1. Evaluation of a Dynamic Time Warping matching function for human fall detection using structural vibrations Ramin Madarshahian, Doctoral Candidate, mdrshhn@email.sc.edu Juan M. Caicedo, Associate Professor Diego Arocha Zambrana, M.S. Candidate http://sdii.ce.sc.edu/

2. Table of Content 2 DTW 3 Results 5 Introduction 1 Factorial analysis 4 Future work 6 Problem statement 2

3. 3 1 Introduction Problem statement 2 DTW 3 Factorial analysis 4 Results 5 Future work 6 Importance of fall

4. Statistics: – Approximately one in every three adults 65 years old or older, falls each year [1,2] – Falls are the leading cause of injury deaths and accounted for 83% of all fatal falls in 2005 in Ireland [3] – annual cost of €10.8 m alone for just one Irish hospital [4] 4 1 Introduction Problem statement 2 DTW 3 Factorial analysis 4 Results 5 Future work 6 Importance of fall [1] A. Salva,and et all, 2004 [2] S.R.Lord,and et all, 2001 [3] www.cso.ie. www.cso.ie [4] P.E. Cotter, 2006

5. Long lie! – Involuntarily remaining on the ground for an hour or more following a fall [1] – Half of those elderly who experience a ‘long-lie’ die within 6 months, even if no direct injury from the fall has occurred [2] 5 1 Introduction Problem statement 2 DTW 3 Factorial analysis 4 Results 5 Future work 6 Importance of fall [1] A.C. Reece, 1996 [2] D. Wild,and et all, 1981

6. Wearable devices Advantages: – Reliable algorithms – Cost efficient and not complicated to use Disadvantages – Sometimes it is hard to use – Easy to forget to use, specially for Alzheimer patients 6 1 Introduction Problem statement 2 DTW 3 Factorial analysis 4 Results 5 Future work 6 Importance of fall [1] Daniel Rodríguez-Martín, 2013 fall detection systems [1]

7. 7 1 Introduction Problem statement 2 DTW 3 Factorial analysis 4 Results 5 Future work 6 Importance of fall [1] Philippe Katz, and et al, 2013 fall detection systems Vision based methods Advantages: – Independent of elderly Disadvantages – Privacy concern – Blind point [1]

8. 8 1 Introduction Problem statement 2 DTW 3 Factorial analysis 4 Results 5 Future work 6 Importance of fall [1] Diego Arocha, and et al, 2014 fall detection systems Vibration based methods Advantages: – Independent of elderly – Cost efficient – High sensitivity Disadvantages – More work is needed to develop algorithms with less number of false alarms [1]

9. An experiment designed to consider effect of three different factors on recorded signal: – Type of object dropped – Height of the fall – Distance to the sensor Goal: to see which factor is the most effective. 9 Introduction 12 Problem statement DTW 3 Factorial analysis 4 Results 5 Future work 6 objective

10. 10 Introduction 12 Problem statement DTW 3 Factorial analysis 4 Results 5 Future work 6 objective Experiment Factor Distance Factor Height Factor Type

11. 11 Introduction 12 Problem statement DTW 3 Factorial analysis 4 Results 5 Future work 6 objective Experiment Signal database

12. 12 Introduction 12 Problem statement DTW 3 Factorial analysis 4 Results 5 Future work 6 objective Experiment Signal database

13. 13 Introduction 12 Problem statement DTW 3 Factorial analysis 4 Results 5 Future work 6 objective Experiment Signal database Calibration signals

14. 14 Introduction 1 Problem statement 23 DTW Factorial analysis 4 Results 5 Future work 6 Theory (Example)

15. 15 Introduction 1 Problem statement 23 DTW Factorial analysis 4 Results 5 Future work 6 15 DTW=0.9164 DTW=2.8533 Theory (Example)

16. 16 Introduction 1 Problem statement 23 DTW Factorial analysis 4 Results 5 Future work 6 16 Factors RunheightTypedistanceMeanMedian Standard deviation 1highbagclose0.62690.33321.0072 2lowbagclose0.36070.12710.7939 3highballclose0.02240.01910.0119 4lowballclose0.01050.00950.0055 5highbagfar1.20800.65111.4445 6lowbagfar0.45730.14751.4092 7highballfar0.01070.00610.0118 8lowballfar0.00290.00210.0040 Theory (Example) Results

17. Introduction 1 Problem statement 2 DTW 34 Factorial analysis Results 5 Future work 6 Theory-Normal Plot a case study for optimizing a product life: [1] [1] Jiju Antony, 2013

18. Introduction 1 Problem statement 2 DTW 34 Factorial analysis Results 5 Future work 6 Theory-Normal Plot [1] Jiju Antony, 2013 Factors and Levels In our experiment we have 3 factors: 1-Height Low and High 2-Type Basketball and Bag of K-Nex 3-Distanse Far to the sensor and Close to the sensor

19. Introduction 1 Problem statement 2 DTW 34 Factorial analysis Results 5 Future work 6 Theory-Normal Plot [1] Jiju Antony, 2013 Box-Cox transform Checking assumption

20. Introduction 1 Problem statement 2 DTW 34 Factorial analysis Results 5 Future work 6 Theory-Normal Plot [1] Jiju Antony, 2013 Box-Cox transform

21. Introduction 1 Problem statement 2 DTW 34 Factorial analysis Results 5 Future work 6 Theory-Normal Plot [1] Jiju Antony, 2013 Box-Cox transform

22. 22 Introduction 1 Problem statement 2 DTW 3 Factorial analysis 45 Results Future work 6 Bag Basketball

23. 23 Introduction 1 Problem statement 2 DTW 3 Factorial analysis 45 Results Future work 6 Significant factor

24. 24 Introduction 1 Problem statement 2 DTW 3 Factorial analysis 45 Results Future work 6 1-DTW can be used successfully for comparison of fall signals. 2-To perform a good factorial analysis logarithm transform on response obtained from DTW is recommended. 3-Factorial analysis shows that in compare to height of fall and distance of fall place to sensor, Type of falling object has most significant impact on response.

25. 25 Introduction 1 Problem statement 2 DTW 3 Factorial analysis 4 Results 56 Future work 1-DTW is one of the scales for comparison of signals, other methods of signal comparison can be evaluated using factorial analysis. 2-DTW can be applied using different type of distance matrix. Study of these function is useful to extract the best response. 3-Development of a prediction algorithm using DTW is our next research step in this project.

26. Evaluation of a Dynamic Time Warping matching function for human fall detection using structural vibrations Ramin Madarshahian, Doctoral Candidate, mdrshhn@email.sc.edu Juan M. Caicedo, Associate Professor Diego Arocha Zambrana, M.S. Candidate http://sdii.ce.sc.edu/ Thanks!