Hermie J. Hermens, PhD

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)
• PDAs 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
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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

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

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Sensor development in Twente

• Capacitive EXG

• EMG garment

• Full body movements

• 3-D Force shoe

• Activity monitoring

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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

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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
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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
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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
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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
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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

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Do low back pain patients show an abnormal
activity pattern over the day?

• Chronic pain patients (n29) and asymptomatic
  controls (n20)
• 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
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Do low back pain patients show an abnormal
activity pattern?
1,4
controls
patients
1,2
1
0,8
mean acceleration
0,6
0,4
0,2
0
7am
9am
1pm
5pm
7pm
9pm
3pm
11am
11pm
time

• Overall activity level not different between
  patients and controls
• Activity pattern Patients unbalanced significant
  higher in the morning and lower in the evening

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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

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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.

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The M-health service platform
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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
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Case 3. Fully ambulatory training of
neck/shoulder pain
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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
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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

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EMG Processing for Feedback
Calculate relative relaxation time (RRT
(resampling 125 ms moving window 1 m.)
Filtering Rectification Smoothing
If RRTlt20, warn subject with vibration
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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)
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The service system to enable remote consultation
Sensors Actuators
Bluetooth
Wires
Web based Viewer
UMTS/GPRS
Front-end
Autonomous Feedback
Mobile Base Unit
Consultation Feedback From health care
professional
Exozorg
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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
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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
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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

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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

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Case 2b Monitoring of low back muscle activation
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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?

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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)
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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
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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

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Present and future of Remote Monitoring and
Treatment (RMT)
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Present status Remote Monitoring Treatment

• Minus
• 75 applications fail in valorisation phase
  (Berg)
• Technology not mature
• Feedback primitive, not encouraging
• Health care organisations not ready yet
• Upscaling not yet possible (Decision support
  missing)
• Clinical evidence limited to pilot studies

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Present status Remote Monitoring Treatment

• Technology (sensors, ICT) rapidly developing
• Health Insurance companies get interested
• On agenda of EC, national research agendas
• RMT market 5.6 BD, growing with 70 per year
  (Liebert)
• 2-5 years for mainstream adoption (Gartner)

Gartner 2006
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Focus/trends of RMT in the next years
intelligent autonomic personal health systems

• Biomedical Technology creating comfortable,
  accurate and robust sensing systems
• ICT creating scalable, dependable, autonomic,
  intelligent systems
• Integration with Ambient Assisted living
• Creation of virtual communities for support and
  motivation
• Start with drugs delivery systems with implanted
  sensors and remote support of complex supportive
  systems

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Remote Monitoring Training
Very challenging area, Requiring strong
interdisciplinary cooperation Still in its
infancy period But with a great promise
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Thank you for your attention

• Many Thanks to
• Colleagues from Roessingh Research
• Colleagues from University of Twente
• Colleagues from Roessingh Rehabilitation centre