Health-enabling technologies for pervasive health care: A ...• GAL objective: quality of life in the ageing society • independence within one’s own residence • development

Peter L. Reichertz Institute for Medical Informatics

Reinhold Haux

Matthias Gietzelt, Nils Hellrung, Wolfram Ludwig, Michael Marschollek, Bianying Song, Markus Wagner, Klaus-Hendrik Wolf

Peter L. Reichertz Institute for Medical Informatics University of Braunschweig - Institute of Technology and

Hannover Medical School

Medical Informatics Europe 2009, Sarajevo, Bosnia and Herzegovina

Health-enabling technologies for pervasive health care:

A pivotal field for future medical informatics research and education?


structure

•  background: the demographic change •  health-enabling technologies

and pervasive health care •  examples •  health-enabling technologies

and medical informatics


structure

•  on the Peter L. Reichertz Institute •  background: the demographic change •  health-enabling technologies

and pervasive health care •  examples •  health-enabling technologies

and medical informatics


•  Bardram JE. Pervasive healthcare as a scientific discipline. Methods Inf Med. 2008; 47: 178-85.

•  Haux R et al. Health-enabling technologies for pervasive health care. Inform Health Soc Care. 2008; 33: 77-89.

•  Koch S et al. On Health-enabling and ambient-assistive technologies. Methods Inf Med. 2009; 48: 29-37.

•  Saranummi N. IT applications for pervasive, personal, and personalized health. IEEE Trans Inf Technol Biomed. 2008; 12: 1-4.

•  Saranummi N, Woctlar H, editors. Pervasive Healthcare. Methods Inf Med. 2008; 47: 175-240.

•  GAL: www.altersgerechte-lebenswelten.de •  PLRI: www.plri.de

references


on the Peter L. Reichertz Institute


the Peter L. Reichertz Institute (PLRI)

Prof. Reichertz 1930 - 1987

•  since more than three decades medical informatics with Professor Peter L. Reichertz as pioneer

•  in 2007: University of Braunschweig - Institute of Technology (TU Braunschweig) and Hannover Medical School (MHH) unite their medical informatics institutes as a joint institute, named Peter L. Reichertz Institute for Medical Informatics with two locations Braunschweig and Hannover

•  aim: regional ‘center of excellence‘ •  2 locations, PLRI staff is member of both universities


the PLRI •  fields of research

•  health-enabling technologies

•  eLearning in medicine and dentistry

•  health information systems and management •  medical imaging and visualization

•  education •  medical informatics courses •  medical informatics program (B.Sc, M.Sc., Ph.D)


background: the demographic change


the demographic change adolescenting („aging“) societies „the number of persons aged 60 years or older is

estimated to be 629 million in 2002 and is projected to grow to almost 2 billion by 2050, at which time the population of older persons will be larger than the population of children (0-14 years) for the first time in human history“

(UN Population Division)


the demographic change

source: UN, Population Division 1950 2000 2050

12

9

4

year

Potential Support Ratio 1950-2050 World


the demographic change

source: UN, Population Division 1950 2000 2050

12

9

4

year

Potential Support Ratio 1950-2050 World / Europe

8

5

2


health-enabling technologies and pervasive health care


on health-enabling technologies (HET)

•  HET are information and communication technologies for creating sustainable conditions for self-sufficient and self-determined lifestyles

•  sensor-enhanced health information systems play a major role in this context, aiming to enable ambient-assisted living

•  by utilizing advanced HET, individual quality of life is intended to be enhanced, while sustaining the efficiency of health care


on pervasive health care (from Jacob Bardram): acute "continuous hospitalization home & outpatient reactive proactive & preventive IT assistive technology centralizend pervasive sampling monitoring doctor-centric patient-centric

Bardram J. Pervasive Healthcare as Discipline Methods Inf Med. 2008; 47: 178-185.


on HET & pervasive helth care: Niilo Saranummi’s 3‘P‘s •  pervasive technologies

shall enable semantically interoperable platforms to communicate and store health data and the use of health-enabling technologies

•  personal services using sensor technologies for continuously measuring health related data of an individual; to support her or him at specific health problems

•  personalized decision support adapted, ‘tuned’ to the individual’s norm, not to averages in populations


on HET: financial considerations •  example for possible cost savings through

health-enabling technologies for Germany: •  if elder citizens can stay for three month

longer in their homes, we will save 315 million € / year


on HET: opportunities •  in our adolescenting/ageing societies, advanced

HET may permit active, self-sufficient and autonomous lifestyles at high quality for increasing numbers of fellow citizens

•  sensor-enhanced health information systems for ambient-assisted living will be critical for early detection and prevention of diseases in the pre-clinical stage, as well as for alleviating chronic diseases


the double circle

Informatics for Health and Social Care. 2008; 33, 77 - 89.


examples


Demonstration

•  ECG and triaxial accelerometer •  long-term monitoring of ECG/HRV with respect to

•  activity intensity •  different activities of daily living (ADL) under real-life conditions

  deduction: physiologic reaction of the cardio-vascular system to physical stress

  long-term changes?


