PREDICTIVE MAINTENANCE PARADIGMS IN BIOMEDICINE

Transcription

1 NSF I-UCRC on Intelligent Maintenance Systems; Planning Workshop at the University of Texas; May 15, 2012, Austin, TX PREDICTIVE MAINTENANCE PARADIGMS IN BIOMEDICINE PROF. DRAGAN DJURDJANOVIC PROF. JONATHAN DINGWELL

2 2 PRESENTATION OUTLINE» Motivation» Methodology» Preliminary Work» Research Plan NSF I-UCRC on Intelligent Maintenance Sys. Planning Workshop at the University of Texas

3 3 MONITORING PARADIGMS IN ENGINEERING AND BIOMEDICINE Symptomatic versus system-based Output Inputs Output monitoring System System Utility: Clinical review Tele-operated robots Rehabilitation Prosthetics Challenges: Incomplete system information Inaccessibility of input Indirect measure of output Nonlinear input-output relationship wp=gc39084

4 4 NEURO-MUSCULO-SKELETAL (NMS) SYSTEM Jq + B(q,q)q + K(q,q)q = T ext + å h r h (q) F h (q,q,emg, f (EMG)) Muscles are activated by neurons via a electro-chemical control mechanism The electrical portion of the control is measurable (EMG) The effect of muscle activation on the joint is also measureable (joint torque) Thus, the NMS system is a prime candidate for input-output modeling and system based monitoring of performance

5 5 PRESENTATION OUTLINE» Motivation» Methodology» Preliminary Work» Research Plan

6 6 NEED FOR COHEN S CLASS TFD-S 1000 Fre qu enc y 500 (Hz ) Multicomponent, Nonstationary Signal Demonstration Time (s)

7 7 CHALLENGE OF NON-STATIONARY SIGNALS IN BIOMEDICINE Data from / 150 EC G (m V) Fre qu enc y 62 (Hz ) Time (s) wp=gc39084

8 8 Data courtesy of Arian Vistamehr of Dr. Rick Neptune s Lab CHALLENGE OF NON-STATIONARY SIGNALS IN BIOMEDICINE 250 EM G (µv ) Fre qu enc y 150 (Hz ) 0 10 Time (s) wp=gc

9 9 DYNAMIC SYSTEM MODELING AND MONITORING USING GROWING STRUCTURE MULTIPLE MODEL SYSTEMS Local model 6 Local model 7 Local model 1 Local model 5 Plant Local model 2 Local model 4 Piecewise Dyn. Model + - Operating Region Current behavior Compute CV Normal behavior Anomalous Behavior 9

10 10 PRESENTATION OUTLINE» Motivation» Methodology» Preliminary Work» Research Plan

11 MODEL BETWEEN EMG AND PEDALING FORCES DURING CYCLING EMG Tibialis Anterior Gastrocnemius Soleus Vastus Lateralus Vastus Lateralus Rectus Femoris Biceps Femoris Gluteus Maximus Force Measurements Vertical pedal force Horizontal pedal force Task Repetitive cycling Light load Data fit with ARX model Autoregressive order 2 Exogenous order for all muscles 0 Delay approximately 50ms

12 12 PRESENTATION OUTLINE» Motivation» Methodology» Preliminary Work» Research Plan

13 FATIGUE RELATED BIOMECHANICAL DATASET EMG Middle trapezius Pectoralis major Deltoids Triceps Biceps Flexor carpi radialis Extensor carpi radialis Joint Kinematics (Angle) Shoulder Elbow Wrist Force Measurements 6 DOF load cell on handle New Challenges Large range of motion for many joints Demonstrable fatigue First session - Sawing motion with 5%-20% MVC to exhaustion Image and Data courtesy of Deanna H Gates and Jonathan B Dingwell [76] Second session: Sawing motion (5%-20% MVC) for 6 minutes Induce shoulder fatigue: Manipulate small objects with arms at 45o Shoulder raises at 20% MVC Sawing motion (5%-20% MVC) for 6 minutes

14 14 RESEARCH TASKS AND PROPOSED TIMELINE OF THE RESEARCH Task 1: Feature extraction from EMGs (months 1-4) Task 2: Identification of the structure and parameters of the GSMMS models (months 3-9) Task 3: Training of normal (rested) NMS system behavior (months 8-10) Task 4: Perform fatigue detection using Confidence Value (CV) concept (months 10-12) Task 5: Analyze models indicative of fatigue (months 12-20) Ø Look for model structure and/or parameter trends Ø Observe interregional switching Ø Observe patterns in EMG features Task 6: Localization of sources of abnormalities (months 18-23) Task 7: Documentation of fatigue related work (months 22-24)

15 15 SUMMARY OF PROPOSED WORK Task 1: EMG Feature extraction Task 2: Identification of the GSMMS structure and parameters Method for modeling the Task 3: Training of normal (rested) NMS system behavior Task 4: Perform fatigue detection using CV concept Task 5: Analyze fatigue models Task 6: Localization of the sources of anomalies Task 7: Documentation relations between EMG signals and joint kinematics/dynamics (Task 2) Model of normal (rested) operation of a selection portion of the NMS system (Task 3) Detection of fatigue based on deviations away from the normal NMS system model (Task 4) Mentors: Prof. Dragan Djurdjanovic, Prof. Jonathan Dingwell and industrial partners Researchers: One graduate student per application area in the NMS system Quantitative characterization of fatigue Ability to localize sources of fatigue and tailor exercise/training procedures to individual athletes Understanding of the NMS system behavior and laying of the foundations for the EMG based actuation (neuron-driven prosthetics) T A S K S YEAR

16 16 BUDGET SUMMARY Year 1 Budget Development of the method for characterization and monitoring of relations between EMG signals and joint kinematics/dynamics $40K (student tuition and stipend for a year) Budget for a second portion of the neuro-musculo-skeletal (NMS) system Additional $40K (student tuition and support for 6 months + experimental costs) Year 2 Budget Model analysis for fatigue $40K (student tuition and stipend for a year) 20K for each additional application area Localization of the source(s) of abnormal NMS system behavior Total: $60K Total: $40-80K

17 THANK YOU