Activity Recognition for the Digital Home

Yang, Jeonghwa; Schilit, Bill N.; McDonald, David W.

doi:10.1109/mc.2008.106

Cited by 19 publications

(4 citation statements)

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“…As an example, Yang, et al [19] collected computer usage information to recognize computer-based activities including multiplayer gaming, movie downloading, and music streaming. In addition, some researchers such as Brdiczka et al [20] video tape smart home residents and process the video to recognize activities.…”

Section: Activity Recognitionmentioning

confidence: 99%

Activity Discovery and Activity Recognition: A New Partnership

Cook

Krishnan

Rashidi

2013

IEEE Trans. Cybern.

203

143

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Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS datasets.

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Section: Activity Recognitionmentioning

confidence: 99%

Activity Discovery and Activity Recognition: A New Partnership

Cook

Krishnan

Rashidi

2013

IEEE Trans. Cybern.

203

143

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“…For recognition of specialized classes of activities researchers use more specialized sources of information. As an example, Yang, et al [24] collected computer usage information to recognize computer-based activities including multiplayer gaming, movie downloading, and music streaming. In addition, some researchers such as Brdiczka et al [25] video tape smart home residents and process the video to recognize activities.…”

Section: Related Workmentioning

confidence: 99%

Discovering Activities to Recognize and Track in a Smart Environment

Rashidi

Cook

Holder

et al. 2011

IEEE Trans. Knowl. Data Eng.

412

256

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The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been pre-selected and for which labeled training data is available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper we describe our activity mining and tracking approach and validate our algorithms on data collected in physical smart environments.

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“…Smart home is a platform of building [7], network appliances [8], communication [9], appliances automation [10], telemedicine, home office, entertainment, and other functions [11]. It provides a safe [12], convenience [13], energy saving [14], and friendly living environment [15], which includes system, structure, service, and management. Biometric access control is considered in smart home [16].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Wireless Sensor Networks System with Multiple Servers Communication

Ming

et al. 2015

International Journal of Distributed Sensor Networks

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For shortcomings of current smart home system and power line carrier insufficient data transferring, an intelligent sensor networks system based on intelligent gateway is studied in this paper. Smart home system includes internal network, intelligent gateway, and external network. Multiple servers are designed in gateway, which start at the same time and receive various forms of data. Then data is processed for internal network format. External network includes several modes of communication such as TCP/IP, UDP, and pipe. Internal network includes ZigBee wireless and power line carrier (PLC) communication. Power line carrier protocol is improved, and it expands data transmission capacity to accommodate communication needs of modern family. Smart home system is designed as modules, such as intelligent gateway module, power line carrier communication module, ZigBee wireless communication modules, and appliance within electric equipment. Finally this system is physically designed and verified. The smart home system introduced in this paper has easily used and has strong compatibility.

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Activity Recognition for the Digital Home

Cited by 19 publications

References 0 publications

Activity Discovery and Activity Recognition: A New Partnership

Activity Discovery and Activity Recognition: A New Partnership

Discovering Activities to Recognize and Track in a Smart Environment

An Intelligent Wireless Sensor Networks System with Multiple Servers Communication

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