Potential for new technologies in clinical practice

Transcription

1 Potential for new technologies in clinical practice International Neurorehabilitation Symposium University of Zurich Feb 12-13th 2009 Prof Jane Burridge Jane Burridge Feb 2009

2 Introduction Societal drivers Technological opportunities Clinical motivation Evidence for effectiveness and users perspective Combining robot therapy and FES The future

3 Examples of Rehabilitation technologies Rehabilitation Robots Functional Electrical Stimulation Saeboflex Personalised, interactive computer-based activities

4 Societal drivers In 2030 nearly 25% of people in the EU will be over 65 (an increase of 17% from 2005). Europe's old-age dependency ratio (the number of people >65 compared with the number of working-age people will more than double by 2050 The cost of stroke care in Europe is predicted to rise in real terms by 30% between 1991 and 2010 Increased burden on health care and rehabilitation resources the crunch is coming! If the capacity of health services is to meet future demand we need to develop novel approaches to rehabilitation. Enabling rehabilitation outside the hospital and using technology may: reduce cost increase intensity of therapy shift the emphasis of responsibility for good health from healthcare professional to patient

5 Technological opportunities (FES and Robots) Revolution in the use of technology in rehabilitation accelerating change in attitudes First use of FES for drop-foot in In 1990 FES was confined to a few centres & mainly research over 6000 registered with the National Clinical FES centre in Salisbury UK Approval by NICE for the use of FES for foot-drop. Reimbursement for the Bioness L300 for SCI in the US. Robot therapy is more recent: 1994: first clinical use of the MIT Manus currently >500 patients involved in US studies (Krebs) 2001 First clinical use of the Lokomat - currently >200 devices in use 2007 Launch of Armeo currently 50 centres in 15 countries New robot therapy systems are emerging that have the potential to be used in the home.

6 For successful clinical use systems must be: Robust and reliable Motivating, engaging and easy for patients to use Made possible by advances in electronic and software design, signal processing and control engineering Demonstrate clinical benefit and cost-effectiveness Driven by a better understanding of the neuroscience of motor learning and control

7 Clinical drivers for rehabilitation robotics Historically Rehabilitation has been the poor relation in Medicine Absence of scientific rationale Absence of evidence No conventional rehabilitation procedures are any better than any other but intensity is important (Pomeroy) Recovery of lower limb function (walking) is better than upper limb function [Hiraoka 2001] Of a sample of patients with no active upper limb movement on admission: 14% experienced complete recovery 30% partial recovery 56% had no recovery [Hendricks 2002] 5% of survivors with severe paresis regain upper limb function [Barecca 2001] Can we do better?

8 Examples of upper limb robotic training systems 2D (e.g. MIT Manus) and 3D systems (e.g. Gentle/s) with mechanical active-assisted / progressive-resisted training 3D un-weighing systems (Armeo) Bilateral arm systems for wrist flex/ext and pro/sup (Bi- Manu-Track) Systems that augment error or distort reality (see: Johnson 2005, Patten 2006 and Tang 2007)

9 Examples of therapeutic robots and counterbalance systems

10

11 Theoretical benefit of Rehabilitation Robots Robot will allow the patient to achieve a task Repetitive goal orientated practice requiring attention Tasks can be adjusted to provide success at the limit of performance Motivating and varied VR / games Allows intensive and safe training could be used in conjunction with CIMT (shaping) therapy at home Appropriate for all levels of ability

12 Clinical Evidence

13 Evidence for Robot Therapy Strong evidence for improved motor control (impairment) and some evidence for improved function [Kwakkel 2008, EBRSR & Prange 2006] Proximal training = proximal benefit Possibly people with moderate impairment respond better Better understanding of how therapy should be applied dose, activities, bilateral, resisted / assisted Include hand and wrist / order of training Potential for combining functional training with robot training

14 Systematic review Kwakkel (2008)

15

16 Using iterative learning control to modulate electrical stimulation (ES) in a robot workstation tracking task 2D pursuit tracking task Using ES rather than mechanical error correction ILC to ensure that minimal ES is applied to correct tracking error

17 Why combine ES and Robot therapy? Association between ES and voluntary activity is an important factor affecting outcome (de Kroon et al. 2005) Supported by motor learning and neurophysiology theory Hebbian learning / LTP Current ES systems trigger ES but do not vary output in response to performance Theoretical argument that incentive to use voluntary effort is inhibited by applying ES

