Apply incremental learning for daily activity recognition using low lever sensor data = áp dụng học tăng cường cho bài toán nhận dạng hành động hàng ngày sử dụng dữ liệu từ cảm ứng đơn giản

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY TA VIET CUONG APPLY INCREMENTAL LEARNING FOR DAILY ACTIVITY RECOGNITION USING LOW LEVEL SENSOR DATA MASTER THESIS OF INFORMATION TECHNOLOGY Ha Noi, 2012 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY TA VIET CUONG APPLY INCREMENTAL LEARNING FOR DAILY ACTIVITY RECOGNITION USING LOW LEVEL SENSOR DATA Major: Computer Science Code: 60.48.01 MASTER THESIS OF INFORMATION TECHNOLOGY Supervised by Assoc Prof Bui The Duy Ha Noi, 2012 Table of Contents Introduction 1.1 Overview 1.2 Our Works 1.3 Thesis Outline 2 Related Work 2.1 Daily Activity Recognition in Smart Home System 2.2 Daily Activity Recognition approach 2.3 Incremental learning model 5 Framework for Activity 3.1 Data Acquisition 3.2 Data annotation 3.3 Feature extraction 3.4 Segmentation Recognition in the Home Environment Growing Neural Gas model 4.1 GNG structure 4.1.1 Competitive Hebbian Learning 4.1.2 Weights Adapting 4.1.3 New Node Insertion 4.2 Using GNG network for supervised learning 4.2.1 Separated Weighted Euclid 4.2.2 Reduce Overlapping Regions by Using a Local 10 10 12 12 14 Error Threshold 15 15 17 18 19 23 24 25 Radial Basic Function network 27 5.1 Standard Radial Basic Function 27 5.2 Incremental Radial Basic Function 29 iv TABLE OF CONTENTS v Experiment and Result 31 Conclusion 35 7.1 Summary 35 7.2 Future works 36 Apply Incremental Learning for Daily Activity Recognition Using Low Level Sensor Data Abstract Daily activity recognition is an important task of many applications, especially in an environment like smart home The system needs to be equipped with the recognition ability so that it can automatically adapt to resident’s preferences So far, a great deal number of learning models has been proposed to classify activities However, most of them only work well in off-line manner when training data is known in advance It is known that people’s living habits change over time, therefore a learning technique that should learn new knowledge when there is new data in great demand In this thesis, we improve an existing incremental learning model to solve this problem The model, which is traditionally used in unsupervised learning, is extended for classification problems Incremental learning strategy by evaluating the error classification is applied when deciding whether a new class is generated A separated weighted Euclid is used to measure the distance between samples because the large variance of information contains in feature vectors To avoid constant allocation of new nodes in the overlapping region, an adaptive insertion constraint is added Finally, an experiment is carried to assess its performance The results show that the proposed method is better than the previous one The proposed method can be integrated into a smart system, which then pro-actively adapts itself to the changes of human daily activity pattern Introduction Smart home is considered as a part of Ubiquitous Computing trend It aims to enrich the home environment with intelligent devices with the purpose of supporting the residents living inside the environment [4] Understanding the users’ activities is one of the key features of a smart home system Regular activities which are performed in the home can be divided into two types One type of activities is characterized as simple gestures of some part of the body such as running or walking The other one is the set of actions which become a pattern in users’ daily routines Some examples of this type are reading, cooking or watching television Recognizing these activities will provide useful information for understanding the environment context In our thesis, we focus on the problem of recognizing the second types of the activities The most common data source in the smart home system is low-level sensor information [4] There have been many propose approaches for recognizing activities from low level sensory data ([20, 3, 21]) One of the properties of daily activities in the home environment is its variation through time because the user’s habits are change in real life To deal with this problem, previous approach must have to train the models again when there are changes in activities patterns However, this is a resource consuming process In our thesis, we apply incremental learning model into the problem of activity recognition to resolve the above problem The incremental learning models are based on the Growing Neural Gas (GNG) network [8] We extend the growing neural gas model for activity recognition in two ways The first approach is to use multiple GNG networks For each activity class, we train a separated network using samples of the class Technically, the weak point of the traditional growing neural is its network size constantly grows based on the error evaluation for new sample insertion This learning strategy encounters the performance problem in case classes overlap We propose the use of another metric for measure distance in the GNG network and a constraint to the growing condition with the purpose to make the models more balance between each class The second approach is to create a Radial Basic Function (RBF) network [16] from the GNG network This approach is similar to the method proposed in [7] We carry out an experiment to compare both models Performance is evaluated on real daily activity dataset The experimental results show that the task of recognizing daily activity can achieve good result with incremental learning approach The remaining of