Hermie J. Hermens, PhD Roessingh Research & University of Twente

1. Telerehabilitation; Towards Remote Monitoring & Remotely Supervised Training Hermie J. Hermens, PhD Roessingh Research & University of Twente

2. Trends in health care • Rising demand for care • Increase of number of elderly people • Increase number of people with chronic disorders • Rand Coop predicts in US • 2010: 141 million people with chronic illness • 2030: 171 million people with chronic illness • Chronic Care now over 50% of total costs and increasing

3. Costs of Health Care (% BNP) • Source: OECD (2002) • Tsjechie 5.7 % BNP • England: 7.7 % BNP • Netherlands: 9.1% BNP • Germany 10.5 % BNP • Suisse 11.2 % BNP • United States 14.6 % BNP Quote Martin van Rijn (NL): with unchanged policies, health care will cost us 12% BNP and 20% of working people will have to work in healthcare

4. Results of these trends in health care • Rising demand for chronic care • With limited budget and people • Will force a higher productivity without losing our high quality • Changes in the customer will require • Independent living as long as possible • Individually tailored solutions All these changes will require a change in our approach to health care

5. Need to change our approach Present focus From Philips

6. Need to change our approach Required extensions From Philips

7. Trends in Technology • Sensing technology • High quality ambulatory acquisition possible • Smaller, low power, wireless connection • User friendly (integrated in textiles) • PDA’s get smarter and more powerful • Information & Communication Technology • Broadband connection available and cheap • Special data transport platforms available • Centralised Electronic Health record available

8. Creating new opportunities Combining Biomedical Engineering with Information and Communication Technology creates a new area of research and relevant clinical services: • Remote monitoring • Remotely supervised training Enabling monitoring and treatment of subjects anywhere, anytime and intervene when needed

9. Remote monitoring Guarding the health condition of a subject by measuring and interpretation of vital biosignals, without interference of his daily activities but able to react when required

10. Remotely supervised training & Treatment Enabling the subject to train at his time and place, providing him the same quality of feedback/assurance as in the intramural situation = Monitoring + dedicated feedback X

11. The potential benefits Monitoring • Less intramural care (costs) • More freedom for the patient • Peace of mind Remotely supervised treatment • High intensity of training possible (more = better) • Training in natural environment translates better to ADL situations (more effective training) • Patient himself responsible for results • Clinician can ‘treat’ several patients at the same time

12. The challenges • Are we able to make this technologically feasible ? • Will this result this in a same quality of treatment ? • Will this be accepted by health care providers ? • Will this be accepted by the patients ? Summarising experiences of the past ten years by: • Roessingh Research & Rehabilitation Centre & • University of Twente • Many partners (Lucent, Philips, Atos Origin, ..) Ongoing research focused, on ambulatory monitoring and treatment

13. Architecture Remote monitoring & remotely supervised training

14. General architecture RMT systems Decision support Personal Coach Feedback Care & Coaching Sensing Hospital & “Central” Database Informal coach (Hermens, 2008)

15. wireless (GPRS,UMTS, WiFi) An example: The Mobihealth system • Developed in European projects (Mobihealth and HS24, Awareness) • Supports mobile data transport • Supports various networks • Data encryption of biosignals • Access • User Identification by password • Device authentication by pin • Tested in many clinical pilot studies • See www.mobihealth.com secure wireless transmission patient data MyoTel Service Centre Internet hybrid data communication infrastructure

16. Sensing; general demands • Sensors • Sensors should be wearable, comfortable, forgettable • Autonomic placement feasible • Processing • Continuous sensing required, independent of place, time • “Real-time” processing and feature extraction required

17. Sensor development in Twente • Capacitive EXG • EMG garment • Activity monitoring • 3-D Force shoe • Full body movements

18. Automatic feature extraction in EMG E.g. spasticity (Detect when and how often muscle active) AGLR to detect changes in variance Post-processing based on physiol. properties

19. Looking for new features: physical condition Important variable in chronic diseases But requires max effort Can this be estimated in non max conditions Have people do various activity , while measuring ECG and activity ECG and activity

20. Estimation of physical condition from ambulatory measurements First results :Modeling predicts good correlations with Astrand cycle ergometer test and modified Bruce treadmill test

21. The Body Area Network (BAN) • Often more then on body sensor is required (sensor fusion): • To enable more robust features (e.g. Movements) • To enable different features (e.g. Physical condition) • And actuators for feedback purposes So, we need to connect multiple sensors and actuators to a central point to enable synchronised data collection

22. Several approaches to create a BAN • One amplifier/AD converter (classic) • Bussystem (e.g. Xsens) • Multiple bluetooth connections • Upcoming: Wireless sensor networks

23. Applications = Services

24. What kind of services should we develop? Considering that chronic diseases develop slowly but sudden events might happen Monitoring services should aim at: Monitoring sudden adverse events Detecting slow changes over time Detecting changes in patterns Treatment services should aim at: Providing feedback to the person, so he can change his “negative” behaviour and To the health care professionals for consultation purposes support and to enable interventions

25. Case 1 Chronic low back pain

26. A Tele-treatment of chronic pain patients • 80% all people ever have low back complaints • About 90% recovers, 10% becomes chronic • Over 80% no clear damage • Lot of medical shopping • High costs (5 BE, 1995) • Present treatments not very effective (35% for multidisc. Programs) • All models do predict a change in activities as part of the chronification process

27. Do low back pain patients show an abnormal activity pattern over the day? • Chronic pain patients (n=29) and asymptomatic controls (n=20) • Wore MT9 inertial 3-D motion sensor to measure the activity level during 7 consecutive days. • Fill in questionnaires to assess the activity level subjectively. Van Weering et al 2007