18 Method - Objectives Identify normal muscle activation patterns during a range of different tasks in the robot arm - Movement analysis and EMG recordings Create a dynamic model of the upper limb and the effect of stimulation of one muscle to write ILC programme Test the model with unimpaired subjects with no voluntary input - FES modelling and trial of Control Algorithms Test the model with impaired subjects and with voluntary input (Pre and post Assessments, Interventions)

19 Iterative Learning Control Applicable to one class of problem Trajectory, repetitive or cyclic tasks where error can be defined Uses information from previous iterations to generate a plant input to reduce tracking error Error is reduced to zero over (in our study) six iterations

20 Method The workstation uses a robotic arm to: Constrain the arm to move in the horizontal plane Provide additional assistance if error cannot be reduced by FES Elliptical trajectories are projected onto a target above the patient s hand ES to triceps for elbow extension ILC is used to modulate stimulation output (timing and pulse-width) Minimal stimulation is applied to encourage voluntary effort

21 Stroke Rehabilitation - Workstation Subject using the workstation Elliptical projected trajectory

22 An illustration of the tracking task showing poor and good performance. Red LEDs indicate off target and green on target

23 Learning Control (ILC) using a Robot & FES - ILC algorithm applies during extension phase only

24 Conclusion Early results suggested iterative learning control mediated by FES could be used to enable normal subjects to accurately track a trajectory within six iterations Preliminary analysis of results with stroke patients suggests that motor tracking is improved over a course of 18 sessions Future Work Used for other neurological conditions, such as cerebral palsy and incomplete spinal cord injury Next study will develop system in 3D and include opening of wrist and hand using Smart glove as position sensor

25 Bioness inc. H200 Cyclic triggered stimulation enabling functional tasks

26 Training Orthosis for practicing reaching and grasping objects Target group Ability to make a grip but unable to open the hand or release Impaired elbow extension Stimulation Wrist, finger and thumb extensors Triceps Stimulation triggered by movement sensor Assistance to reach and open the hand in response to attempt to make movement

27 Implanted microstimulators (Bions) for closed loop upper limb rehabilitation post-stroke Microstimulators implanted into elbow, wrist and finger/thumb extensors Independent control of each device Sensors initiate stimulation and transfer between activity sequences Stimulation is responsive to participants speed of movement Therapeutic effect of 12 weeks home exercise and 12 week follow-up

28 Using sensors to control stimulation sequences during a reach and grasp activity

29

30 Saeboflex

31 Position and EMG Biofeedback Easy to use by both patient and clinician Interesting to use and feasible for use at home EMG systems do not reflect functional movement Inertial systems lack precision

32 Motivating mobility

33 UK National Institute for Health Research (NIHR) Innovative technology for the rehabilitation of the upper limb following stroke Five-year project ( 1.9M) funded by NIHR to provide an evidence based clinical service for upper limb stroke rehabilitation Technologies to supplement or replace conventional therapy Robotics Constraint induced movement therapy (CIMT), Functional electrical stimulation (FES) Orthotics Biofeedback Botulinum toxin Objectives, described as Work Packages (WPs) 1. Survey of current treatment and outcome measures 2. Systematic review of literature 3. Qualitative study: barriers to use of ATs - patients and clinicians and commissioners 4. Clinical trials to assess the cost effectiveness of AT combinations 5. Propose a new care pathway and an implementation strategy based on the results 6. Dissemination of results - NHS & patient groups

34 Future Directions Including the hand: Is order of training important? Krebs et al, Robot-Aided Neurorehabilitation: A Robot for Wrist Rehabilitation IEEE TNSRE 2007 Haptic feedback: Cuijpers et al, Consistent haptic feedback is required but it is not enough for natural reaching to virtual cylinders. Human Movement Science 2008 integrating visual and haptic feedback Wearable robots and take home systems Combining FES with Robotics:

35 Designing a Smart Armeo Including the wrist and hand Initial work to model normal hand opening in the specific tasks Mechanical opening ES to open the hand Providing feedback

36 Some summary thoughts about Rehabilitation Technologies The engineering can be complex but the system must be simple to use Must be easy to use, don/doff and capable of being used without supervision Designed in collaboration with patients, carers and clinicians Other considerations: Informed by an understanding of neuroscience Designed to improve function Cost effectiveness Used at home rehabilitation unit local gym Enable an increase intensity of practice Motivating fun - challenging - sociable