thesis is organized as follows In Section 2, we present the works related to activity recognition and incremental learning In Section 3, we describe more detail of activity recognition problems including data acquisition, data segmentation and feature extraction In Section 4, we introduce the structures of the GNG network and the way of using it for daily activity recognition problem In Section 5, the incremental version of the RBF network based on the GNG network is presented An experiment with real data set is presented in Section In Section 7, we discuss the summary of our thesis and future works Related Work In recent years, there are many researches in building a system for smart home environment In these systems, the activity recognition is considered as a part of the context recognition module While the context recognition module is refer to understand a wide range of knowledge in the intelligent environment, the main purpose of activity recognition is to extract the information of what the resident is doing The task of recognizing activities depends on which sensory data the system can perceive from the real world There are a variety of sensor types in a smart home system Sensors can be used for position measurement, detecting motion, detecting sound, reading temperature These sensors usually create many data streams [14], which have to be combined in order to produce more useful information Comparing to other types of receivers like cameras or microphones, using low level sensors offers low cost in building the sensor network and transparency The data generated by low level sensors is quite easy to collect and process in comparison to other types of device like camera 2.1 Daily Activity Recognition approach There are many proposed models for recognizing daily activities In [20], they built an activity recognition model based on Naive Bayes classifiers In [23], a multilayer model is used to detect walking movements from passive infrared motion detectors placed in the environment In [3], they proposed a framework for activity recognition including feature extraction, feature selection and predicting models The framework use the four popular machine learning methods including Bayes Belief Networks, Artificial Neural Networks, Sequential Minimal Optimization and Boosting One of the properties of daily activity is the change of their patterns because of the vary of users’ habits This makes the decision boundary can change through time Proposed methods requires to train the models again when the changes occur Incremental learning approach can be used to solve this problem 2.2 Incremental learning model The approach of incremental learning is aim to handle the problem of learning new knowledge while maintaining the previous one [11] In unsupervised approach, the well known clustering algorithm k-means [13] can be learned on-line by a weight update rule An artificial neural network, namely SelfOrganized Map (SOM) [12], is presented as a unsupervised learning method using incremental approach More flexible networks with similar approach are Growing Cell Structures [7] and Growing Neural Gas [8] In supervised learning, there are several efforts to adapt the exist offline method to use in incremental training With the inspiring of adaptive boost algorithm(AdaBoost) [6], [17] proposed an incremental learning method, namely Learn++ It uses the MultiLayer Perceptron network as a week learner to generate a number of weak hypotheses Radial Basic Function (RBF) networks [16] combine a local representation of the input space and a perceptron layer to fit the input pattern with its target [9] proposed a method to insert new nodes into the RBF network Using this approach, the RBF network is capable for learning overtime Another approach is Fuzzy ARTMAP [2] which based on Adaptive Resonance Theory (ART) networks Framework for Activity Recognition in the Home Environment In this section, we present the framework of daily activity recognition using low level sensor data in the home environment Its main purpose is to map the data from the environment to a sequence of activities We adapt the proposed framework in [3] to emphasize the incremental learning properties of the framework Figure illustrates the steps in an activity recognition module using low-level sensor data The information of the surrounding environment comes under a stream of sensor events Then, the stream can be annotated and split into labelled samples The labeled samples are then provided to the incremental learning model for training In online recognition, the stream is segmented into separated sequences of sensor events Each Figure 1: Framework for activity recognition separated sequence is then classified by the learned model By applying incremental learning, the model is trained continuously over the life span of the system whenever there is new labelled data 3.1 Data Acquisition In the data acquisition phase, sensors are implemented around the home The sensors are low level, which are differ from cameras or microphones They continuously monitor the environment for some specific information There are many types of information in the home environment that can be monitored by state change sensor such as door, picking object or temperature Choosing the types of sensor are included totally depends on the system’s design The data from each low level sensor is collected and combined into a stream of data at a central server Each sensor’s signal is considered as an event in the stream An example of event stream [5] is given in table Each event has a time stamp, the name of the sensor and its value There are several platforms for collecting