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Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection

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Full paper in https://link.springer.com/chapter/10.1007/978-3-319-46257-8_56

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Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection

  1. 1. Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection Armando Collado María D. R-Moreno David F. Barrero1 Daniel Rodriguez2 1Computer Engineering Departament, Universidad de Alcalá, Spain 2Computer Science Departament, Universidad de Alcalá, Spain IDEAL 2016 Yangzhou, China Oct. 12-14, 2016
  2. 2. Summary 1 Introduction Motivation Anatomy of a fall in elderly Problem statement 2 Data acquisition Syncope falls Forward falls Null class 3 Classifiers trainning Overview Time window length Sample rate Features selection 4 Model evaluation Classifiers comparison Robustness analysis 5 Conclusions and future work
  3. 3. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Motivation Anatomy of a fall in elderly Problem statement Introduction Motivation Fall detection is a revelant issue in elderly care ... consequence of other health issues ... may originate new health problems Related work with two approaches Dedicated devices - Expensive Cell phones - Psychological rejection Our proposal: Smartwatches Rich sensing and communications Programmable IDEAL 2016, Yangzhou, China 3 / 18
  4. 4. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Motivation Anatomy of a fall in elderly Problem statement Introduction Anatomy of a fall in elderly Aging induces physiological changes Trunk moved forward Legs apart, slow feet motion Shifted center of gravity ⇒ Lateral falls highly unusual Types of falls Syncope fall: Loss of control over muscles Forward fall: Trip while walking IDEAL 2016, Yangzhou, China 4 / 18
  5. 5. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Motivation Anatomy of a fall in elderly Problem statement Introduction Problem statement Objective Implement an algorithm able to detect falls with a smartwatch Fall detection as a classification task: Fall or not-fall Data from accelerometer: X, Y and Z Limited computational resources We need data to train classifiers Smartwatch gathering acceleration measures Hardware imposed the sampling period (20ms) Different procedures for each type of fall IDEAL 2016, Yangzhou, China 5 / 18
  6. 6. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Syncope falls Forward falls Null class Data acquisition Syncope falls Syncope falls Nursing mannequin Trainned by experts Expert supervision 42 simulated falls 30 validated falls IDEAL 2016, Yangzhou, China 6 / 18
  7. 7. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Syncope falls Forward falls Null class Data acquisition Forward falls Forward falls Three volunteers Trainned by experts 47 simulated falls 40 validated falls IDEAL 2016, Yangzhou, China 7 / 18
  8. 8. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Syncope falls Forward falls Null class Data adquisition Null class Syncope fall Time (s) Acceleration(m/s^2) −2001020 AccX −2001020 AccY −100102030 1 2 3 4 5 AccZ Forward fall Time (s) Acceleration(ms2 ) −1001020 AccX −10010 AccY −20010 1 2 3 4 5 6 AccZ Not-fall data needed Vertical motion desirable ⇒ Basketball match Accelerations lower than a threshold were removed IDEAL 2016, Yangzhou, China 8 / 18
  9. 9. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Overview Time window length Sample rate Features selection Classifiers tranning Overview (I) Data preprocessing 1 Data clean-up 2 Features creation 3 Time windows construction Three datasets: Syncope, Forward and not-fall Time-window approach IDEAL 2016, Yangzhou, China 9 / 18