Measuring movement - accelerometers

•  cheap, small, mobile/wearable

standing up walking turning walking sitting down


Long-term activity monitoring

•  N=1, young and healthy •  Device: Sensewear Pro2 (multisensor) •  duration: 6 months •  data: 201.984 minutes, 100.087 annotated

(=1,668 hrs.) •  28 activities, no preselection •  activity classification with pattern recognition

algorithms OneR, Naive Bayes, C4.5


Long-term activity monitoring - results

activity classification accuracy for data sets

classifier 9s 21s 60s OneR 78.0% 71.9% 69.2%

Naive Bayes 81.1% 80.5% 80.6% C4.5 94.7% 92.4% 90.0%

Marschollek et al., 2006


class number of instances

classification accuracy

sleeping while supine 286,732 99.3%

working at the computer 155,763 95.6%

watching TV 73,958 95.0%

working at a constr. site 10,872 95.0%

dancing 1,176 94.9%

walking 24,824 94.3%

sleeping while sitting 303 93.7%

party 2,564 92.7%

driving a vehicle 624 90.4%

watching movie at a cinema 1,581 90.3%

lying down 25,420 90.1%

teaching 4,732 89.0%

packing and moving 1,544 88.4%

class number of instances

classification accuracy

gardening 1,973 85.4%

standing still 1,073 85.3%

telephoning 2,212 84.6%

eating 14,628 84.6%

household cleaning 2,389 83.3%

folding laundry 149 80.5%

meeting 22,727 80.3%

going to the bathroom 3,118 79.8%

working at the office 8,998 79.6%

sitting 7,666 75.5%

waiting at traffic light 336 73.8%

shopping 1,027 71.5%

reading 2,535 69.1%

traveling in a vehicle 1,546 64.0%

Activities


Activity profiles of elderly persons

•  N=5, ∅ 67 years •  Device: Sensewear Pro2 •  data: 69.808 minutes •  17 activities (with >100min.) •  activity classification with pattern recognition

algorithm C4.5 •  evaluation:

•  10x 10-fold cross-validation (intraindividual) •  „leave-one-out“ cross-validation (interindividual)


Activity profiles – results

person no. classification accuracy

1 93.1%

2 90.9%

3 78.6%

4 99.2%

5 95.4%

mean 91.4%

intraindividual classification interindividual classification

person no. classification accuracy

1 32.8%

2 52.3%

3 27.8%

4 87.9%

5 67.8%

mean 53.7%

SmarTel Best Paper Award Marschollek et al., 2007


Activities in detail activity number of instances mean classification accuracy

sleeping 16872 98.4%

riding a bike 1265 95.6%

watching TV 4199 89.3%

physiotherapy 114 86.8%

taking a walk 135 83.4%

reading 1537 82.6%

preparing meal 620 80.2%

shopping 633 79.9%

telephoning 413 76.5%

eating 1421 76.3%

plant care 553 74.3%

personal hygiene 465 72.3%

doing one’s laundry 130 70.6%

sewing 237 65.4%

ironing 174 60.1%

writing 345 46.1%

inhaling 196 37.8%


Detecting falls

•  enabling fall alarms without patient interaction •  many lab experiments, very few studies,

reliability?


Predicting falls

•  Can we measure individual fall risk automatically by analyzing movement/gait patterns?

•  cohort study, N=50, duration: 1 year •  aim: prediction of falls at home •  results: ∼80% classification accuracy •  „activity“ most important parameter •  further studies necessary


„Smart Homes“

•  heart rate •  blood pressure •  oxygen saturation •  breath rate, peak flow •  ECG •  body temperature •  serum glucose •  weight, body fat/composition •  NO •  …

  activity profiles   gait parameters   sleeping pattern   …

  home appliance usage: o  shower, toilet o  oven/ refridgerator o  rooms o  …


Feedback and motivation – participation


health-enabling technologiesand medical informatics


HET and medical informatics •  research, education and practise of medical

informatics has always changes and will need to change!

•  What are the consequences with respect to HET?


HET and medical informatics •  first we need to ask if we want to haveHET as a

part of medical informatics •  consequences

•  for research … •  for education … •  for health care practice …


Information and communication technologies for promoting and sustaining quality of life, health

and self-sufficiency in the second half of life

Lower Saxony Research Network Design of Environments for Aging

example for HET research: GAL


example for HET research: GAL

•  GAL objective: quality of life in the ageing society •  independence within one’s own residence •  development of systems for assisting elderly

people, relatives and caregivers •  GAL approach: interdisciplinary research

•  synergy of geriatrics, gerontology, economics, informatics, engineering, medicine, nursing

•  development, evaluation and assessment of exemplary assisting systems


example for HET research: GAL GAL: project structure


example for HET education: our HET course at TU Braunschweig for M.Sc. students – lectures & exercises – 6 ECTS credits

1.  introduction: health care of the future 2.  disease patterns and parameters 3.  measurement engineering and sensor data 4.  representation of sensor data 5.  fusion and analysis of biomedical data 6.  application systems for continuous data 7.  sensor-enhanced health information systems 8.  examples 9.  perspectives: new ways of health care


HET and health care practice:

recall the double circle Informatics for Health and Social Care. 2008; 33, 77 - 89.



A pivotal field for future medical informatics research and education?



A pivotal field for future medical informatics research and education!

see you next year in Cape Town at MEDINFO 2010

Documents

Health-enabling technologies for pervasive health care: A ...• GAL objective: quality of life in the ageing society • independence within one’s own residence • development