Learning a taxonomy of predefined and discovered activity patterns

Many intelligent systems that focus on the needs of a human require information about the activities that are being performed by the human. At the core of this capability is activity recognition. Activity recognition techniques have become robust but rarely scale to handle more than a few activities. They also rarely learn from more than one smart home data set because of inherent differences between labeling techniques. In this paper we investigate a data-driven approach to creating an activity taxonomy from sensor data found in disparate smart home datasets. We investigate how the resulting taxonomy can help analyze the relationship between classes of activities. We also analyze how the taxonomy can be used to scale activity recognition to a large number of activity classes and training datasets. We describe our approach and evaluate it on 34 smart home datasets. The results of the evaluation indicate that the hierarchical modeling can reduce training time while maintaining accuracy of the learned model.

[1]  G. Englebienne,et al.  Transferring Knowledge of Activity Recognition across Sensor Networks , 2010, Pervasive.

[2]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[3]  Jian Lu,et al.  A hierarchical approach to real-time activity recognition in body sensor networks , 2012, Pervasive Mob. Comput..

[4]  Gwenn Englebienne,et al.  Hierarchical Activity Recognition Using Automatically Clustered Actions , 2011, AmI.

[5]  Tao Gu,et al.  Object relevance weight pattern mining for activity recognition and segmentation , 2010, Pervasive Mob. Comput..

[6]  Ping-Yu Hsu,et al.  Mining hybrid sequential patterns and sequential rules , 2002, Inf. Syst..

[7]  George Karypis,et al.  Evaluation of hierarchical clustering algorithms for document datasets , 2002, CIKM '02.

[8]  Qiang Yang,et al.  Cross-domain activity recognition via transfer learning , 2011, Pervasive Mob. Comput..

[9]  Ari Visa,et al.  GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[10]  Henry A. Kautz,et al.  Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.

[11]  Lawrence B. Holder,et al.  Discovering Activities to Recognize and Track in a Smart Environment , 2011, IEEE Transactions on Knowledge and Data Engineering.

[12]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[13]  Diane J. Cook,et al.  Activity Discovery and Activity Recognition: A New Partnership , 2013, IEEE Transactions on Cybernetics.

[14]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[15]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  Diane J. Cook,et al.  Recognizing independent and joint activities among multiple residents in smart environments , 2010, J. Ambient Intell. Humaniz. Comput..

[18]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[19]  R. Sokal,et al.  THE COMPARISON OF DENDROGRAMS BY OBJECTIVE METHODS , 1962 .

[20]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[21]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[22]  Stan Sclaroff,et al.  A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Diane J. Cook,et al.  Discovering frequent user--environment interactions in intelligent environments , 2011, Personal and Ubiquitous Computing.

[24]  C. Mallows,et al.  A Method for Comparing Two Hierarchical Clusterings , 1983 .

[25]  Sethuraman Panchanathan,et al.  Analysis of low resolution accelerometer data for continuous human activity recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  Oliver Brdiczka,et al.  Learning Situation Models in a Smart Home , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[28]  Gwenn Englebienne,et al.  An activity monitoring system for elderly care using generative and discriminative models , 2010, Personal and Ubiquitous Computing.

[29]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Jian Lu,et al.  An unsupervised approach to activity recognition and segmentation based on object-use fingerprints , 2010, Data Knowl. Eng..

[31]  Mohammed J. Zaki,et al.  PlanMine: Sequence Mining for Plan Failures , 1998, KDD.

[32]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.

[33]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[34]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.

[35]  J. Dunn.  Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[36]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[37]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[38]  Diane J. Cook,et al.  Learning Setting-Generalized Activity Models for Smart Spaces , 2012, IEEE Intelligent Systems.

[39]  Emmanuel Munguia Tapia,et al.  Toward Scalable Activity Recognition for Sensor Networks , 2006, LoCA.

[40]  Donald E. Brown,et al.  Health-status monitoring through analysis of behavioral patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[41]  Umeshwar Dayal,et al.  PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth , 2001, ICDE 2001.

[42]  Diane J. Cook,et al.  Improving home automation by discovering regularly occurring device usage patterns , 2003, Third IEEE International Conference on Data Mining.

[43]  Michalis Vazirgiannis,et al.  Clustering validity assessment: finding the optimal partitioning of a data set , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[44]  Aidong Zhang,et al.  Bridging centrality: graph mining from element level to group level , 2008, KDD.

[45]  Diane J. Cook,et al.  Human Activity Recognition and Pattern Discovery , 2010, IEEE Pervasive Computing.

[46]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[47]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[48]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[49]  Jian Pei,et al.  Constraint-based sequential pattern mining: the pattern-growth methods , 2007, Journal of Intelligent Information Systems.

[50]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[51]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[52]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .