Ubiquitous Health:
Wearable Computing Systems that Promote
Healthy Living and Transform Health Care
Prof. Bjoern Eskofier,...
Introduction
The Medical Valley of Germany
3
Digital Sports in Erlangen
4
Digital Sports Group
Digital Sports Group
Data Mining
Biomechanics
Physiology
Wearable Systems
Sensors
Algorithms
5
Sports...
Research Environment
6
Sports ScienceMedical Experts
Industry
Dr. B. Krabbe
Prof. M. LochmannProf. J. Klucken
Digital Spor...
Wearable Computing Systems
Origins – adidas_1 (2008)
8
Eskofier et al.: Embedded Surface Classification in Digital Sports. Pat Rec Let 30(16), 2009
Internet
Human-Machine-Interface
(Speech, Display, Vibration,…)
M.D. AthleteCoach
Apps for Live-Feedback,
Updates miFitnes...
Wearable Computing Results
10
Running$Analysis$
Schuldh.$et$al.,$2012$
Synchroniza<on$
Kugler$et$al.,$2012$
Research$Senso...
Gradl, S.; Kugler, P.; Lohmüller, C.; Eskofier, B.: Real-time ECG monitoring and arrhythmia detection using Android-
based...
The FitnessSHIRT
12
H Leutheuser, [...], BM Eskofier.
Textile Integrated Wearable Technologies for Sports and Medical Appl...
Smart shoes reach the clinic:
Wearable sensor-based instrumented gait analysis
in Parkinson’s disease
Movement Disorders
0
10.000
20.000
30.000
40.000
50.000
60.000
2002 2004 2006 2008
21.559 22.293 23.609 24.780
18.677 19.4...
The patient view
‘Just imagine what
we could achieve if
we start working
together – as
equals with
different but
complemen...
Care scenario
SymptomSeverity
Disease Progression
DiagnosKcs'
Therapy'
D TD TD TD TD
Chronic Disease
DiagnosKcs'
Therapy'
...
PDXNurse$Pa<ent$
Physician'
Expert'
Physician'MDU*'
*Movement Disorder Unit
Sectors'of'
Care'
Care scenario
Chronic Diseas...
$
$
„Pa#ent'needs“$
Technically$&$Medically$Validated$Technology$ NursePatient
Care Scenario – Clin. Application
IT'PlaUor...
Embedded Gait Analysis using Information Technology
Specific focus on Parkinson‘s Disease
Funding source
Bavarian Research...
1000 PD-Specific Datasets
eGaIT shoes
IMMU sensors
Movement exercises
Clinical routine assessment
Barth, [...], Eskofier; ...
Analysis Paradigm
Movement recording
Robust Stride Segmentation Barth, [...], Klucken*, Eskofier*; EMBC 2013 & Sensors 201...
Signal-processing-driven Stride Parameter Calculation
Rampp, Barth [...], Klucken, Eskofier; TBME 62(4), 2015
IMU Data
Acc...
Timed-Up & Go Instrumentation
Angular velocity [°/s]
First TurnWalking
Second Turn Turn-to-SitWalking
Sit-to-Walk
Time [s]
TUG-Phases in PD patients
n = 265 PD patients, * ANOVA (0.05), post-hoc Bonferroni. Mean time (+/- SEM)
*
*
*
*
Results of...
C I II III C 1 2 3 C L M H
Minimum foot
clearance
Monocenter IIT
193 PD patients
145 controls
C I II III C 1 2 3 C L M H
S...
Gait parameter changes in PD
Longitudinal measurement – intra-individual
Long term monitoring
Stride length Stance phase S...
Need To Go Ambulatory
Stationary lab systems Mobile sensor systems
Non-natural scenario
Limited subject numbers
Home and e...
8 hours of unsupervised gait of PD patients
Unsupervised Gait Analysis
Single'strides'&'
Individual'raKngs'
Gait'signature...
Unsupervised Gait Analysis
ON OFF INTERMED.Motor
Fluctuations
Gait parameters
Stride length (cm)
Freezing
Gait Pattern
Day...
Transforming Healthcare
New reimbursement paradigm:
•  At present: reimbursement per prescription & treatment
•  In future...
Digital Biobank
Biobank of individual
signatures from a diversity
of movement disorders:
• Neurologic: Parkinson, …
• Musc...
EU Data Platform?
Comprehensive Center for Movement Medicine
Physician / Patient
Pharma / Industry
Database
Provide Data
C...
EIT Health
33
Our Vision:
EIT Health is a catalyst for change.
Our community creates novel
solutions that make
healthy liv...
