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Evaluating the effects of signal segmentation on activity recognition

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On-body activity recognition systems are becoming more and more frequent in people's lives. These systems normally register body motion signals through small sensors that are placed on the user. To perform the activity detection the signals must be adequately partitioned, however, no clear consensus exists on how this should be done. More speci cally, considered the sliding window technique the most widely used approach for segmentation, it is unclear which window size must be applied. This presentation investigates the e ffects of the windowing procedure on the activity recognition process. To that end, diverse recognition systems are tested for several window sizes also including the fi gures used in previous works. From the study it may be concluded that reduced window sizes lead to a better recognition of the activities, which goes against the generalized idea of using long data windows.

This presentation illustrates part of the work described in the following articles:

* Banos, O., Galvez, J. M., Damas, M., Pomares, H., Rojas, I. Window size impact in activity recognition. Sensors, vol. 14, no. 4, pp. 6474-6499 (2014)
* Banos, O., Galvez, J. M., Damas, M., Pomares, H., Rojas, I.: Evaluating the effects of signal segmentation on activity recognition. In: Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2014), Granada, Spain, April 7-9, (2014)

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Evaluating the effects of signal segmentation on activity recognition

  1. 1. Evaluating the effects of signal segmentation on activity recognition IWBBIO 2014, Granada (España) Oresti Baños, J.M. Gálvez, M. Damas, A. Guillén, L. J. Herrera, H. Pomares, and I. Rojas Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies of the University of Granada (CITIC-UGR), SPAIN oresti@ugr.es
  2. 2. Introduction • Activity recognition concept – “Recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions” • Applications (among others) – eHealth (AAL, telerehabilation) – Sports (performance improvement, injury-free pose) – Industrial (assembly tasks, avoidance of risk situations) – Gaming (Kinect, Wii Mote, PlayStationMove) • Categorization by sensor modality – Ambient (cameras, microphones, RFID) – On-body (wearables) 2
  3. 3. Activity Recognition Chain (ARC) 3
  4. 4. Activity Recognition Chain (ARC) 4
  5. 5. Activity Recognition Chain (ARC) 5
  6. 6. Activity Recognition Chain (ARC) 6
  7. 7. Activity Recognition Chain (ARC) 7
  8. 8. Activity Recognition Chain (ARC) 8
  9. 9. Activity Recognition Chain (ARC) 9
  10. 10. Activity Recognition Chain (ARC) 10
  11. 11. Segmentation • Types 11 – Simplest (no preprocessing) – Most widely-used – Window sizes (WS) ranges from 0.1s to 15s and more Sliding window Event-basedActivity-defined – Analysis of activity changes (spotting) – Limitedly used – Identification of characteristic events – Mainly used in gait analysis
  12. 12. Segmentation • Types 12 – Simplest (no preprocessing) – Most widely-used – Window sizes (WS) ranges from 0.1s to 15s and more Sliding window Event-basedActivity-defined But… which WS should we use? No consensus!!! A study is lacking!!!
  13. 13. Experimental setup: dataset • Fitness benchmark dataset • 33 activities • 9 IMUs (XSENS)  ACC, GYR, MAG • 17 subjects 13 Baños, O., Toth M. A., Damas, M., Pomares, H., Rojas, I., Amft, O.: A benchmark dataset to evaluate sensor displacement in activity recognition. In: 14th International Conference on Ubiquitous Computing (Ubicomp 2012), Pittsburgh, USA, September 5-8, (2012)
  14. 14. Results 14 • Preprocessing: NO ● Segmentation: 0.25s-7s in steps of 0.25s • Feature extraction: FS1={mean}, FS2={mean,std}, FS3={mean,std,max,min,mcr} • Classification: DT, NB, NCC, KNN ● Evaluation: 10-fold cross-validation, 100 repetitions Experimental Parameters
  15. 15. Conclusions and final remarks • Segmentation is a crucial stage in activity recognition, however, there is no clear consensus on how to partitionate the sensor data stream • Sliding window is the most widely-used segmentation technique, but there is no study that neatly investigates the impact of the window size • We have performed an extensive study for various standard activity recognition models evaluated for a wide range of window sizes and activities • Short windows (1-2s) provide the most accurate detection performance, thus proving that using large window sizes does not necessarily translate into a better recognition performance • An extension of this study* provides designers with a set of practical guidelines for the windowing process and for diverse activity categories and applications * Banos, O., Galvez, J. M., Damas, M., Pomares, H., Rojas, I. Window size impact in activity recognition. SENSORS. (2014) 15
  16. 16. Thank you for your attention. Questions? Oresti Baños Legrán Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies of the University of Granada (CITIC-UGR) Email: oresti@ugr.es Web: http://www.ugr.es/~oresti Phone: +34 958 241 778 Fax: +34 958 248 993 This work was partially supported by the Spanish CICYT Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476 and the FPU Spanish grant AP2009-2244. 16

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