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Placement Variations and their Diagnosis

Placement Variations and their Diagnosis. Waltenegus Dargie TU Dresden Chair of Computer Networks. Outline . Motivation Features Measurements Concluding remarks . Motivation. Use of sensors in mobile environments Healthcare

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Placement Variations and their Diagnosis

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  1. Placement Variations and their Diagnosis Waltenegus Dargie TU Dresden Chair of Computer Networks

  2. Outline • Motivation • Features • Measurements • Concluding remarks

  3. Motivation • Use of sensors in mobile environments • Healthcare • Activity monitoring (patients, nurses, elderly people, children) • Supply chain • Transportation

  4. Motivation • The effect of sensor placement on the quality of sensed data • The acceleration of human movements varies from part to part of human body. • Acceleration in vertical direction and horizontal direction increase in magnitude from cranial (skull) to caudal (tail or posterior part of the body) body parts (Bao and Intille, 2004; Bouten et al., 2002)

  5. Motivation • Not only the acceleration component of whole body, but also the partial acceleration component should be considered; for example, for hopping, measurements values from ankle are higher than value from thigh in magnitude and the measurements from arm and thigh are different. • On the other hand, sensors do not stay on their original place of deployment or might have not been deployed at “the right place” in the first place.

  6. Features • For orientation error, the simple solution is to use the absolute acceleration

  7. Features Mean-value crossing rate Correlation coefficients Auto/Cross correlation

  8. Measurements

  9. Measurements • Sampling rate: 100 Hz • Frame length: 1.5s • Frame overlap: 25% • 6 Placements: thigh-thigh, thigh-knee, thigh-calf, thigh-ankle, thigh-arm, thigh-waist; • 4 Orientations: 0, 30, 45 and 85 • 840 pairs of data sets with total 960s length experiments readings for each movements

  10. Measurements

  11. CC – Upstairs

  12. CC – Upstairs

  13. CC – Down Stairs

  14. CC – Down Stairs

  15. CC – Running

  16. CC – Running

  17. MVC – Upstairs

  18. MVC – Downstairs

  19. MVC – Running

  20. Conclusion • Calibration error has little impact on the quality of features • No one feature is robust against placement variations for all types of movements • Fortunately, the movement types which are distinct acceleration intervals can be detected despite placement and orientation variations.

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