Monitoring Behavioral Transitions in Cognitive Rehabilitation with Multi-Model, Multi-Window Stream Mining

Syed

2012 45th Hawaii International Conference on System Sciences

et al. 2012

Self Cite

We demonstrate the use of sequence pattern mining as applied to monitoring the usage of emailing software by clients with cognitive impairments. We show how Max Motif, a sequence-mining algorithm, can be applied using a stream-mining approach. Consequently, clinicians can now consider sequential patterns in their analysis of client emailing behaviors. Such analysis is part of the Think and Link project, which provides personalized email clients, a kind of assistive technology, to aid client communications that facilitate activities of daily living. By using the simplified, customized system, clients can now email, whereas they could not previously. By continuously monitoring usage, the project is able continually adapt the software to a user's changing needs. Thus, monitoring software usage, particularly email event sequences, is important. This paper introduces theory and design of stream sequence-mining for UI event streams.

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“…They are consistent with transitions in Don's emailing behavior, previously identified and diagnosed [8,9].…”

Section: Sequence Analysissupporting

confidence: 88%

Section: Preprocessing and Miningmentioning

confidence: 99%

Section: Goal Attainment Scalesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pattern Discovery of User Interface Sequencing by Rehabilitation Clients with Cognitive Impairments

Syed

2012 45th Hawaii International Conference on System Sciences

et al. 2012

Self Cite

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“…We can also consider how quickly the predictive quality changes, which is q . An analysis of typical domain values for q and q can provide guidelines that distinguish normal behavioral variations from significant behavioral changes [Robinson and Akhlaghi 2010].…”

Section: Modelmentioning

confidence: 99%

Discovery and diagnosis of behavioral transitions in patient event streams

ACM Trans. Manage. Inf. Syst.

Deng

et al. 2012

Self Cite

Users with cognitive impairments use assistive technology (AT) as part of a clinical treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, realtime data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When the quality of some data-mined models varies significantly from nearby models-as defined by quality metrics-the user's behavior is then flagged as a significant behavioral change. The specific changes in user behavior are then characterized by differencing the data-mined decision tree models. This article describes how model quality monitoring and decision tree differencing can aid in recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other real-time data-intensive analysis problems.

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Diagnosing Stream-Mined Model Changes of Monitored Requirements for Cognitive Rehabilitation

2011 44th Hawaii International Conference on System Sciences

Deng

et al. 2011