Realizing real time monitoring and controlling bio-signals

Transcription

1 Realizing real time monitoring and controlling bio-signals Daniel Berckmans M3 BIORES Katholieke Universiteit Leuven Belgium Philips Eindhoven April

2 K.U.Leuven DIVISION M3-BIORES Measure, Model and Manage Bioresponses Head: Prof. Daniel Berckmans Coordinators Imperfectly mixed fluids Bioresponses 1 Process output & environment Bioresponses 2 Ir. SEZIN EREN OZCAN Prof. JEAN-MARIE AERTS Dr. Claudia Bahr Dr. STIJN QUANTEN Dr. TADIWOS ZERIHUN DESTA Ir. KRISTIEN VAN LOON Prof. ERIK VRANKEN Ir. PIETER SCHIEPERS Ir. VO TAN THANH Ir. GUIDO DE BRUYNE Ir. OZLEM CANGAR Ir. DRAGANA MILJKOVIC Ir. NILA ALBAN Ir. JORIS LEFEVER Ir. SONG XIANGYU Ir. X Ir. SEBASTIAN DE BOODT Ir. TOON LEROY Ir. X Ir. X Ing. KAREL DE BODT Ir. X Ir. X Ir. JAN DEKOCK Ir. SARA FERRARI Ir. X Secretary ANN VAN GINDERACHTER Sound analysis and bioresponses Dr. VASILEIOS EXADAKTYLOS Ir. MITCHELL SILVA BioRICS M3-BIORES, NV, K.U.LEUVEN Belgium Technical assistance Ing. JEAN-LOUIS LEMAIRE LUDO HAPPAERTS PRIVATE COMPANIES BioRICS NV Ir. KIM BATSELIER Ir. FREDERIK JANSEN Ing. MIHAJLO MILUTINOVIC Ir. X Ir. X 2

3 BioRICS n.v. - K.U. Leuven, LRD ( o 1972): 56 spin-offs e.g. LMS, Ubizin, Thromb-X, : 3

4 Problem Livestock farming in the past Farmer had the time to use audio-visual scoring 4

5 Today however Low price of product High number of animals More welfare problems Less available time per animal 5

6 258 million ton meat/year 7 billion pigs 40 billion chicken Market numbers 6

7 A living organism: Complex 7

8 A living organism: Complex Individual Heartbeat (bpm) Time (s) 8

9 Identical Individually different 9

10 A living organism: Complex Individual Time-Varying Example: Heat production of broiler chickens 17 5 days old days old HEAT PRODUCTION (W/KG) MEASURED MODELLED (1ST ORDER) MODELLED (2ND ORDER) TIME (HOURS) HEAT PRODUCTION (W/KG) Different model order MEASURED MODELLED (1ST ORDER) TIME (HOURS) 10

11 A living organism: Complex Individual Time-Varying Dynamic Living organism = CITD - system Measure Model Manage In an on-line way M3-BIORES 11

12 What happens today in other process control 12

13 General methodology: Conditions for process control Position Steering wheel Process Direction MEASURE 2 PREDICTION 3 MEASURE 1 FEEDBACK Desired direction MODEL-BASED CONTROLLER 13

14 Control of Biological Responses Process MICRO- ENVIRONMENT DYNAMIC BIORESPONSE 2 PREDICTION 1 FEEDBACK 3 MODEL BASED CONTROLLER DESIRED BIORESPONSE 14

15 Examples 15

16 Control of the crawl trajectory of larvae of Calliphora vicina 16

17 Control of the crawl trajectory of larvae of Calliphora vicina Light intensity crawl trajectory 17

18 Response of crawl direction to variations in ligth Light intensity u(t) t crawl trajectory y(t) t MEASURE MEASURE 18

19 Responses of crawl direction to steps in light direction Crawl direction ( ) Time 19

20 Active control of crawl direction of larvae Light intensity crawl trajectory MEASURE MEASURE PREDICTION FEEDBACK MODEL-BASED CONTROLLER 20

21 Active control of crawl direction of 6 larvae 21

22 Control of the growth trajectory of chickens 22

23 Active on-line growth trajectory control Chickens > 2000 g Weight Problems! Leg disorders Ascites Sudden death syndrome 40 g Day 42 Day 1 23

24 Possible solution: Growth restriction + compensatory growth = growth control Weight Growth restriction Compensatory growth Day 1 Day 42 24

