Grupo de Procesado de Datos y Simulación                                                         ETSI de Telecomunicación ...
contents                introduction and motivation                architecture details                             cla...
contents                introduction and motivation                architecture details                             cla...
introduction and motivation“Towards a fuzzy-based multi-classifier selection module for activity recognition applications“...
contents                               introduction and motivation                architecture details                 ...
architecture detailson-line stage                                                                                         ...
architecture details          1)      analyse the cost of integrating a set of classifiers to detect user activity in a sm...
contents                               introduction and motivation                architecture details                 ...
architecture details                                                              classifier evaluation module1) analyse t...
architecture details                                                             classifier evaluation module  1) analyse ...
architecture details                                                 classifier evaluation module1) analyse the cost of in...
contents                               introduction and motivation                architecture details                 ...
architecture details                                                                          fuzzy selector module  2) pr...
architecture details                                                                   fuzzy selector module 2) propose a ...
architecture details                                                           fuzzy selector module 2) propose a fuzzy me...
architecture details                                                           fuzzy selector module 2) propose a fuzzy me...
contents                               introduction and motivation                               architecture details ...
system pre-validationreal calculation of classifiers features             •     Android-based Google Nexus S device       ...
contents                               introduction and motivation                               architecture details ...
conclusions and future works • accuracy enhanced when considering the position of   the mobile • accuracy worsens (and siz...
any question?Sensor Networks and Ambient Intelligence – SeNAmI 2012   josue@grpss.ssr.upm.es   21 / 20
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[SeNAmI'12] Towards a fuzzy-based multi-classifier selection module for activity recognition applications

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[SeNAmI'12] Towards a fuzzy-based multi-classifier selection module for activity recognition applications