EIT Health – Partners
Menno$Kok$
Interim$CLC$Director$
Belgium/Netherlands$
CLC'UK/Ireland'
CLC'France'CLC'Spain'
CLC'Belg...
Future Synergies
Fitness'and'sport'
Disease'and'early'
detecKon'
Chronic'disease'
Morbidity
Mortality'
35
36
Fall 2015
Summer 2014
Digital Sports Group
See$you!$
Thank You!
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Bjoern Eskofier: Keynote at DSAI & TISHW 2016 Conference

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Ubiquitous Health:
Wearable Computing Systems that Promote Healthy Living and Transform Health Care
The fast-growing costs of acute care are pushing the healthcare systems worldwide to a limit. Globally, we are coming to realize that we cannot afford to provide everybody with access to unlimited healthcare services in the light of current demographic changes. An alternative approach is emerging that focuses on “keeping people healthy” through primary and secondary prevention in all phases of life. This paradigm shift in the healthcare systems is demanding research in ambient, sensor-enhanced assistive technologies that “keep people outside of the hospital”. Therefore, a fast-growing interest exists for wearable and pervasive computing systems and ambient assistive technology that aim at ubiquitous health promotion for individuals in the home and community settings.
The talk will present several examples for associated research projects in the fields of sports, health, and medicine. A particular example is the miLife research project (Fig. 1). In this project, we i) implemented ambient sensors for physiological (ECG, EMG, ...) and biomechanical (accelerometer, gyroscope, ...) data recording, ii) used pervasive computing systems (e.g. in smartphones or smarthomes) for monitoring and signal processing, and iii) employed data base technology, machine learning algorithms, and simulation models in order to provide accurate information to sportsmen, patients, and caregivers in numerous applications that aimed at promoting healthy living and improving health care.
The talk will also present further research challenges that exist in the field of wearable and pervasive computing systems for ubiquitous health support. Example challenges are the required signal processing and machine learning algorithms that need to be computationally efficient yet sufficiently accurate, but also comprehensive databases, simulative data analysis and holistic data mining strategies. The outlook of the presentation will focus on future research directions that aim at contributing to the above mentioned paradigm shift in global healthcare systems by the use of wearable and pervasive computing systems for ubiquitous health support.

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Bjoern Eskofier: Keynote at DSAI & TISHW 2016 Conference

  1. 1. Ubiquitous Health: Wearable Computing Systems that Promote Healthy Living and Transform Health Care Prof. Bjoern Eskofier, PhD Endowed Professorship of the adidas AG Digital Sports & Health Lab December 1, 2016
  2. 2. Introduction
  3. 3. The Medical Valley of Germany 3
  4. 4. Digital Sports in Erlangen 4
  5. 5. Digital Sports Group Digital Sports Group Data Mining Biomechanics Physiology Wearable Systems Sensors Algorithms 5 Sports Applications Biomedical Applications “… to increase human health …”
  6. 6. Research Environment 6 Sports ScienceMedical Experts Industry Dr. B. Krabbe Prof. M. LochmannProf. J. Klucken Digital Sports Group Prof. B. Eskofier Team: 14 PhDs / 1 PDoc Hi!$
  7. 7. Wearable Computing Systems
  8. 8. Origins – adidas_1 (2008) 8 Eskofier et al.: Embedded Surface Classification in Digital Sports. Pat Rec Let 30(16), 2009
  9. 9. Internet Human-Machine-Interface (Speech, Display, Vibration,…) M.D. AthleteCoach Apps for Live-Feedback, Updates miFitness miTeam Web 2.0 miHealth miCoachBluetooth ZigBee ANT ANDROID Mobile Sensor Framework ASTRUM miLife WebService Feedback, Monitoring and Social NetworkingFeedback Training Sensor Integration Synchronization Communication Volume'2'280'000'€' ' '' European'Fund'for'Reg.'Devt.' Follow;up'project'(2015;2018):' “Urban'Sports”,'1'558'000'€'' miLife Research Project: 2011 9
