Feature extraction for human activity recognition on streaming data

An online recognition system must analyze the changes in the sensing data and at any significant detection; it has to decide if there is a change in the activity performed by the person. Such a system can use the previous sensor readings for decision-making (decide which activity is performed), without the need to wait for future ones. This paper proposes an approach of human activity recognition on online sensor data. We present four methods used to extract features from the sequence of sensor events. Our experimental results on public smart home data show an improvement of effectiveness in classification accuracy.

[1]  Martha E. Pollack,et al.  Autominder: an intelligent cognitive orthotic system for people with memory impairment , 2003, Robotics Auton. Syst..

[2]  Ignas G. Niemegeers,et al.  On the Stability of Ad Hoc Group Mobility Models , 2009, 2009 IEEE International Conference on Communications.

[3]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[4]  Javier Bajo,et al.  Using Heterogeneous Wireless Sensor Networks in a Telemonitoring System for Healthcare , 2010, IEEE Transactions on Information Technology in Biomedicine.

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

[6]  Svetha Venkatesh,et al.  Efficient duration and hierarchical modeling for human activity recognition , 2009, Artif. Intell..

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

[8]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[9]  Kent Larson,et al.  Using a Live-In Laboratory for Ubiquitous Computing Research , 2006, Pervasive.

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

[11]  Christopher G. Atkeson,et al.  Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors , 2005, Pervasive.

[12]  Bernt Schiele,et al.  Towards Less Supervision in Activity Recognition from Wearable Sensors , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[13]  Gerhard Tröster,et al.  Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography , 2009, Pervasive.

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

[15]  Bernt Schiele,et al.  Scalable Recognition of Daily Activities with Wearable Sensors , 2007, LoCA.

[16]  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.

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

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