and processing the data stream in the smart home system such as Motes platform [1] or OSGI platform [10] Table 1: An example of sensor Date Time 2009-10-16 08:50:00 2009-10-16 08:50:01 2009-10-16 08:50:02 2009-10-16 08:50:13 2009-10-16 08:50:17 3.2 events in data stream Sensor Value M026 ON M026 OFF M028 ON M026 OFF M026 OFF Data annotation For the purpose of training model, the stream data needs to be segmented and labelled into separated activities In a smart home system, data can be annotated directly by using devices such as Personal Digital Assistant [20] An alternative way is labelling the stream data indirectly through a post processing step The users can label their activities with a visualization tool [3] Because the data is captured from various sensors and span over a long time, it is difficult to determine the right starting and ending point of an activity After this step, each sensor event in the stream data is added with an optional action label 3.3 Feature extraction Because the daily activities in the home environment usually happen around a specific area, the motion sensors can produce good features for differing the activity classes In [3], the activated motion sensors during the period of an activity is included into the feature vector The feature vector also uses high contextual information such as day of week, time of day, previous activitiy, next activity and energy consumption This approach achieves a high accuracy with learning models such as multilayer perceptron or decision tree In our approach, we use a similar method to extract feature for training with online learning model A feature vector of an activity sample includes: • Length of the activity in second • Part of the day when the activity happens It is divided into six separated parts: morning, noon, afternoon, evening, night, latenight, • Day of week • Previous performed activity • Number of motion sensors are activated during the activity’s period, and the number of value ”ON” • A list of motion sensors which are activated • Energy consumption 3.4 Segmentation In a real application of activity recognition, the stream of sensor events is required to be segmented into separated subsequences before the recognition models can classify them In [18], they mine the data stream to discover the trigger patterns and use these patterns to determine the start of an activity However, the trigger patterns are not always clear enough to discover because daily activities are often change and overlapped with each other An alternative approach is to use sliding time windows [19] The data stream is split into fixed-time windows and then classify them into one of the activity classes Although this method can generate a sequence of activity labels from data stream effectively, it may have difficulty when the time window overlaps with more than one activity Growing Neural Gas Network In this chapter, we present an incremental learning model which is improved from Growing Neural Gas (GNG) networks [8] The GNG network is an unsupervised learning and has been applied into vector quantization, clustering, and interpolation Its structure is similar to SOM [12] network and Neural Gas [15] network The network produces a local representation of the input space which is learned incrementally In the first section, we introduce the structure and learning rule of the network in unsupervised learning Then, we extend its structure into the supervised problems with some modifications for improving the performance 4.1 GNG Network Structure A GNG network includes nodes and connections Each node u in the network is presented with reference vector wu in the input space V (V is a subset of Rn ( and a local counter variable eu The wu is considered as the position of node u in the vector space and eu presents the density in the region around u The vector space is split into small regions, which takes each node of the network as a center The connections between nodes represent the structure of the network If two nodes are connected, their correspondent regions are adjacent in V The network extends the Competitive Hebbian Learning principle [22] to learn its structure The GNG network uses its structure to adapt the nodes’ reference vector and to determine where to add new nodes When the network receives a new training sample x, the model will find the nearest node s1 to x and assign x to the region of s1 Then, the position of s1 and all its topological neighbors n are moved toward x The moving distances are computed as follow: The weight of s1 is adjusted by an amount of ∆s1 : ∆s1 = b (x − w s1 ) (1) For each neighbour v of s1 , the weight vector wv is added by ∆v: ∆v = n (x − wv ) (2) The network uses two constants b , n to control the adaptive of a node to a new sample The network automatically increases its number of nodes after receiving a fixed number λ of training samples A new node will be placed into the highest density region; i.e the region has maximum local error in the network More specifically, the adding algorithm finds a node q which eq is maximum Then it chooses f among the neighbors of q which ef is maximum New node u is set to the middle between q and f and the local error of the two nodes q and f are decreased due to the introducing of a new node 4.2 Apply growing neural gas model to activity recognition The GNG network can be extended for supervised learning by creating a separate network for each class In unsupervised problems, the GNG constructs a network that represents the underlying structure of the input space V By using this property, it is possible to have an estimate of the error when assigning an input vector to a specific