Peter Wilson School of Psychology, Australian Catholic University Melbourne Campus

1. A developmental cognitive neuroscience perspective on motor rehabilitation: The case for VR-augmented therapy Peter Wilson School of Psychology, Australian Catholic University Melbourne Campus

2. Collaborating Team Peter H. Wilson1, Dido Green2, Jonathan Duckworth2, Jan Piek4 1School of Psychology, ACU, Melbourne, Australia 2School of Occupational Therapy, Oxford-Brookes University, Oxford UK 3Creative Media & Communication, RMIT University, Melbourne 4Curtin University, Perth, Australia

3. Aims • Sketch the core processes that help explain motor development (and deviations from it) • Present a conceptual model for VR-based, paediatric movement rehabilitation • Highlight the importance of multimodal augmented feedback in training predictive control

4. What processes drive typical skill development in children? • Body schema – linked to multimodal integration • Internal modeling – • Mapping output signals and their effects on the body • Enables prediction and online control Prediction develops rapidly over childhood: • Force control • Postural adjustments • Online control  Progressive integration of feedback & feedforward mechanisms; well developed by 8-9 years.

5. Developmental Coordination Disorder DCD = Motor clumsiness in children, not explained by a medical condition (like MS, etc.) DSM-V category Issues • What are core deficits? • Is there a neural locus? • Is severe DCD a mild form of CP??

6. Modeling atypical development: A multi-level perspective E N V I R O N M E N T  

7. Key levels of analysis • Maturational integrity of the CNS • Motor control mechanisms • Biomechanics: kinetics & kinematics • Cognitive control mechanisms • Energetics – arousal, motivation, etc. • Others Top-down is still important

8. Meta-Analysis Aims • Quantitative review of the published performance data since 1997 • Identify the motor control, learning, and cognitive control deficits that best discriminate between children with and without DCD • Identify patterns that suggest causal mechanisms Wilson et al. (in press). Developmental Medicine & Child Neurology.

9. Contributing Studies Final “sample” of studies = 129. 1785 effect sizes

10. Main Findings - Motor Control The main motor control deficits relate to: • Predictive control / forward internal modeling • > 5 categories with v. high effect size (d > 1.0) • Timing and coordination of interceptive actions • Postural stabilisation, adjustment & anticipation • Rhythmic coordination • Gait dynamics

11. Limb Movement Neuro-computational model 1 Magescas et al. (2009)

12. Neuro-computational model 2

13. Forward modeling – the simple one! From Nowak (2007)

14. Issues for Treatment • Practice does not guarantee skill in CP and DCD! • Excess neural noise  greater performance variability in DCD • Feedback dependency in DCD, with costs for speed & online control • Need techniques that will build predictive control  Concurrent augmented feedback + Attentional cuing.

15. Augmented Feedback Forms of AF • Knowledge of Results (KR) • Knowledge of Performance (KP), and • Concurrent AF (C-AF).

16. The benefits of AF are well documented • mainstream motor learning (Carson and Kelso 2004; Gordon & Magill 2012). • rehabilitation of brain injury (Winstein, Wing et al. 2003; van Vliet& Wulf, 2006). BUT, the quality of evidence varies. Little is known a/b the relative effect of visual, verbal, video and kinematic feedback, and optimal scheduling.

17. How does C-AF work? • C-AF (which promotes an external focus of attention) can assist recovery of upper-limb function in ABI (e.g., Quaney et al. 2010). • C-AF may do so by training motor prediction (aka forward modeling) – empirical question. • Sobering is the fact that most verbal instructions (esp. in clinical settings) are likely to induce an internalfocus of attention (Durham, Van Vliet et al. 2009).

18. Training (motor) prediction using C-AF C-AF serves two main purposes: • Provides children with additional input on the outcomes of their actions:reinforces the relationship b/n motor output & consequence.  builds body schema • Encourages the child to focus their attention on the effects of their movement (Wulf & Prinz 2001). Hypothesis: VR-augmented therapy works by training predictive control (and associated body schema “knowledge”)

19. Virtual workspace design – Embodied interaction VR-based systems are the perfect vehicle for C-AF: • Multimodal C-AF • Aesthetic design • Intuitive (tangible) interfaces • Client-centred

20. Augmented Feedback

21. Exploratory interaction / “training”

22. Exploratory Tasks – Squiggles

23. Exploratory Tasks – Mixer

24. Exploratory Tasks – Swarm

25. Multimodal AF works in paediatric rehab (but is part of a treatment package)! Green & Wilson (2011) - Mixed hemiplegic group  Green, Wilson, & Lin (2012) – As above BUT, we still don’t know the relative impact of different components of the system

26. FIN

28. System measures • Accuracy • % overlap b/n target & object • Movement Speed • Rate of movement (m/s) • Efficiency • Deviation of object from straight line path • % score

29. Within-group Evaluation

30. Within-group design Performance assessed at 3 time points: Pre-test 1 4 weeks before VR therapy Pre-test 2 Immediately before VR therapy Post-test Immediately after 4 weeks of VR therapy

31. Participants 9 patients (5 male) with severe TBI, recruited from Epworth Hospital, Melbourne. Age range: 23 – 49 years. Note: Both L-R side affected, but L somewhat more in 6/10 PTA range: 28 – 630 days. Inclusion criteria • < 50 y-o • Score of 2+ for muscle activity, Oxford Scale • Cognitively able to understand the program, and provide consent

32. System measures • Accuracy • Movement Speed • Efficiency

33. Standardised measures (1) Box and Block test • blocks moved in 60 s • (2) McCarron Assessment of Neuromuscular Dysfunction (MAND) • Timed nuts-&-bolts task = Bimanual

34. Neurobehavioral Functioning Inventory (NFI) A measure of cognitive & functional impairments in TBI. Sub-scales are: • Depression • Somatic • Memory/attention • Communication • Aggression • Motor

35. Adapted Presence Questionnaire • Sub-scales: • Involvement/Control - engagement & ability to exert control • Interface Quality – how intuitive and easy to use • Distraction – ability to “isolate” user from external environ’t. • Sensory factors - richness of VE & multimodal info. • 5-point Likert scale (1=not at all; 5=a great deal).

36. Results

37. Data analysis • Planned comparisons for 6 DVs • Pre-Test Contrast - Pre1 vs. Pre2 • Pre-Post Contrast – (Pre1, Pre2) vs. Post

38. Subjective evaluation of the exploratory VEs Averaged over the three exploratory environments, mean ratings were: Involvement/Control - 3.90 (SD=0.54) Interface Quality - 4.13 (SD=0.06) Distraction - 4.84 (SD=0.15) Sensory factors - 4.00 (SD=0.18)

39. Conclusions • VR-based therapy “value adds” to upper limb rehab for TBI • Training effects tend to be task-specific, with some functional gains, BUT • Training effects more mixed on standardised measures. Why? Bimanual performance was not targeted by our system. (See also other VR work).

40. Subjective evaluation of the exploratory VEs • High presence engendered by the exploratory VEs, esp. task involvement / control. • Sensory stimulation was strong • Engagement was high, & distraction low • Sense of control • Motivational incentive for patients in creating their own feedback effects (visual and auditory). The combination of goal-based and exploratory environments holds great promise.

41. Where to from here?

42. Re-Action Project (London) – Green, Lin, & Wilson Paediatric VR

43. The art and science of interface design Affordance, tactility, curiosity, play Soft Graspable User Interfaces

44. Never you mind. I’ve been practising! Co-located environments “WOW! Where’d you get that move from?” The power of social facilitation and cooperation

45. Blending motor & cognitive virtual rehabilitation

46. FIN