  10. 10. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Overview Time window length Sample rate Features selection Classifiers tranning Overview (II) Two binary classifiers Syncope fall / not-fall Forward fall / not-fall Classical classifiers considered: C4.5, 1-NN, Naïve Bayes, PART 10-fold crossvalidation Attribute Label No. of attributes Acceleration X AccelX[X1, ...,xN ] N Acceleration Y AccelY[y1, ...,yN ] N Acceleration Z AccelZ[Z1, ...,zN ] N Mean X, Y and Z MeanX, MeanY, MeanZ 3 Std. deviation X, Y and Z DevX, DevY, DevZ 3 Two parameters to set: Window length and sample rate IDEAL 2016, Yangzhou, China 10 / 18
  11. 11. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Overview Time window length Sample rate Features selection Classifiers trainning Time window length Accuracy with varying time window length Window length Type C4.5 1-NN Naïve Bayes PART 10 (200ms) S 83,71 92,38 68,76 85,18 15 (300ms) S 85,38 93.28 68,62 87,66 20 (400ms) S 87,25 92,86 69,65 89,59 25 (500ms) S 86,10 89,80 71,15 90,85 30 (600ms)* S 87,90 85,49 70,05 91.99 35 (700ms) S 91.59 83,59 71.99 90,31 40 (800ms) S 88,95 82,89 70,64 91,94 10 (200ms) F 86,01 92,84 69,34 88,63 15 (300ms) F 85,38 93,97 74,41 90,34 20 (400ms) F 86.73 94,09 79,1 91,03 25 (500ms)* F 85,87 94.39 80,93 90.91 30 (600ms) F 84,66 92,25 84,58 87,54 35 (700ms) F 82,94 90,59 88.41 85,36 IDEAL 2016, Yangzhou, China 11 / 18
  12. 12. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Overview Time window length Sample rate Features selection Classifiers trainning Sample rate Maximum sampling rate imposed by hardware: 20ms Other rates got by subsampling Accuracy with different sample rates Sample period Type Attrib C4.5 1-NN Reg. Log. Naïve Bayes PART 20ms S 97 95.38 97.80 90.26 85.71 95.82 40ms S 52 92.10 97.32 87.48 84.35 91.65 60ms S 37 88.38 92.03 87.24 83.60 87.70 20ms F 82 95.66 98.43 88.76 86.74 94.09 40ms F 46 91.97 97.21 89.34 84.43 92.62 60ms F 34 88.45 89.50 84.25 83.46 85.04 Best with high sampling rates IDEAL 2016, Yangzhou, China 12 / 18
  13. 13. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Overview Time window length Sample rate Features selection Classifiers trainning Features selection High number of features Syncope falls: 30 ∗ 3 + 6 = 96 attributes Forward falls: 25 ∗ 3 + 6 = 81 attributes Correlation between attributes and class Forward fall Syncope fall % Inf. Attrib. % Inf. Attrib. % Inf. Attrib. % Inf. Attrib. 0.515 DevZ 0.173 AccelX1 0.541 DevY 0.233 AccelX11 0.429 DevY 0.163 MeanZ 0.523 MeanZ 0.232 AccelX8 0.399 MeanX 0.159 AccelZ24 0.238 AccelY12 0.229 AccelY11 0.316 DevX 0.154 AccelX2 0.235 AccelX7 0.229 AccelX9 0.209 MeanY 0.144 AccelZ23 0.235 AccelX12 0.227 AccelX10 0.195 AccelX0 0.135 AccelX3 0.233 AccelX13 0.226 AccelX14 IDEAL 2016, Yangzhou, China 13 / 18
  14. 14. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Classifiers comparison Robustness analysis Model evaluation Classifiers comparison Settings Sampling rate: 20ms; 30 or 25 samples per window 10-fold crossvalidation Wrapper feature selection with hill climbing search C4.5 1-NN Log. Reg. Naïve Bayes PART Accuracy S 0.98 1 0.92 0.93 0.96 F 0.98 1 0.89 0.91 0.95 Recall S 0.98 1 0.94 0.90 0.97 F 0.98 1 0.91 0.92 0.97 Attributes S 12 7 16 9 7 F 9 13 11 12 7 IDEAL 2016, Yangzhou, China 14 / 18
  15. 15. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Classifiers comparison Robustness analysis Model evaluation Robustness analysis Experiment Forward falls (not enough mannequins) Classifiers from previous phase Evaluate with two unseen people’s falls C4.5 1-NN Log. Reg. Naïve Bayes PART Accuracy F 0.91 0.66 0.97 0.98 0.98 Recall F 0.90 0.98 0.95 0.96 0.98 Attributes F 9 13 11 12 7 IDEAL 2016, Yangzhou, China 15 / 18
  16. 16. Introduction Data acquisition Classifiers trainning Model evaluation Conclusions Conclusions and future work Mild difficulty Classifiers implemented on a Samsung Gear S Satisfactory result High battery consumption Future work Multisensor fall detection (sound and image) Ensamble of classifiers IDEAL 2016, Yangzhou, China 16 / 18
  17. 17. Thanks for your attention! 谢谢 Scripts and datasets can be freely downloaded from http://atc1.aut.uah.es/˜david/ideal2016 David F. Barrero david@aut.uah.es

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