Authors: Andreas Krause, Asim Smailagic, Daniel P Siewiorek, CMU

1. Context-Aware Mobile Computing:Learning Context-Dependent PersonalPreferences from a Wearable Sensor Array Authors: Andreas Krause, Asim Smailagic, Daniel P Siewiorek, CMU IEEE Transactions on Mobile Computing, Vol. 5, No. 2, February 2006 Presenter here: Aravind Krishna Kalavagattu

2. A.SenseWear armband B.BlueSpoon Headset C.Backpack emcompassing Life-Book P-1120 with 802.11b transceiver D.Tungsten W smart phone E.External antenna for GPS receiver.

3. Background • Clustering • the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure • Algorithms • K-means • k-means algorithm is an algorithm to cluster objects based on attributes into k partitions

4. Background • Classification • The learner is required to learn (to approximate) the behavior of a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Training Set • Training and generating a model • Testing Set • Data is assigned labels according to the learnt model from training

5. Classification (example..)

6. Background • KSOM (Kohonen Self-Organizing Maps) • In the training phase weights of the whole neighborhood are moved in the same direction, similar items tend to excite adjacent neurons. Therefore, SOM forms a semantic map where similar samples are mapped close together and dissimilar apart. • Bayesian Networks • Ayan’s slides.. • Key Idea: • If we can make use of independences within the random variables effectively, we can avoid requiring more probability numbers • Depends on the way Joint probability distribution can be factored • If all variables are independent, the number of parameters is linear in the number of variables • Example

7. Bayesian Networks • Bayesian network is a generative model • Joint Probability Distribution • Conditional Dependencies • In techniques used to learn Bayes nets, the order in which the variables are introduced is important, and it reflects the causality between the random variables

8. Motivation • Exploit context information to significantly reduce demands on human attention. • Example: Configure settings of your mobile proactively. • Use a BodyMedia SenseWear to monitor the user and learn with minimum supervision • Defining context is the key! • Having predefined thresholds to define contexts and generic behavior won’t satisfy the user • User interests vary and we need personalization ! • Personalize the application to each user by learning their preferences rather than have predefined thresholds.

9. Novelty and Contributions • Context Identification • Offline and Online approaches to define a context abstraction from various sensor readings • Using state-of-the-art machine learning techniques • Preference Learning • Using Bayesian networks to effectively represent the causal dependencies and learn the probabilities using user interactions, for further inference. • Design and implementation of a wearable study platform realizing these methods • A survey justifying the importance of personalization • A platform for implementing the system using a wearable sensor array. • Shows that context-aware wearable computers are feasible for real life applications • Improvements in wearability and usability.

10. Key Ideas and Details • Two step approach gathers data using wearable sensors. • Context identifier identifies typical contexts and classifies acquired signals. • -Preference Learner relates user’s device interactions to their current context and provide feedback plus configuration of the system variables

11. Key Ideas and Details

12. Context Identifier

14. Preference Learning • Creating a generative model relating the context- and system variables • Technique: Bayesian Network • Efficient method to compute joint PDF • Can handle incomplete data • Can incorporate dynamics • Issues • Algorithms for parameter- / structure learning • K2 • Priors

15. Experimental Design • Motivation of machine learning approach • Survey among college phone users (preliminary) • Threshold analysis • Evaluation of Context Identification method • Self-report study • Real-time movement identification / classification • Evaluation of Preference Learning method • SenSay training • Self-report study

16. System Architecture • Three major components • A laptop • PDA • Wearable sensor array

17. Experimentation

18. User studies

19. Software Architecture • Sensor fusion process modeled as a directed acyclic graph • Sensors and User Interactions are sources • Preprocessing steps are internal nodes • Clustering / Learning algorithms are sinks • Configurable using XML • Object oriented implementation (Java) • Extendable with new sensors / preprocessing steps

20. Software details (contd..) • Event based communication • Distribution of events over the network or streaming into a database (different speeds) • Infrastructural sensors can connect upon availability • High level of concurrency • Maintenance / Reliability • Acoustic feedback in case of error • Tap into sensor fusion graph • Runs 10+ hours without recharging

21. Drawbacks • Practical deployment seems very costly • A PDA, laptop and sensors per person • Motivating scenario could have been better, than just adjusting the cell phone settings. • From a machine learning perspective, the reasons for choosing the specific approach over other alternatives is not clear • Experimental analysis with other possible techniques would have been better • Temporal independence among the observed evidence is assumed • Dynamic Bayes Nets would be more effective • They seem to agree with this, but leave it as future work • User study is preliminary and not representative enough. • Though the sample is small, the population could have been diverse (different age groups, different work schedules and habits) • In experimentation for learning user preferences, a study with metrics like precision-recall would have been appropriate to understand how the system’s learning is compatible with the actual user preferences.

22. Relation to the class • Context-aware computing • New type of sensors • Location Management • Related to Midterm paper • Context-aware migratory services • Application of machine learning techniques for mobile computing problems

23. Relation to our project • Context-aware caching scheme for real-time health monitoring systems • Monitoring patients in a community setting • Hierarchical Tree topology • Sensors report to a PDA • Intermediate Hubs • Doctor sits at the server • Context defined in terms of environmental conditions, sensor readings and user’s health history plus vulnerabilities • Parameters like TTL and Priority are set based on the context, to manage resources and ensure real-time caching.

24. Conclusions • Enable a wearable computer to learn about individual user states using sensors • This process should not require supervision by the user • Let the computer learn to associate user states with user preferences • This paper showed (at least a small-scale) practical implementation framework to do so! • Machine Learning techniques are effectively used

25. References • Andreas Krause, Asim Smailagic, Daniel P. Siewiorek, Context-Aware Mobile Computing: Learning Context-Dependent Personal Preferences from a Wearable Sensor Array, IEEE Transactions on Mobile Computing, Vol. 5, No. 2, February 2006 • Lecture Slides • CSE494 (Information Retrieval Course, Dr. Rao Kambhampati) • CSE471 (Introduction to Artificial Intelligence, Dr. Rao Kambhampati) • K. Van Laerhoven, “Combining the Kohonen Self-Organizing Map and K-Means for On-Line Classification of Sensor Data,” Artificial Neural Networks—ICANN 2001, G. Dorffner, H. Bischof, and K. Hornik, eds., pp. 464-470, 200 • Wikipedia • Clustering • Classification • A. Krause, D.P. Siewiorek, and A. Smailagic, “Unsupervised, Dynamic Identification of Physiological and Activity Context,” Proc. Seventh Int’l Symp. Wearable Computers, Oct. 2003.