  10. 10. Wearable Computing Results 10 Running$Analysis$ Schuldh.$et$al.,$2012$ Synchroniza<on$ Kugler$et$al.,$2012$ Research$Sensor$ Blank$et$al.,$2014$ Golf$PuDng$ Jensen$et$al.,$2015$ Swimming$Classifi.$ Jensen$et$al.,$2016$ ECG$Classifica<on$ Gradl$et$al.,$2012$ Sleep$Monitoring$ Gradl$et$al.,$2013$ eGaIT$System$ Rampp$et$al.,$2015$ Nykturia$Monit.$ Huppert$et$al.,$2015$ Wearable$ECG$ Richer$et$al.,$2016$ Cycling$System$ Richer$et$al.,$2015$ Skateboard$Classif.$ Groh$et$al.,$2016$ Beach$Volleyball$ Kautz$et$al.,$2016$ Ski$Jumping$ Groh$et$al.,$2016$ Soccer$System$ Zhou$et$al.,$2016$
  11. 11. Gradl, S.; Kugler, P.; Lohmüller, C.; Eskofier, B.: Real-time ECG monitoring and arrhythmia detection using Android- based mobile devices. In: Proc. of the Int. Conf. of the IEEE EMBS (EMBC2012). Elgendi, M.; Eskofier, B.; Dokos, S. Abbott, D.: Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems. PLoS ONE 9(1), e84018, 2014. HRV QRS detection ECG signal classifcation HR features Hearty – realtime ECG analysis & arrythmia detection Biosignal Analysis 11
  12. 12. The FitnessSHIRT 12 H Leutheuser, [...], BM Eskofier. Textile Integrated Wearable Technologies for Sports and Medical Applications. Springer, Berlin, Germany, 2016
  13. 13. Smart shoes reach the clinic: Wearable sensor-based instrumented gait analysis in Parkinson’s disease
  14. 14. Movement Disorders 0 10.000 20.000 30.000 40.000 50.000 60.000 2002 2004 2006 2008 21.559 22.293 23.609 24.780 18.677 19.411 20.481 22.546 6.508 6.612 6.541 6.577 Cost of Movement Disorders (Mio Euro/ Year) weitere ambulant stationär Federal statistical office, Germany 14
  15. 15. The patient view ‘Just imagine what we could achieve if we start working together – as equals with different but complementary areas of expertise!’ 15
  16. 16. Care scenario SymptomSeverity Disease Progression DiagnosKcs' Therapy' D TD TD TD TD Chronic Disease DiagnosKcs' Therapy' Acute Illness Incomplete Remission Complete Remission 16
  17. 17. PDXNurse$Pa<ent$ Physician' Expert' Physician'MDU*' *Movement Disorder Unit Sectors'of' Care' Care scenario Chronic Disease: Parkinson Syndrome Telemedicine' Medical' InformaKon' Medical'' Technology' IT'PlaUorm'' CommunicaKon' Individualised'PaKent'History' GxP,$Data$safety,$Privacy,$Security,$etc.$ 17
  18. 18. $ $ „Pa#ent'needs“$ Technically$&$Medically$Validated$Technology$ NursePatient Care Scenario – Clin. Application IT'PlaUorm'' CommunicaKon' Individualised'PaKent'History' GxP,$Data$safety,$Privacy,$Security,$etc.$ Technology' EMGECG, Respiration, Temperature Instrumented Gait Analysis Video based Diagnostics Activity 18
  19. 19. Embedded Gait Analysis using Information Technology Specific focus on Parkinson‘s Disease Funding source Bavarian Research Foundation Volume 878 000 € New funding source FAU Emerging Fields Project Volume 860 000 € eGaIT Research Project: 2011 19
  20. 20. 1000 PD-Specific Datasets eGaIT shoes IMMU sensors Movement exercises Clinical routine assessment Barth, [...], Eskofier; EMBC 2011 / Klucken, Barth, [...], Eskofier, Winkler; PLoS ONE 8(2), 2013 25.03.2015: Bayerischer Innovationspreis Gesundheitstelematik 2015 für eGAIT 22.10.2014: Erlanger Medizintechnikpreis 2014 (Kategorie Versorgung) für eGAIT 20
  21. 21. Analysis Paradigm Movement recording Robust Stride Segmentation Barth, [...], Klucken*, Eskofier*; EMBC 2013 & Sensors 2015 Stride Signatures Machine Learning: Waveshape Klucken, [...], Eskofier, Winkler; PLoS ONE 8(2), 2013 Stride Parameters Signal Analysis: Biomechanics Rampp, Barth [...], Klucken, Eskofier; TBME 2015 × TO + HS ! MS 21
  22. 22. Signal-processing-driven Stride Parameter Calculation Rampp, Barth [...], Klucken, Eskofier; TBME 62(4), 2015 IMU Data Accelerometer Gyroscope Normalization Calibration Invert Axes Stride Segmentation msDTW Gait Event Detection Mid Stance (MS) Heel Strike (HS) Toe Off (TO) Spatial Gait Parameters Orientation Estimation (MS to MS) Gravity Cancellation Zero Velocity Update Angle Course De-Drifted Integration Distance Estimation Sensor Clearance Estimation (SC) Sensor-Toe- Distance Estimation Stride Length Angle Heel Strike Angle Toe Off Temporal Gait Parameters Stride Time Stance Time Swing Time Time HS to HS Time HS to TO Time TO to HS Max Toe Clearance Toe Clearance Estimation