input space The error measure is defined as the nearest distance from the input vector to all the nodes in the network Based on the error, the input vector then is classified into the class which minimizes the error Assume there are L classes, then we create L different GNG networks, namely G1 , G2 , , GL Each network Gi is trained with all the input vectors of the label i Then, the distance from an input vector x to a network Gi is defined as the distance from the nearest node in the network to the input vector: d(x, Gi ) = min||x − wu || u∈Ai (3) where Ai is the set of node in Gi We use the defined distance as the error when x is assigned to Gi To classify a new input sample x, we iterate all the trained networks and match the input to the network which has the smallest error to x 4.3 Separated Weighted Euclid We use an adaptive version of weighted Euclid for a more suitable distance measure A GNG network G is associated with a standard deviation vector σ = {σ1 , σ2 , , σN } Each σi is the standard deviation for the dimension i of the input space V The σ vector is updated online in learning process Using the above σi , the distance between an input vector x and a weight vector wu in G is calculated as: N u d(x, w ) = i=1 4.4 (xi − wiu )2 σi2 (4) Reduce Overlapping Regions by Using a Local Error Threshold We extend the approach in [9] to reduce the overlapping regions between networks Each node u is associated with an adding threshold tu of the local error If there is a node has local error counter e exceed the threshold, the network will be extended At the beginning, tu is assigned a value which is the same for all nodes Then, it is updated during the learning process A high value of tu means the region of u should not be inserted more nodes We update tu as follows: Init tu = T which is the starting adding threshold Let (x, y) is the input vector and its label of a training sample Find the nearest network Gi and the node u of Gi has the smallest distance to x: i = argmin(d(x, Gj )) j∈[1 L] u = argmin(d(x, v)) v∈Ai If there is a false prediction i = y, then update the adding criterion tu of u: tu = (1 + β) ∗ tu Radial Basic Function network The Radial Basic Function (RBF) network [16] is a supervised learning method Typically, RBF network typically contains two layers: the hidden layer and the output layer Normally, the hidden layer represents a set of local neurons which is similar to the cells of growing neural gas Each neuron has a weight vector indicates its position in the input space The neuron will be activated if the input vector is closed enough to it, by a Gaussian activation function The neutrons in the hidden layer are connected to the neurons in the output layer to to create a structure similar to a perceptron layer The main purpose of the perceptron layer is to produce a linear combination of the activation of each neuron in the hidden layer Training a RBF network can be combined from an unsupervised learning and a supervised one The positions of hidden neurons in the hidden layer can be found by using a cluster algorithm such as k-means After that, the weight connect from the hidden layer to the output layer can be found by using a perceptron network with a gradient descent method Besides, the two steps can be done separately In the tradition RBF network model, one of its weaknesses is the fixed number of neurons in the hidden layer It is hard to determine how many 10 neurons are appropriate for the input space of the problems, especially in lifelong learning tasks Furthermore, the approach of having fixed neurons in the hidden layer can face difficult in cover the whole space if the dimension of the input space becomes large Using a similar approach as growing gas model, an incremental version of Radial Basic Function can be trained without knowing the number of neurons in the hidden layer The main difficult in the incremental approach is to compute the weight of edges connect the hidden layer to the output layer In the tradition approach, a layer of perceptrons is built and the gradient descent method is used to train until the error converge Unfortunately, we can not apply this to the incremental approach because we not know which the set of inputs is fed to the network Besides that, when a node is added to the hidden layer, it must be connect to the output layer This can affect the already trained weights The criterion to stop in gradient descent training is not easy to define here because we not have the addition validation set or refer back the error of previous input samples However, [7] proposed to use only one step of gradient descent instead of running gradient descent until the network converges Experiment and Result In this experiment, we apply the above incremental learning algorithm for daily activity recognition The activities are segmented and annotated We use dataset from the WSU CASAS smart home project [5] in our experiment This dataset is sensor data, which was collected from a house of a volunteer adult in months There are 33 sensors, which include motion sensors, door sensors and temperature sensors A motion sensor is used to discover the motion, which happens around its location and takes ”ON” or ”OFF” value A door sensor is used to recognize opening and closing door activity and takes ”OPEN” or ”CLOSE” value A temperature sensor is used to sense a changing in temperature for a specific threshold and takes real value The entire data set is annotated with the beginning and ending of activities The number of annotated activities is 2310 and the number of different types is 15 We run our experiment with four described models: Fuzzy ARTMAP, incremental