25 Control diagram Temperature Light schedule Feed quantity Feed quality Weight trajectory t Prediction Feedback Model Model based controller 25

26 EXP afdeling Gewicht (gram) Weight (grams) Tijd (dagen) Time (days) Controlled Gestuurd groeitraject growth trajectory Reference Referentie growth groeitraject trajectory Growth Groeitraject trajectory controle of control groep group = 3.7% - 6.0% PLF control results in reduction of mortality (12 exp, 2900 animals each) of 4% 26

27 Optimisation of physical training of race horses 27

28 Optimisation of physical training of race horses Running speed Heart rate v VO 2 Heart rate Lactate conc. 28

29 Sensors Polar equine HR monitor Garmin forerunner GPS 29

30 Optimisation of physical training of race horses Velocity and Heart rate - Kyrielle (Bert) velocity (km/u) Velocity Heart rate heart rate (bpm) :00:00 0:05:00 0:10:00 0:15:00 0:20:00 0:25:00 time (u:mm:ss) 30

31 Optimisation of physical training of race horses heart rate (bpm Controlled heart rate Target heart rate 0:00:00 0:10:00 0:20:00 0:30:00 0:40:00 0:50:00 1:00:00 time(h:mm:ss) 31

32 Optimisation of physical training of race horses Time-constant of HR over time TC 50 TC (sec) Week 0 Week 1 Week 2 Week 3 Week 4 Time (date) 32

33 Model based monitoring of physical & mental performance (AC Milan) 33

34 Process Physical training Physical performance Mental training CITD CITD Mental performance Performance Total performance = a. Mental performance + b. Physical performance 34

35 Process Training exercises Performance MEASURE (Inmotio) Body MEASURE (Inmotio + Hosand) 35

36 Mental monitoring Heart Rate Physical Activity Mental algorithm On-line mental monitor Time??? Reference mental status 36

37 Reference mental status MINDROOM 37

38 Demo: mental monitoring 38

39 Mindroom data (1133 and 1156) /03/ /03/2006 3/4/2006 7/4/ /4/ /4/ /4/ /4/2006 1/5/2006 2/5/2006 3/5/2006 4/5/ /5/ /03/ /03/2006 7/4/ /4/2006 Mindroom vs. Mental algorithm Mental status [pos, neg or neutral] Date (ddmmyy) MINDROOM

40 Mindroom vs. Mental algorithm Mental algo vs Mindroom /4/2006 Mental status [pos, neg or neutral] 27/03/ /03/2006 3/4/2006 7/4/ /4/ /4/ /4/ /4/2006 1/5/2006 2/5/2006 3/5/2006 4/5/ /5/ /03/ /03/2006 7/4/2006 Date (ddmmyy) MINDROOM MENTAL ALGO

41 Real-time process control Physical training BioRICS Physical performance Mental training CITD CITD BioRICS Mental performance Performance Total performance = a. Mental performance + b. Physical performance 41

42 Application Examples 42

43 Stressmonitoring USB transceiver BioRICS server Accelerometer ECG Mobile phone 43

44 Monitoring and control of physical performance USB transceiver BioRICS server Accelerometer ECG Mobile phone 44

45 Weight Control Accelerometer ECG Mobile phone USB transceiver BioRICS server 45

46 Driver Sleepiness Pedal + steer input USB transceiver BioRICS server Temperature sensor ECG 46

47 General hardware scheme Sensor Modules ECG module Temp. module Accelerometer module1 Accelerometer module 3 Bidirectional Wireless link 1 2m Central Module PDA or Mobile Bidirectional Wireless link 2 Central Module : Transceiver connected to PDA or mobile (mini-usb) Responsible for synchronization of the signals from the different sensor modules 47

48 Specifications ECG sensor module : sampling freq : 300 Hz Temperature module : sampling freq : 1/60Hz (1 sample/minute) Accelerometer modules : sampling freq : 200 Hz g range : 6g ; 10g ; 25g Bidirectional Wireless link 1 : wireless range : 2m Strongly dependent on application field low power (e.g. recharging after 1 week) Central Module : USB transceiver Synchronization of the different signals form the sensor modules Data transmission via mobile network to BioRICS server 48

49 Complex Algorithms (high level language, e.g. Matlab) Translation Low level language (C) Compiler 49

50 Daphnia applications Mussels Chickens Tubifex 10 Products ESA Race horses Intensive Care Bicycle helmets Professional Cyclists 11 Patents 50

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