1. Saisakul Chernbumroong, Shuang Cang, Anthony Atkins, Hongnian Yu Expert Systems with Applications 40 (2013) 1662–1674 Elderly activities recognition and classification for applications in assisted living 1

2. Outline 2 Introduction Methodologies Experiment Conclusions

3. Introduction 3 Population ageing phenomenon has affected every human in many ways, especially in healthcare Assisted living system can help elderly persons maintain healthy and safety while living independently. Current systems are ineffective in actual situation, difficult to use and have a low acceptance rate.

4. Introduction 4 Factors required for activity recognition in assisted living system

5. Introduction 5 Based on sensor types, activity recognition can be divided into two approaches On-object On-body On-object sensor isn’t use in this study An infeasible and time-consuming process uncertainty of sensor

6. Introduction 6 Small, low-cost, non-intrusive non-stigmatize wrist- worn sensors. Develop a feature selection method named feature combination which emphasizes on the performances of a combination of features rather than single feature.

7. Methodologies 7

8. 8 Choice of activities of daily livings (ADLs) BADLs Base ADLs IADLs Instrumental ADLs

9. Methodologies 9 Sensors Acceleration X-axis Y-axis Z-axis Temperature Altitude

10. Pre-processing 10 WMA: smoothen the signal A t = w 1 A t + w 2 A t−1 +… + w n A n−1 Segmented at 3.88 s with 50% overlapping

11. Feature generation 11

12. Clamping Method & Feature Combination 12

13. Experiment 13 H1: the proposed method can achieve high classification rate (>90% accuracy) which is higher than previous similar works H2: activity classifications accuracy can be improved by combining data from temperature sensor and/or altimeter with accelerometer.

14. Experiment 14 A total of 19.2 h of sensor data were collected from 12 healthy older adults aged between 65 and 78 years 10-fold cross validation

15. Experiment 15

16. Experiment 16

17. Conclusion 17 First paper An activity recognition method which considers both technical and practical aspects focusing on user acceptance, privacy (non-visual), systems accuracy and cost. Develop a feature selection method named feature combination which emphasizes on the performances of a combination of features rather than single feature.

18. Authors: Saisakul Chernbumroong, Shuang Cang, and Hongnian Yu Biomedical and Health Informatics, IEEE Journal of (Volume:19, Issue: 1 ) Reporter: 張記維 18 Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People

19. Outline Introduction Activity recognition framework Experiment Future research Conclusion

20. Introduction 20 The number of people aged 65 and over has increased significantly over the years Increase health care cost One way is to promote home-based care Develop an activity recognition model to detect activity od an elderly people

21. Introduction 21 Sensors Chest strap heart rate Dominant wrist Accelerometer, gyroscope, light and barometer Non-dominant wrist Temperature, altimeter The sensors are separated between the two wrists due to the limitation on the hardware

22. Introduction 22 Activity recognition framework :probability that model j predicts that data x i belongs to class K

23. Introduction 23 Genetic Algorithm Determine the fusion weights Mimics natural selection in which the population is modified over time Start to find solution with a group of answer rather than one answer

24. Introduction 24 Genetic Algorithm 1. Generate a number of answers randomly 2. Calculate fitness function 3. Repeat the follow steps until achieve goal 1. Selection 2. Crossover 3. Mutation 4. Calculate fitness function 100010100 : 735 101000010 : 498 111011101 : 875 001011010 : 902 100010 100 101000 010 111011 101 001011 010 111011 \/ 101 001011 /\ 010 111011 \/ 010 001011 /\ 101 100010100 001011010 111011101 001011101 100010100 001001010 111011101 001011101

25. Introduction 25 Limitation from previous work: fitness functions such as a function that reflects on the seven classifier combination functions have not been investigated some of these results were often compared with the mean accuracy of a set of classifiers rather than to the best individual classifier

26. Pre-processing 26 WMA: smoothen the signal A t = w 1 A t + w 2 A t−1 Segmented at 3.88 s with 50% overlapping

27. Feature extraction 27

28. Feature extraction 28 Each input data including 14 features. Mean Maximum Minimum … Correlations between each acceleration axis and gyroscope axis are calculated In total, there has 202 features are extracted

29. Feature selection 29 Feature combination algorithm

30. Classification Algorithm 30 Three algorithms which are widely used in the sensor- based activity recognition Multilayer perceptron neural network (MLP) Radial basis function neural network (RBF) Support vector machine (SVM) :probability that model j predicts that data x i belongs to class K

31. Classifier fusion k=1, w 1 P 1 + w 2 P 2 + w 3 P 3 k=2, w 1 P 1 + w 2 P 2 + w 3 P 3 k=3, w 1 P 1 + w 2 P 2 + w 3 P 3. k=13, w 1 P 1 + w 2 P 2 + w 3 P 3 max k

32. Genetic Algorithm Based Fusion Weight (GAFW) 32 0.20.4 0.35 0.3 0.50.30.2 0.60.2 0.10.80.1 345 676 894 256 512 0.20.4 0.35 0.3 0.50.30.2 0.60.2 0.10.80.1 0.50.3 0.50.30.1 0.35 0.1 0.35 0.2 0.10.80.2 0.50.3 0.50.250.1 0.35 0.1 0.35 0.2 0.10.80.2 Calculate fitness function Select a place to cut Crossover 445 676 894 256 512

33. Experiment 33 Collected data of 13 activities Perform each activity for 10 min Allowed to perform the activities 10-fold cross validation brushing teeth Exercising Feeding Ironing Reading Scrubbing Sleeping using stairs Sweeping Walking washing dishes watching TV wiping

34. Experiment 34 Selected Features

35. Experiment 35

36. Experiment

37. Future research 37 Combine all sensor in a watch The sensor data are sent wirelessly to the PC Development in offline

38. Conclusion 38 Second paper Strengths Most of the classifiers have higher accuracy compared with best classifier Weaknesses Higher computational cost Offline Bad result against the other weight function

39. Q&A 39