Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection EWSN08: European Workshop on Wireless Sensor Networks Piero

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

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Contents

Introduction System Details Evaluation Conclusion

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Contents

Introduction Abstract Background Problem Statement Solution Approach

System Details Evaluation Conclusion

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.

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Abstract

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.

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Background

Wearable computing

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Background

Wearable computing issue

Trade-off solution is needed!!

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Problem Statement

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.

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.

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Solution Approach

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Contents

Introduction System Details

System Overview Metaclassifier for Activity Recognition Dynamic Sensor Selection

Evaluation Conclusion

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.

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System Overview

This activity recognition algorithm is based on a metaclassifier fusing the contributions from several sensor nodes.

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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.

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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.

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Finally, they fuse the class label using a naïve Bayes technique.

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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

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It is hard to compute

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.

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Do not need to compute by experiments

Common elements

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

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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.

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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

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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)

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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

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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.

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Contents

Introduction System Details Evaluation

Evaluation of Activity Recognition Performance Network Lifetime

Conclusion

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Evaluation of Activity Recognition Performance

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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

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Correct classification ratio among random cluster as a function of cluster size

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Network Lifetime

Dynamic sensor selection scheme vs all sensors

(90% minimum correct classification ratio)

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Network Lifetime



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Network Lifetime



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Network Lifetime

Network life as a function of the minimum accuracy required

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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

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Contents

Introduction System Details Evaluation Conclusion

Pros Cons

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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.

Pros 주어진 classification ratio 를 만족시키면서 network

lifetime 을 증가시킬 수 있었음 .• 전체를 다 사용하는 것 보다 월등히 좋은 lifetime 을 가지고 있다는 것을 알

수 있음 .

각 노드에서 병렬적으로 datamining 을 수행하기 때문에 , sensor network 특성에 잘 맞음 .

• 각 센서가 제한된 자원을 가지는 센서네트워크의 특성상 병렬적 처리가 적합함 .

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Conclusion

1. System Level

Cons naïve Bayes classifier 계산시 모든 class 의 P(C=c) 가

같다는 가정의 신빙성 결여• 실험 상 모든 class 가 나올 확률이 같다고 하였지만 , 사람이 처한 상황 등

기타 조건에 따라 class 가 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 sensor 에서 분석한 a1, a2 등이 independence 하다는 가정하에 naïve Bayes classifier 를 수행하였음 .

• 한 동작에 대해서 각각의 sensor 가 동시에 분석하여 나온 결과물이 independence 하다는 가정은 적합하지 않을 것 같음 .

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Conclusion

1. System Level

Cons Dynamic sensor selection 에서 새로운 노드 추가하는

방법에 대한 추가 논의 필요• 본 논문에서는 cluster 에 새로운 노드를 추가할 시 , 모든 조합을 다 맞춰본

후 가장 성능이 좋은 것을 추가하기로 하였음 .• 이러한 방법은 실시간으로 실행시 overhead 가 발생할 수 있기 때문에 ,

미리 노드별로 priority 를 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 .

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Conclusion

1. System Level

Pros 기본의 data mining 기법 중 신뢰도가 높은 것을 선정하여

classifier 로 삼았음 .

Cons 타 알고리즘과 비교 부족

• 본 논문에서는 자신들의 selection 기법과 전체노드가 다 사용되는 방법을 비교 .

• Network lifetime 을 늘리는 것을 더욱 강조하기 위해서는 , lifetime 을 늘리기 위한 다른 방안들과 직접적이 비교가 더 필요 .

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Conclusion

2. Literature Level

Documents

Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection EWSN08: European Workshop on Wireless Sensor Networks Piero