AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities

We present AALO: a novel Activity recognition system for single person smart homes using Active Learning in the presence of Overlapped activities. AALO applies data mining techniques to cluster in-home sensor firings so that each cluster represents instances of the same activity. Users only need to label each cluster as an activity as opposed to labeling all instances of all activities. Once the clusters are associated to their corresponding activities, our system can recognize future activities. To improve the activity recognition accuracy, our system preprocesses raw sensor data by identifying overlapping activities. The evaluation of activity recognition performance on a 26-day dataset shows that compared to Naive Bayesian (NB), Hidden Markov Model (HMM), and Hidden Semi Markov Model (HSMM) based activity recognition systems, our average time slice error (24.15%) is much lower than NB (53.04%), and similar to HMM (29.97%) and HSMM (26.29%). Thus, our active learning based approach performs as good as the state of the art supervised techniques (HMM and HSMM).

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

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

[3]  Matthai Philipose,et al.  Building Reliable Activity Models Using Hierarchical Shrinkage and Mined Ontology , 2006, Pervasive.

[4]  Ying Zhang,et al.  SensCare: Semi-automatic Activity Summarization System for Elderly Care , 2011, MobiCASE.

[5]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

[6]  Bernt Schiele,et al.  Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning , 2009, LoCA.

[7]  Qiang Yang,et al.  CIGAR: Concurrent and Interleaving Goal and Activity Recognition , 2008 .

[8]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[9]  Deborah Estrin,et al.  Improving activity classification for health applications on mobile devices using active and semi-supervised learning , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[10]  Edwin Naroska,et al.  Unsupervised Recognition of ADLs , 2010, SETN.

[11]  Jian Lu,et al.  epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

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

[13]  Michael Eisenbarth,et al.  An approach for detecting deviations in daily routine for long-term behavior analysis , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[14]  Gwenn Englebienne,et al.  UvA-DARE ( Digital Academic Repository ) Activity recognition using semi-Markov models on real world smart home datasets , 2010 .

[15]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[16]  Chris D. Nugent,et al.  Using duration to learn activities of daily living in a smart home environment , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[17]  Henry A. Kautz,et al.  Guide: Towards Understanding Daily Life via Auto- Identification and Statistical Analysis , 2003 .

[18]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

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

[20]  Mark W. Newman,et al.  Automatic Assessment of Cognitive Impairment through Electronic Observation of Object Usage , 2010, Pervasive.

[21]  Diane J. Cook,et al.  Using Association Rule Mining to Discover Temporal Relations of Daily Activities , 2011, ICOST.

[22]  Gilles Virone.  Assessing everyday life behavioral rhythms for the older generation , 2009, Pervasive Mob. Comput..

[23]  David Wetherall,et al.  Recognizing daily activities with RFID-based sensors , 2009, UbiComp.

[24]  Huiru Zheng,et al.  Human Activity Detection in Smart Home Environment with Self-Adaptive Neural Networks , 2008, 2008 IEEE International Conference on Networking, Sensing and Control.

[25]  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.