28. Do low back pain patients show an abnormal activity pattern? 1,4 controls patients • Overall activity level not different between patients and controls • Activity pattern Patients unbalanced: significant higher in the morning and lower in the evening 1,2 1 0,8 mean acceleration 0,6 0,4 0,2 0 7pm 9pm 7am 9am 1pm 5pm 3pm 11am 11pm time

29. An idea for a new treatment concept • Starting from: LBP patients have a dysbalanced activity pattern during the day • Assuming that such dysbalance in activity is an important component in the chronification process in low back pain • Conceptual idea: Normalising this activity pattern might reverse the chronification! • Realise this by providing continuous personalised feedback on the activity pattern

30. Personalised context aware feedback Scenario: After making breakfast for the kids and while doing the dishes, the system detects that Cinderella has been too active for a period of time. She receives feedback: • General feedback: • That she needs to rest for some minutes. • Personalized feedback (preferences): • That she should have some tea. • Context-aware feedback (time, weather, presence) • Drink a cup of tea in the backyard and enjoy the sun.

31. Personal context data Non-Personal context data Body area network Back End system Sensors &Actuators Bluetooth Wires UMTS/GPRS Transport system Front-end www Database interpretation, reasoning and storage Mobile Base Unit PC Continuous context aware feedback Healthcare professional Medical display Professional feedback The M-health service platform

32. Present status • Activity sensing implemented on PDA • Personalised messaging implemented • Context aware feedback not yet • Clinical trial recently started • First responses positive Input pain level Personalised feedback Feedback of performance

33. Case 3. Fully ambulatory training of neck/shoulder pain

34. Chronic neck/shoulder pain • Chronic pain in neck/shoulder with no clear cause of physical overloading • Often associated with computer work • Cinderella theory: • Lack of relaxation results in overloading specific muscle parts • Overloading results in pain • Pain results in changes in posture and more overloading Solution: warn the subject in case of insufficient relaxation, so subject is able to learn and adapt posture and muscle activation

35. Starting points for the system design • Assess muscle relaxation by surface EMG measurement and processing • Provide private feedback when there is insufficient relaxation • Enable an intense treatment outside the hospital! • during normal activities: fully ambulant • Non-obtrusive • Support independent

36. EMG sensing garment for neck/shoulder muscles

37. Summarizing our experiences • In about 50 patients: • Unstable signals during first five minutes, then good signal for over 24 hours • Requires initial individual fitting, then reproducible signals • Independent donning and doffing possible • Not interfering with activities of daily living

38. EMG Processing for Feedback Calculate relative relaxation time (RRT; (resampling 125 ms; moving window 1 m.) Filtering Rectification Smoothing If RRT<20%, warn subject with vibration

39. Myofeedback in practice • Able to improve muscle relaxation (international RCT) • Able to decrease pain complaints But often intensive supervision required: • Discuss experiences and results • Troubleshooting in first week(s) So, could this treatment be improved using ICT ? Voerman et al 2006; Huis intVeld 2007)

40. Database with signals And subject data PC Healthcare professional The service system to enable remote consultation Sensors &Actuators Wires Bluetooth Web based Viewer UMTS/GPRS Front-end Autonomous Feedback Mobile Base Unit Consultation & Feedback From health care professional Exozorg

41. Remotely supervised Myofeedback for treatment of neck/shoulder pain • Initial Questions: • Is it technically feasible to monitor muscle activity during 8 hours per day? • Is it accepted by patients and care givers? • Are care givers able to provide advice not seeing the patient? • Is it effective? Huis intVeld et al. 2007

42. Results Remotely supervised Myofeedback for treatment of neck/shoulder pain • Inital study in 10 patients • Often failure of wireless connections • Enough data was received at backend • Remote consultation feasible • Confidence in treatment both by patients and clinicians • Clinically at least as effective as non-remote myofeedback treatment • Now entered market validation study in 3 countries (eTEN project Myotel) Huis intVeld et al. 2007

43. The next step: show large scale feasibility • Show effectiveness and efficiency (Market validation ) in 3 countries (eTEN Myotel) • Development business plan • Development of Decision support system

44. Development of a CDSS • To assist the clinician and patient in optimising advices during consultation session • Characteristics: • Streaming data: 2 signals (RMS, RRT) of two muscles • Filtered, re-sampled at 4 Hz and stored in database • Together with activity diary and pain scores • Direction of solution • Using Bayesian network to • Detect technical failures • Relating specific activities to pain • Relate specific moments to related pain • Implement this in an agent platform

45. Case 2b Monitoring of low back muscle activation

46. A similar feedback treatment for low back pain ? • Literature shows inconsistent data on the muscle activation of the low back • Indications of both inactivity and hyperactivity patterns were found • So, what EMG patterns can be found and do they differ from normal subjects?

47. Ambulatory measurement of low back muscles Development: Special garment to measure the EMG Utilising dry electrodes in a flexible system to enable stable contact during activities of daily living Studies Able to don/doff independently without affecting the EMG signals ? Differences in muscle activity pattern between patients and controls How, what, when to feedback ? (de Nooy et al, 2008; patent pending)

48. Example EMG patterns low back muscles Accumulated EMG activity in the evening: Left healthy subject showing phasic patterns and Right a patient showing rather continuous low level activation

49. Present status Garment can be worn during at least 8 hours without pain/serious discomfort Sensitivity misplacement: low in longitudinal direction, high in lateral direction So far: data of 10 patients and 9 healthy subjects during 7 days Differences in patterns apparent Feedback strategy : avoid constant activity ? First implementation carried out

50. Present and future of Remote Monitoring and Treatment (RMT)