Recognizing the Use of Portable Electrical Devices with Hand-Worn Magnetic Sensors

The new method proposed here recognizes the use of portable electrical devices such as digital cameras, cellphones, electric shavers, and video game players with hand-worn magnetic sensors by sensing the magnetic fields emitted by these devices. Because we live surrounded by large numbers of electrical devices and frequently use these devices, we can estimate high-level daily activities by recognizing the use of electrical devices. Therefore, many studies have attempted to recognize the use of electrical devices with such approaches as ubiquitous sensing and infrastructure-mediated sensing. A feature of our method is that we can recognize the use of electrical devices that are not connected to the home infrastructure without the need for any ubiquitous sensors attached to the devices. We evaluated the performance of our recognition method in real home environments, and confirmed that we could achieve highly accurate recognition with small numbers of hand-worn magnetic sensors.

[1]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[2]  Ian Witten,et al.  Data Mining , 2000 .

[3]  Takuya Maekawa,et al.  Object-Blog System for Environment-Generated Content , 2008, IEEE Pervasive Computing.

[4]  Paul Lukowicz,et al.  Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers , 2004, Pervasive.

[5]  Eric C. Larson,et al.  HydroSense: infrastructure-mediated single-point sensing of whole-home water activity , 2009, UbiComp.

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

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

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

[9]  Eric C. Larson,et al.  GasSense: Appliance-Level, Single-Point Sensing of Gas Activity in the Home , 2010, Pervasive.

[10]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[11]  Alex Pentland,et al.  InSense: Interest-Based Life Logging , 2006, IEEE MultiMedia.

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

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

[14]  Takuya Maekawa,et al.  Object-Based Activity Recognition with Heterogeneous Sensors on Wrist , 2010, Pervasive.

[15]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[16]  J. Herbertz Comment on the ICNIRP guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz) , 1998, Health physics.

[17]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[18]  David W. Murray,et al.  Wearable hand activity recognition for event summarization , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[19]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[20]  John Krumm,et al.  UbiComp 2007: Ubiquitous Computing, 9th International Conference, UbiComp 2007, Innsbruck, Austria, September 16-19, 2007, Proceedings , 2007, UbiComp.

[21]  Mani B. Srivastava,et al.  ViridiScope: design and implementation of a fine grained power monitoring system for homes , 2009, UbiComp.

[22]  J. Lenz A review of magnetic sensors , 1990, Proc. IEEE.