RBF (RBF ), the traditional Growing Neural Gas model (GNG) and the improve Growing Neural Gas model(new GNG) 11 Table 2: Accuracy of the Training train1 Fuzzy ARTMAP 68.74 % RBF 58.79 % GNG 70.56 % new GNG 72.55% four models train2 train3 72.29 % 71.26% 61.30 % 57.40% 71.17% 72.47% 74.81% 77.58% The data set is randomly divided into two equal parts: train and test The first part is used to train the models and the second part is used for evaluate Each part has around 1100 samples To simulate an incremental learning process in the real system, the train part is further divided into three equal parts train1 , train2 , train3 These part are used to train the models incrementally More specifically, there are three training phases In the first training phase, the part train1 is used Each sample in the train1 part is fed only once in a random order to the model Then, in the second phase, we use samples in the train2 phase to train the model which is received after the first phase Next, we use samples in train3 to train the model from the second phase In each phase, the model is tested on the test data The results are presented in the table IIt can be seen clearly from the table that the Fuzzy ARTMAP, GNG, new GNG improve their accuracy when they are provided more training data In the three models, the new GNG has the highest improvements and reaches the best accuracy at 77.58% in the third training phase Only the RBF decreases substantially when the train3 is provided In all three training phases, both the Fuzzy ARTMAP and GNG model are approximately equal while the RBF has a lowest figures It can be explained by the different between the RBF model and the other ones The RBF combines the local representation with a perceptron layer However, in incremental learning, training the perceptron layer until its converges is a difficult task Using one step of gradient descent really decrease the model’s accuracy compare to the other three models Hypercubes representation of Fuzzy ARTMAP model and sphere representation of GNG model result in a similar accuracy However, adding more constraint for controlling the overlapped areas in new GNG may increase the accuracy fairly (5% with the train3 data) 12 Conclusion In this thesis, we presented an incremental approach for daily activity recognition in smart home system The main focus of the problem is to classify the data comes from low level sensor into different type of activities The proposed approach is based on an unsupervised learning method, namely GNG network The experiment is carried out on real life data set While the incremental version of RBF network suffers from weight changing, the multiple GNG networks approach has quite good performance in comparison to the Fuzzy ARTMAP model By changing the metric of distance measurement and preventing the constant inserting of new nodes in the overlapping region, the improved version can separated different activity classes well In the future, we are planing to employ more efficient method for feature extraction from the sequence of sensor events The method describing in our thesis depends largely on the space properties of the activity patterns It does not use temporal information which are presented in the sequence of sensor events Therefore, it can have difficulties in classifying the activities which usually happen in the same area In addition to that, because the feature vector combines different types of category data, finding a good distance metric in the input space is difficult References [1] G Anastasi, M Conti, A Falchi, E Gregori, and A Passarella Performance measurements of motes sensor networks In In MSWiM 04: Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, pages 174–181 ACM Press, 2004 [2] G A Carpenter, S Grossberg, N Markuzon, J H Reynolds, and D B Rosen Fuzzy artmap: A neural network architecture for incremental supervised learning of analog multidimensional maps IEEE Transactions on Neural Networks, 3(5):698–713, 1992 [3] Chao Chen, Barnan Das, and Diane Cook A data mining framework for activity recognition in smart environments Intelligent Environments IE 2010 Sixth International Conference on, pages 80–83, 2010 13 [4] Diane J Cook, Juan C 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on Ubiquitous computing UbiComp 08, 344:1, 2008 [22] Ray H White Competitive Hebbian Learning 1991 [23] Christopher R Wren and Emmanuel Munguia Tapia Toward scalable activity recognition for sensor networks Networks, 3987:168–185, 2006 15 Publications Multi-Agent Architecture For Smart Home, Viet Cuong Ta, Thi Hong Nhan Vu, The Duy Bui, The 2012 International Conference on Convergence Technology January 26-28, Ho Chi Minh, Vietnam A Breadth-First Search Based Algorithm for Mining Frequent Movements From Spatiotemporal Databases Thi Hong Nhan Vu, The Duy Bui, Quang Hiep Vu, Viet Cuong Ta, The 2012 International Conference on Convergence Technology January 26-28, Ho Chi Minh, Vietnam Online learning model for daily activity recognition, Viet Cuong Ta, The Duy Bui, Thi Hong Nhan Vu, Thi Nhat Thanh Nguyen, Proceedings of The Third International Workshop on Empathic Computing (IWEC 2012) 16 ... works 36 Apply Incremental Learning for Daily Activity Recognition Using Low Level Sensor Data Abstract Daily activity recognition is an important task of many... TECHNOLOGY TA VIET CUONG APPLY INCREMENTAL LEARNING FOR DAILY ACTIVITY RECOGNITION USING LOW LEVEL SENSOR DATA Major: Computer Science Code: 60.48.01 MASTER THESIS OF INFORMATION TECHNOLOGY Supervised... 2.1 Daily Activity Recognition in Smart Home System 2.2 Daily Activity Recognition approach 2.3 Incremental learning model 5 Framework for Activity