 Angle Dependent Correction of SC Stride Parameters 22
  23. 23. Timed-Up & Go Instrumentation Angular velocity [°/s] First TurnWalking Second Turn Turn-to-SitWalking Sit-to-Walk Time [s]
  24. 24. TUG-Phases in PD patients n = 265 PD patients, * ANOVA (0.05), post-hoc Bonferroni. Mean time (+/- SEM) * * * * Results of the analysis Reinfelder, S.; […]; Klucken, J.; Eskofier, B.: Timed Up-and-Go Phase Segmentation in Parkinson's Disease Patients using Unobtrusive Inertial Sensors. EMBC 2015. 24 This$is$great,$but…$
  25. 25. C I II III C 1 2 3 C L M H Minimum foot clearance Monocenter IIT 193 PD patients 145 controls C I II III C 1 2 3 C L M H Stride length Gait parameter changes in PD Schlachetzki, J.; […]; Eskofier, B.; Klucken, J.: Smart shoes reach the clinic: Wearable Sensor-based Instrumented gait analysis in Parkinson’s disease. Lancet Neurol, under review, 2015. H&Y UPDRS-GAIT UPDRS-III 25
  26. 26. Gait parameter changes in PD Longitudinal measurement – intra-individual Long term monitoring Stride length Stance phase Swing phase UPDRS-III Change at follow-up visit Schlachetzki, J.; […]; Eskofier, B.; Klucken, J.: Smart shoes reach the clinic: Wearable Sensor-based Instrumented gait analysis in Parkinson’s disease. Lancet Neurol, under review, 2015. 26 This$is$fantas<c!$
  27. 27. Need To Go Ambulatory Stationary lab systems Mobile sensor systems Non-natural scenario Limited subject numbers Home and everyday life Big Data! 27 Espay, A.; [...]; Klucken, J.; Eskofier, B.; [...]; Papapetropoulos, S.: Technology in Parkinson disease: Challenges and Opportunities. Submitted to Movement Disorders 12/2015. On behalf of the MDS Taskforce on Technology. Pasluosta, C.; Gassner, H.; Winkler, J.; Klucken, J.; Eskofier, B.: An Emerging Era in the Management of Parkinson’s disease: Wearable Technologies and the Internet of Things. IEEE J Biomed Health Inform 19(6), 1873-1881, 2015.
  28. 28. 8 hours of unsupervised gait of PD patients Unsupervised Gait Analysis Single'strides'&' Individual'raKngs' Gait'signatures'&' Gait'parameters' Daytime [hour] Reinfelder, Marxreiter, Klucken*, Eskofier*; Unpublished, in preparation for TBME 28 Time Sync Sensor Data Patient Rating
  29. 29. Unsupervised Gait Analysis ON OFF INTERMED.Motor Fluctuations Gait parameters Stride length (cm) Freezing Gait Pattern Daytime [hour] 29
  30. 30. Transforming Healthcare New reimbursement paradigm: •  At present: reimbursement per prescription & treatment •  In future: reimbursement per objectively measured treatment success? New chronic disease management concepts: •  Present concept: •  Future concept: 6 months 6 months variable, dep. on needs variable 30
  31. 31. Digital Biobank Biobank of individual signatures from a diversity of movement disorders: • Neurologic: Parkinson, … • Musculoskeletal: OA, ... Signatures consist of: • Inertial sensor data • Biomechanical data • Imaging data • Clinical scales 31
  32. 32. EU Data Platform? Comprehensive Center for Movement Medicine Physician / Patient Pharma / Industry Database Provide Data Controls Access Engage Organize 32
  33. 33. EIT Health 33 Our Vision: EIT Health is a catalyst for change. Our community creates novel solutions that make healthy lives a reality for all. Funding by EU: 2 billion / 10 years
  34. 34. EIT Health – Partners Menno$Kok$ Interim$CLC$Director$ Belgium/Netherlands$ CLC'UK/Ireland' CLC'France'CLC'Spain' CLC'Belgium/Netherlands' InnoStars' CLC'Germany' CLC'Scandinavia' 34
  35. 35. Future Synergies Fitness'and'sport' Disease'and'early' detecKon' Chronic'disease' Morbidity Mortality' 35
  36. 36. 36 Fall 2015 Summer 2014 Digital Sports Group See$you!$
  37. 37. Thank You!

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