Toward active sensor placement for activity recognition

Authors:
Paul M. Yanik;Joe Manganelli;Linnea Smolentzov;Jessica Merino;Ian D. Walker;Johnell O. Brooks;Keith E. Green
Affiliations:
Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina;School of Architecture, Clemson University, Clemson, South Carolina;Department of Psychology, Clemson University, Clemson, South Carolina;Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina;Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina;Department of Psychology, Clemson University, Clemson, South Carolina;School of Architecture, Clemson University, Clemson, South Carolina
Venue:
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
Year:
2011

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Abstract

The development of ubiquitous sensing strategies in home environments underpins the promise of adaptive architectural design, assistive robotics, and services which would support a persons ability to live independently as they age. In particular, the ability to infer the actions, behavioral patterns and preferences of the individual from sensor data is key to effective design of such components for aging in place. Very often, sensing for recognition of human activity utilizes vision based sensors. However, it has been seen that many home users find the presence of cameras to be invasive. Hence, we seek to develop a sensing system which uses non-vision based sensors to discreetly discern occupant position, activity, and user context in the home environment. This paper describes initial experimentation to determine optimal sensor placement for detection of specific activities. Three essential reaching motions typical of individuals lying in bed are examined. Action data was collected using IR motion sensors positioned at an array of vantage points on a virtual sphere surrounding the motion space. Histograms of Oriented Gradients (HOGs) are used to extract motion representations from Self-Similarity Matrices (SSMs) for each action. It is shown that mean HOGs can serve as exemplars and allow us to choose a preferred sensor position for each motion type. Using these exemplars, motions can be classified with a promising level of accuracy ( 75% for our data set), with improved outcomes observed through aggregation of sensor readings.