Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection

1. Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection EWSN08: European Workshop on Wireless Sensor Networks Piero Zappi, Clemens Lombriser, Thomas Stiefmeier, Elisabetta Farella,Daniel Roggen, Luca Benini, and Gerhard Troster 발표자 : 20095376 최재운

2. Contents • Introduction • System Details • Evaluation • Conclusion

3. Contents • Introduction • Abstract • Background • Problem Statement • Solution Approach • System Details • Evaluation • Conclusion

4. Abstract • In this paper, Authors present Dynamic Sensor Selection. • In order to use efficiently available energy while achieving a desired activity recognition accuracy. • They introduce an activity recognition method. • Activity recognition method • It relies on a meta-classifier that fuses the information of classifiers on individual sensors. • Sensors are selected according to their contribution to classification accuracy.

5. Background • Wearable computing • Supporting people by delivering context-aware services • Wearable technology has been used in behavioral modeling, health monitoring systems, information technologies and media development. • Gestures and activities are important aspect of the user’s context • Small and low-power wireless sensor nodes are used. • Limited memory and computational power.

6. Background • Wearable computing

7. Problem Statement • Wearable computing issue Trade-off solution is needed!! • High classification accuracy is needed • Large number of sensors distribute over the body. • For high classification accuracy, many sensors should be activated. • Minimize energy use • Sensors have battery limitations. • For enhancing lifetime, minimizing sensor size is needed.

8. Solution Approach • Related works about energy use • Adaptive sampling rate and unpredictable duty cycle are representative methods. • In this case, they can not be used to minimize energy use. • Since, user gestures can occur at any time, fixed sensor sampling rate and continuous sensor node operation are needed. • Here, they investigate how to extend network life in an activity recognition system, while maintaining a desired accuracy.

9. Contents • Introduction • System Details • System Overview • Metaclassifier for Activity Recognition • Dynamic Sensor Selection • Evaluation • Conclusion

10. System Overview • System Overview • System relies on classifier fusion to combine multiple sensor data • Gesture classification is performed on individual nodes using Hidden Markov Models (HMM). • A Naïve Bayes classifier fuses these individual classification results to improve classification accuracy. • System introduce dynamic sensor selection to cope with dynamically changing networks • Most sensor nodes are kept in low power state and they are activated when their contribution is needed to keep the desired accuracy.

11. Metaclassifier for Activity Recognition • This activity recognition algorithm is based on a metaclassifier fusing the contributions from several sensor nodes.

12. Metaclassifier for Activity Recognition • Hidden Markov Models (HMM) • A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unobserved state.

13. Metaclassifier for Activity Recognition • Features extracted from the sensor data are classified by competing Hidden Markov Models • In this paper, they started with 15 random initial models and select the one that shows best classification accuracy on the training set.

14. Metaclassifier for Activity Recognition • Finally, they fuse the class label using a naïve Bayes technique.

15. Metaclassifier for Activity Recognition • The naïve Bayes classifier • Probabilistic classifier based on the Bayes’ theorem and the hypothesis that the input features are independent • A typical decision rule is to classify an instance as beloning to the class that maximizes the a posteriori probability. • C : Class, Ai : n input attributes It is hard to compute

16. Metaclassifier for Activity Recognition • The naïve Bayes classifier • Applying the hypothesis of independence and the decision rule they obtain; • The Likelihood is the only parameter that has to be calculated. Do not need to compute by experiments Common elements

17. Metaclassifier for Activity Recognition • The naïve Bayes classifier • Defining • tc : the number of training instances for which the C=c and Ai=ai • t : the number of training instances for class c • Some classes c may not have a sample for which Ai=ai. =>= 0 • For this reason, they define as follows; • m : the virtual sample per class added to the training set • p : a priori probability of a certain value for an attribute

18. Dynamic Sensor Selection • Purpose : To achieve a desired classification accuracy while prolonging the system lifetime • To select at run-time the sensors which are combined to perform gesture classification. • The system minimize the number of sensor used.

19. Dynamic Sensor Selection • Example • Activated cluster set of sensors to achieve the desired classification accuracy is first selected ( Cluster Size = D ) • All subclusters of size (D-1) must still achieve the desired accuracy

20. Dynamic Sensor Selection • Example • When a node fails, they first test whether the remaining nodes fulfill this condition( sub cluster of size D-1 must achieve desired accuracy)

21. Dynamic Sensor Selection • Example • If not, all the clusters of size D+1 that can be built by adding one idle node to the given cluster are tested. • The one that achieves the best performance is selected

22. Dynamic Sensor Selection • Example • If not, the process is repeated until a cluster that fulfills the condition or no idle nodes are left. • In the latter case the system is not able to achieve the desired performance any more.