  1. 1. Grupo de Procesado de Datos y Simulación ETSI de Telecomunicación Universidad Politécnica de MadridTowards a fuzzy-based multi-classifier selection module for activity recognition applications SeNAmI 2012 Henar Martín, Josué Iglesias, Jesús Cano, Ana M. Bernardos, José R. Casar josue@grpss.ssr.upm.es
  2. 2. contents  introduction and motivation  architecture details  classifier evaluation module  fuzzy selector module  system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 2 / 20
  3. 3. contents  introduction and motivation  architecture details  classifier evaluation module  fuzzy selector module  system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 3 / 20
  4. 4. introduction and motivation“Towards a fuzzy-based multi-classifier selection module for activity recognition applications“ why activity recognition? how to perform activity recognition? patient monitoring video processing sport trainers wearable sensors emergency detectors o ad hoc sensors diary builders o personal mobile embedded sensors location systems accelerometers/gyroscopes, compass, camera, microphone, etc. • mainly infrastructure-based network coverage, latency, privacy, etc. what about using smartphones processing capabilities for activity recognition? • their use on a daily basis and • processing capabilities are growing spectacularlyfocus1) analyse the cost of integrating a set of classifiers to detect user activity in a smartphone2) propose a fuzzy method to select the best classifier configuration (in order to save device resources)Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 4 / 20
  5. 5. contents   introduction and motivation  architecture details  classifier evaluation module  fuzzy selector module  system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 5 / 20
  6. 6. architecture detailson-line stage off-line stage Comp. cost memory Position Classifier Activity Classifier Classifier All features or selection fuzzy selection Evaluation mean and variance Decision Tree (J48) Decision Table  accuracy All sensors or  size accelerometer only Position features Activity features  response time Real time computation computation  complexity Sliding windows with or Sensor without overlap back trousers pocket measurements front trousers pocket gathering sit shirt pocket stand Position hand texting Activity walk hand talking classifier waist case classifier slow walk rush walk backpack run jacket pocket long strap bag armband Position Activitya) b) Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 6 / 20
  7. 7. architecture details 1) analyse the cost of integrating a set of classifiers to detect user activity in a smartphone 2) propose a fuzzy method to select the best classifier configurationon-line stage off-line stage 2) Comp. cost memory Position Classifier Activity Classifier Classifier All features or selection fuzzy selection Evaluation mean and variance Decision Tree (J48) Decision Table  accuracy All sensors or  size accelerometer only Position features Activity features  response time Real time computation computation  complexity Sliding windows with or Sensor without overlap back trousers pocket measurements front trousers pocket gathering sit shirt pocket stand Position hand texting Activity walk classifier hand talking waist case classifier slow walk 1) rush walk backpack run jacket pocket long strap bag armband Position Activitya) b) Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 7 / 20
  8. 8. contents   introduction and motivation  architecture details  classifier evaluation module  fuzzy selector module  system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 8 / 20
  9. 9. architecture details classifier evaluation module1) analyse the cost of integrating a set of classifiers to detect user activity in a smartphone sensors features classifiers activitiesembedded sensors time-domainaccelerometer mean sit linear acceleration variance gravity zero crossing rate decision table standmagnetometer percentile 75orientation interquartilegyroscope walk device position frequency-domain fft energy slow walk + light sensor frequency domain entropy + proximity sensor power spectrum centroid decision tree rush walk hand (texting) short/long strap bag hand (talking) trouser pockets backpack shirt/jacket pocket armband waist case signal energy runSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 9 / 20
  10. 10. architecture details classifier evaluation module 1) analyse the cost of integrating a set of classifiers to detect user activity in a smartphoneon-line stage off-line stage Comp. cost memory Position Classifier Activity Classifier Classifier All features or selection fuzzy selection Evaluation mean and variance Decision Tree (J48) Decision Table  accuracy All sensors or  size accelerometer only Position features Activity features  response time Real time computation computation  complexity Sliding windows with or Sensor without overlap back trousers pocket measurements front trousers pocket gathering sit shirt pocket stand Position hand texting Activity walk classifier hand talking waist case classifier slow walk 1) rush walk backpack run jacket pocket long strap bag armband Position Activitya) b) Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 10 / 20
  11. 11. architecture details classifier evaluation module1) analyse the cost of integrating a set of classifiers to detect user activity in a smartphone on-line stage off-line stage Comp. cost memory Activity Classifier Classifier All features or activities fuzzy selection Evaluation mean and variance classifiers  accuracy All sensors or  size accelerometer only Real time features  response time  complexity Sliding windows with or without overlap sensors (~32) classifier configurations classifier featuresSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 11 / 20
  12. 12. contents   introduction and motivation  architecture details   classifier evaluation module  fuzzy selector module  system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 12 / 20
  13. 13. architecture details fuzzy selector module 2) propose a fuzzy method to select the best classifier configurationon-line stage off-line stage 2) Comp. cost memory Position Classifier Activity Classifier Classifier All features or selection fuzzy selection Evaluation mean and variance Decision Tree (J48) Decision Table  accuracy All sensors or  size accelerometer only Position features Activity features  response time Real time computation computation  complexity Sliding windows with or Sensor without overlap back trousers pocket measurements front trousers pocket gathering sit shirt pocket stand Position hand texting Activity walk hand talking classifier waist case classifier slow walk rush walk backpack run jacket pocket long strap bag armband Position Activitya) b) Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 13 / 20
  14. 14. architecture details fuzzy selector module 2) propose a fuzzy method to select the best classifier configurationapplication requirements required classifier 1 accuracy response delay classifier 2 chosendevice context classifier 3 classifier battery level memory available classifier N CPU load classifier evaluation Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 14 / 20
  15. 15. architecture details fuzzy selector module 2) propose a fuzzy method to select the best classifier configurationapplication requirements 2.a) quality trained accuracy required computation module response delay file size accuracy complexity response delay target classifierdevice context 0.91 accuracy battery level 0.83 delay 0.38 size memory available 0.67 complexity CPU load classifier evaluation Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 15 / 20
  16. 16. architecture details fuzzy selector module 2) propose a fuzzy method to select the best classifier configurationapplication requirements 2.a) quality trained accuracy required computation module response delay file size accuracy complexity response delay target classifierdevice context 0.91 accuracy battery level 0.83 delay 0.38 size memory available 0.67 complexity CPU load classifier evaluation 2.b) distance-based classifier selector  Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 16 / 20
  17. 17. contents   introduction and motivation   architecture details   classifier evaluation module   fuzzy selector module  system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 17 / 20
  18. 18. system pre-validationreal calculation of classifiers features • Android-based Google Nexus S device • 16 subjects (6 activities, 11 device positions) • response times ◦ Android’s Traceview Tool • accuracy ◦ WEKA (leave-one-subject-out method)sweeping test• freeMemory = 80%• requiredAccuracy = medium• requiredResponseTime = medium Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 18 / 20
  19. 19. contents   introduction and motivation   architecture details   classifier evaluation module   fuzzy selector module   system pre-validation  conclusions and future worksSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 19 / 20
  20. 20. conclusions and future works • accuracy enhanced when considering the position of the mobile • accuracy worsens (and size reduced) when the accelerometer is the only sensor considered  better approach to determining the complexity of the classifiers  dynamic fuzzy membership functions  real application on top of this systemSensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 20 / 20
  21. 21. any question?Sensor Networks and Ambient Intelligence – SeNAmI 2012 josue@grpss.ssr.upm.es 21 / 20

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