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Novel Method for Feature-set Ranking Applied to Physical Activity Recognition

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The benefits arising from proactive conduct and subject-specialized healthcare have driven e-health and e-monitoring into the forefront of research, in which the recognition of motion, postures and physical exercise is one of the main subjects. We propose here a multidisciplinary method for the recognition of physical activity with the emphasis on feature extraction and selection processes, which are considered to be the most critical stages in identifying the main unknown activity discriminant elements. Efficient feature selection processes are particularly necessary when dealing with huge training datasets in a multidimensional space, where conventional feature selection procedures based on wrapper methods or
‘branch and bound’ are highly expensive in computational terms. We propose an alternative filter method using a feature quality group ranking via a couple of two statistical criteria. Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist.

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

* Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily Living Activity Recognition based on Statistical Feature Quality Group Selection. Expert Systems with Applications, vol. 39, no. 9, pp. 8013-8021 (2012)
* Banos, O., Pomares, H., Rojas, I.: Ambient Living Activity Recognition based on Feature-set Ranking Using Intelligent Systems. In: Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN 2010), IEEE, Barcelona, July 18-23, (2010)

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Novel Method for Feature-set Ranking Applied to Physical Activity Recognition

  1. 1. Novel Method for Feature-set Ranking Applied to Physical Activity Recognition IEA-AIE 2010 Córdoba (SPAIN) O. Baños, H. Pomares, I. Rojas
  2. 2. Health Sector Today • Innovations in Technology and Globalization have transformed health services • Medical interventions have changed from “direct and specific person treatment” to “continuous and spatio-independent interaction” 2 • Acute diseases have evolved to chronic diseases, while World population is becoming older
  3. 3. AmiVital Project • Create an integral and consistent approach for the provision of AmI (Ambient Intelligence) services to citizens, from both a social and health care perspective 3 • Merge concepts from the AmI paradigm and the current framework for health assistance into a more general and integral model of services
  4. 4. Activity Recognition • Fundamental part of medical/health assistant work, being applicable to other areas (sport efficiency, videogames industry, robotics, etc.) • Changeableness due to capability for discovering and identifying actions, movements and gestures than normally are unnoticed • Objectives 4  Define an original methodology  Identify the main characteristics  Improve results in unsupervised monitoring studies
  5. 5. Experimental setup • Five accelerometers Walking Sitting and relaxing Standing still Running 5 • Four activities • Twenty subjects • Two monitoring methodologies
  6. 6. Data preprocessing • Different approximations were studied • Best results “a posteriori” using a LPF+HPF (IIR elliptic) 6 ORIGINAL MEAN FILTERING LPF+HPF
  7. 7. Feature extraction Magnitudes Amplitude Autocorrelation Cepstrum Correlation lags Cross correlation Energy Spectral Density Spectral coherence Spectrum amplitude/phase Histogram Historical data lags Minimum phase reconstruction Wavelet decomposition Statistical functions 4th and 5th central statistical moments Energy Arithmetic/Harmonic/Geometric/ Trimmed mean Entropy Fisher asymmetry coefficient Maximum / Position of Median Minimum / Position of Mode Kurtosis Data range Standard deviation Total harmonic deviation Variance Zero crossing counts 7
  8. 8. 2 2.5 3 3.5 4 Walking Sitting and relaxing Standing still Running Why feature selection is needed? • Influence on classification process OPTIMUM Few Features Good classification 0 500 1000 -1 -0.5 0 0.5 1 x 10 4 Thigh accelerometer Features Featurevalue 8 • Huge feature set (861 parameters  2861  1.5 x 10259 possible combinations)
  9. 9. Feature selection 0 5 10 15 20 25 30 Wavelet coef. (a5) geometric mean Featurevalue Discriminant capacity Robustness Quality group 4 5 1 4 4 2 4 3 3 4 2 4 4 1 5 3 5 6 3 4 7 3 3 8 3 2 9 3 1 10 2 5 11 2 4 12 2 3 13 2 2 14 2 1 15 1 5 16 1 4 17 1 3 18 1 2 19 1 1 20 0 5 21 Overlapping criteria Robustness criteria 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 1 1.5 2 2.5 3 3.5 4 Walking Sitting and relaxing Standing still Running 9
  10. 10. Feature selection 0 0.2 0.4 0.6 0.8 1 0 200 400 600 800 1000 Overlapping Threshold No.DiscriminantFeatures 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 100 200 300 400 500 600 700 800 900 Overlapping Threshold No.DiscriminantFeatures Walking Sitting and relaxing Standing still Running All activities All activities & all accelerometers 10 • Features extracted from the complete signal • Data corresponding to hip accelerometer      thf thf okpifkclassdiscrim.no okpifkclassdiscrim. f )( )( 
  11. 11. Feature selection 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 100 200 300 400 500 600 700 800 900 Overlapping Threshold No.DiscriminantFeatures Walking Sitting and relaxing Standing still Running All activities All activities & all accelerometers 0 0.2 0.4 0.6 0.8 1 0 200 400 600 800 1000 Overlapping Threshold No.DicriminantFeatures 11 • Features extraction based on a windowing method • Data corresponding to hip accelerometer      thf thf okpifkclassdiscrim.no okpifkclassdiscrim. f )( )( 
  12. 12. Classification (SVM) 12 • Fast • Simple solutions • Good precedents • Binary multiclass models based on • Different kernels (linear, quadratic, RBF, MPL, etc.)
  13. 13. Classification (SVM) 13 • Fast • Simple solutions • Good precedents • Binary multiclass models based on • Different kernels (linear, quadratic, RBF, MPL, etc.)
  14. 14. Classification (DT) 14 • Very fast • Easy interpretability • Entropy related
  15. 15. Test 15 • Cross validation ▫ Leave-one-subject-out ▫ 50% training – 50% test SVM DT LAB 96.37 ± 4.58 98.92 ± 1.08 SEM 75.81 ± 0.90 95.05 ± 1.20 Mean (%) ± standard deviation (%)
  16. 16. Comparison with other studies 16 Work Accuracy rates S.W. Lee and K. Mase. Activity and location recognition using wearable sensors. 92.85% a 95.91% J. Mantyjarvi, J. Himberg, and T. Seppanen. Recognizing human motion with multiple acceleration sensors. 83% a 90% K. Aminian, P. Robert, E. E. Buchser, B. Rutschmann, D. Hayoz, and M. Depairon. Physical activity monitoring based on accelerometry: validation and comparison with video observation. 89.30% L. Bao and S.S. Intille. Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions 89% THIS WORK 95.05% (SEM), 98.92(LAB) Source: L. Bao and S.S. Intille. Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions
  17. 17. Conclusion • Only a source of data (accelerometer ) is necessary for inferring the considered activities • Best results (≈ 100%) for laboratory data: • Seminaturalistic accuracy rates are highly improved with respect to prior works (≈ 95%) 17 Filtering Feature extraction over the complete signal Features selected: coef. wavelets, autocorrelación or amplitude geometric mean Classification based on DT
  18. 18. Future work • Analyze other methods and compare with the presented work • Study other activities and apply this methodology to other kind of problems • Define new approaches for other physiological parameters (ECG, PPG, body temperature,…) 18
  19. 19. Thank you for your attention Questions? 19

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