Wearable Computing Systems for Sensor Based Movement Analysis

Transcription

1 Wearable Computing Systems for Sensor Based Movement Analysis Dominik Schuldhaus & Bjoern Eskofier, Ph.D. Digital Sports Group, Pattern Recognition Lab (CS 5)

2 Introduction

3 The Pattern Recognition Lab Founded professors (full, associate, assistant, emeritus) Prof. Dr.-Ing. Joachim Hornegger Prof. Dr.-Ing. Elmar Nöth Prof. Dr. Björn Eskofier Prof. em. Dr.-Ing. Heinrich Niemann 62 PhD students + 4 PostDocs 12 external PhD students 4 research groups: Medical Image Processing Computer Vision Speech Recognition Digital Sports

4 Digital Sports Group Digital Sports Group Data Mining Biomechanics Physiology Wearable Systems Sensors Algorithms 4

5 Digital Sports Group Data analysis & classification Wearable computing systems Algorithm development: biomechanical signal analysis biomedical signal analysis Embedded system algorithms: sensor data fusion real-time pattern classification Applications in the biomedical and sports world 5

6 Research Environment J. Barth Industry Applications Products Dr. B. Krabbe Digital Sports Group Pattern Recognition Embedded Algorithms Team: 12 PhDs Prof. B. Eskofier Medical Experts Gait Disorders Patient Database Sports Science Biomechanics Measurements PD Dr. J. Klucken Prof. J. Winkler F. Hebenstreit Prof. M. Lochmann 6

7 Digital Sports in Erlangen 7

8 Digital Sports Today

9 Motivation Why do we need wearable computing systems? Traditional analysis time consuming obtrusive no feedback Mobile analysis Because Sports is not performed in a lab! integrated in clothing unobtrusive sensors direct feedback 9

10 milife Research Project Synchronization Communication ANDROID Mobile Sensor Framework milife WebService Web 2.0 Bluetooth ZigBee ANT Internet Apps for Live-Feedback, Updates micoach mihealth miteam mifitness Sensor Integration Human-Machine-Interface (Speech, Display, Vibration, ) Feedback and Usability M.D. Tele Health Coach Team Sports Athlete Social Nets Funding by IuK Bayern and European Fund for Regional Development 10

11 Research Sensor Hardware GP I/O Antenna Wireless: Bluetooth/ Intelligent: TI microcontroller Several sensors: 9DoF kinematic (Accel./Gyro./Mag.) Physiology (ECG, EMG, ) Environmental C2420 Transceiver Dock Bluetooth Reset MSP430 μc Vibration Light (15g) Longliving Accelerometer SD Card Dock Small form factor 11

12 Mon Tues % % Example Applications player statistics Activity recognition patient statistics P1 P2 rest sprint dribble active not active 13

13 angular velocity [deg/s] Example Applications GaitRecorder foot kinematics for walking / running Sensor vs. Ground Truth correlation time [s] Kugler, P.; Schlarb, H.; Blinn, J.; Picard, A.; Eskofier, B.: A Wireless Trigger for Synchronization of Wearable Sensors to External Systems during Recording of Human Gait. In: Proc. of the Int. Conf. of the IEEE EMBS (EMBC2012). 14

14 ADC value Example Applications Outdoor Running kinematics & physiology 2600 ECG lead I time [ms] Schuldhaus, D.; Kugler, P.; [ ]; Eskofier, B.: Classification of Surfaces and Inclinations During Outdoor Running Using Shoe-Mounted Inertial Sensors. In: Proc. of the Int. Conf. on Pattern Recognition (ICPR2012). 15

15 angle [deg] Example Applications SquadFeeder live feedback of knee joint angle x 1 Sensors x 2 Sensor Video correlation 0.96 time [s] Kugler, P.; Jensen, U.; Eskofier, B.; Hornegger, J.: Feedback-Training mit tragbaren Sensor-Netzwerken. In: Proc. der 40. Jahrestagung der Gesellschaft für Informatik (INFORMATIK 2010). 16

16 Example Studies Joint angle estimation using inertial measurement units accelerometer + gyroscope + + magnetometer sensor alignment procedure + + extended Kalman filter flexion/extension angle estimation of a 3D joint Jakob, C.; Kugler, P.; [ ]; Lochmann, M.; Eskofier, B.: Estimation of the Knee Flexion-Extension Angle During Dynamic Motions Using Body-worn Inertial Sensors. Submitted to: BodyNets Conf

17 Example Applications MoGoPuCo mobile golf putting coach Jensen, U.; Dassler, F.; Eskofier, B.: Classification of Kinematic Golf Putt Data with Emphasis on feature selection. In: Proc. of the Int. Conf. on Pattern Recognition (ICPR2012). 18

18 Example Studies Swim Trainer Swim style detection & time feedback accelerometer + gyroscope Classification of swim style + + detection & timing of lanes Potential integration in swimming goggle feedback device Jensen, U.; Prade, F.; Eskofier, B.: Classification of Kinematic Swimming Data with Emphasis on Resource Consumption. In: Proc. of the IEEE Body Sensor Networks Conf. (BSN2013). 19

19 Example Applications egait embedded gait analysis using IT Analysis of movement impairment Sensor based, mobile, objective Classification of Parkinson s disease Complementary diagnostic tool Klucken, J.; Barth, J.; [ ]; Eskofier, B.; Winkler, J.: Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease. PLOS ONE, accepted for publication,

20 Example Applications Hearty realtime ECG analysis & arrythmia detection HR features ECG signal classifcation QRS detection HRV Gradl, S.; Kugler, P.; Lohmüller, C.; Eskofier, B.: Real-time ECG monitoring and arrhythmia detection using Androidbased mobile devices. In: Proc. of the Int. Conf. of the IEEE EMBS (EMBC2012). 21

21 Quo vadis?

22 Sensors in Abundance Examples: micoach Heart Rate Sensor micoach Foot Pod micoach Pacer / Zone micoach iphone App 23

23 Summary / Outlook Wearable computing systems allow future applications in sports and medical engineering milife WebService Web 2.0 Internet micoach mihealth miteam Apps for Live-Feedback, Updates mifitness Outlook Improvement of sensors and algorithms for future applications Databases for longitudinal movement M.D. & physiological Coach data Athlete Screening for early signs of injuries or movement impairments 24

24 Thank You!