F(1,42) = 17, p < 0.0001], and Group and Task interaction [F(1,42) = 9.1, p < 0.01]. The effect of Task and was due to the Parkinsonâ&#x20AC;&#x2122;s disease group gain being smaller on the memory-guided task [mean 0.68 vs 0.73, p < 0.0001] but with no change in the control group [mean 0.84 vs 0.85, p = 0.47]. Discussion: The final gain in both tasks was not different between groups, although there was a task effect. On the memoryguided task, overall gains were smaller, related to spatial memory underestimating the distance to the target. In comparison to the final gain, the gain of the primary submovement was found to be substantially smaller in the Parkinson's disease group compared to controls in both tasks. Also, while the gain of the primary submovement was equal in both tasks for the control group (even though the final gain of the memory-guided movement was smaller), the gain was smaller in the memory-guided task compared to the visually-guided task in the Parkinson's disease group (even though the final gain in the memory-guided task was not different to that of controls). This highlights the extra difficulty people with Parkinson's disease have in making a movement of the correct size, especially when the target is represented in spatial memory or is internally generated. In older people and in pathological conditions there are several other contenders for submovements beside a primary submovement and corrective submovement. Firstly, if the multiple limb segments are not coordinated perfectly this can lead to irregularities in the movement traces which are not directly attributable to a separately generated submovement. Additionally, multiple forms of tremor (e.g., rest, action, and essential) all add irregularities to the movement trace and are a 372


Australas. Phys. Eng. Sci. Med. Vol. 31, No 4, 2008

EPSM Conference Abstracts

form of overlaid unintentional submovements. However, use of criteria that a detection has to be significant in comparison to jitter at rest, which in many cases causes the algorithm to treat the peak velocity of the movement as the peak velocity of the submovement, means that in many cases the algorithm is likely over-estimating the size of the primary submovement in subjects with tremor. Conclusions: Our results show that the underlying primary submovement in visually-guided movements in people with Parkinson's disease is hypometric and that the degree of hypometria is even greater in memory-guided movements. More sophisticated submovement decomposition methods, in combination with additional sensors attached to all upper-limb segments in the analysis, may be able to tease out the contributions of the different joints and the components of the movement that are due to a form of tremor. This will offer greater insight into how the production and size of submovements change in people with Parkinson’s disease and may also offer an objective measure to quantify motor improvements due to treatment. AUGMENTED REALITY INTERACTION TECHNIQUES FOR MEDICAL VISUALIZATION APPLICATIONS Mark Billinghurst HIT Lab NZ, University of Canterbury, Christchurch, New Zealand

Augmented Reality (AR) allows medical data to be overlaid on the real world, providing new methods for medical visualization. However there still is significant research that can be performed in how to interact with AR content. In this presentation we review a variety of AR interaction techniques that may be useful for the medical domain and describe important research directions for the future. Some of these techniques include using lens based interaction, mobile AR displays, and transitional interfaces, among others. Research will be presented from the HIT Lab NZ and other AR research labs worldwide.

CLINICAL MODELLING

CLINICAL DATA VALIDATION OF A NEW, PHYSIOLOGICALLY RELEVANT CRITICAL CARE GLYCAEMIC CONTROL MODEL Normy Razak1, Jacquelyn Parente1, Jessica Lin2, J. Geoffrey Chase1, Christopher E. Hann1, Christopher Pretty1, Aaron J Le Compte1 and Geoffrey M Shaw2 1 Dept of Mechanical Eng, University of Canterbury, Centre for Bio-Engineering, Christchurch, NZ Department of Medicine, Univ of Otago Christchurch, and Christchurch Hospital, Christchurch, NZ

2

Introduction: Hyperglycaemia is prevalent in critical care due to the stress of condition, even without a previous history of diabetes. Tight glycaemic control is associated with significantly improved patient outcomes. However, providing tight control is difficult due to evolving patient condition and interactions with common drug therapies resulting in recurring hyperglycaemic episodes. Model-based and model-derived tight control methods, such as the SPRINT system in Christchurch, have shown significant reductions in mortality. However, as computational capability and access improve, there are still avenues of further improvement to be made – if better models and/or methods were available. This research presents an updated control model, and its predictive virtual patient validation for use in real-time glycaemic control. Methods: The model is based on prior work in this area by the authors’ research group and defines a simple pharmacokinetic and pharmacodynamic system model: GÝ= − pG ⋅ G − SI ⋅ G ⋅

P(t) +EGPmax − CNS Q + VG (t) 1+ α G Q

Q& = −kQ + kI

I& = −

u (t ) nI + ex + e −( k I uex ( t )) I B VI 1 + αI I

(1) (2) (3)

In the model, G is the blood glucose level, I is the plasma insulin, and Q is the interstitial insulin. EGPmax is the theoretical maximum endogenous glucose production for a patient under no presence of glucose or insulin. Endogenous glucose production (EGP) is suppressed with increasing G and Q. Insulin independent glucose removal (excluding central nervous 373

/0515  

http://www.vanderveer.org.nz/research/publications/papers/0515.pdf

Read more
Read more
Similar to
Popular now
Just for you