23. Contents • Introduction • System Details • Evaluation • Evaluation of Activity Recognition Performance • Network Lifetime • Conclusion

24. Evaluation of Activity Recognition Performance

25. Evaluation of Activity Recognition Performance • Purpose : Evaluate the performance of classification as a function of the number of nodes • They perform a set of experiments using 19 nodes placed on the two arms of a tester • They applied their algorithm to clusters of nodes with increasing size (one to 19 nodes). • For each size, they created 200 clusters from randomly selected sensor nodes. • For each cluster size, the average, maximum and minimum classification accuracy is recorded

26. Evaluation of Activity Recognition Performance • Correct classification ratio among random cluster as a function of cluster size

27. Network Lifetime • Dynamic sensor selection scheme vs all sensors • (90% minimum correct classification ratio)

28. Network Lifetime • Dynamic sensor selection scheme vs all sensors • (85% minimum correct classification ratio)

29. Network Lifetime • Dynamic sensor selection scheme vs all sensors • (80% minimum correct classification ratio)

30. Network Lifetime • Network life as a function of the minimum accuracy required

31. Network Lifetime • Evolution of the network • On the left, in dark, are the active nodes • On the right, the number of active nodes • A) 80% minimum accuracy. B) 90% minimum accuracy

32. Contents • Introduction • System Details • Evaluation • Conclusion • Pros • Cons

33. Conclusion • Energy aware design aims to extend sensor nodes life by using low power devices and poweraware applications. • Their method minimizes the number of nodes necessary to achieve a given classification ratio.

34. Conclusion 1. System Level • Pros • 주어진 classification ratio를 만족시키면서 network lifetime을 증가시킬 수 있었음. • 전체를 다 사용하는 것 보다 월등히 좋은 lifetime을 가지고 있다는 것을 알 수 있음. • 각 노드에서 병렬적으로 datamining을 수행하기 때문에, sensor network 특성에 잘 맞음. • 각 센서가 제한된 자원을 가지는 센서네트워크의 특성상 병렬적 처리가 적합함.

35. Conclusion 1. System Level • Cons • naïve Bayes classifier 계산시 모든 class의 P(C=c)가 같다는 가정의 신빙성 결여 • 실험 상 모든 class가 나올확률이 같다고 하였지만, 사람이 처한 상황 등 기타 조건에 따라 class가 나올 확률이 다를 가능성도 높음. • 이러한 확률을 미리 계산하여 계산에 추가를 하였다면, 계산량은 많아지겠지만 정확도를 높일 수 있을 것으로 예상. • naïve Bayes classifier 계산시 (a1, a2, …) 의 independence 가정 • 각각의 sensor에서 분석한 a1, a2 등이 independence하다는 가정하에 naïve Bayes classifier 를 수행하였음. • 한 동작에 대해서 각각의 sensor가 동시에 분석하여 나온 결과물이 independence 하다는 가정은 적합하지 않을 것 같음.

36. Conclusion 1. System Level • Cons • Dynamic sensor selection에서 새로운 노드 추가하는 방법에 대한 추가 논의 필요 • 본 논문에서는 cluster에 새로운 노드를 추가할 시, 모든 조합을 다 맞춰본 후 가장 성능이 좋은 것을 추가하기로 하였음. • 이러한 방법은 실시간으로 실행시 overhead가 발생할 수 있기 때문에, 미리 노드별로 priority를 선정하고 이에 맞춰서 새로운 노드 추가 방안 고려. • Network lifetime 늘리는데 더욱 초점을 맞추고자 한다면, idle 노드 중 잔여 배터리가 많은 노드에게 배치하는 방안 고려.

37. Conclusion 2. Literature Level • Pros • 기본의 data mining 기법 중 신뢰도가 높은 것을 선정하여 classifier로 삼았음. • Cons • 타 알고리즘과 비교 부족 • 본 논문에서는 자신들의 selection 기법과 전체노드가 다 사용되는 방법을 비교. • Network lifetime을 늘리는 것을 더욱 강조하기 위해서는, lifetime을 늘리기 위한 다른 방안들과 직접적이 비교가 더 필요.