Rhythm modeling, visualizations and applications

Abstract

People use their awareness of others' temporal patterns to plan work activities and communication. This paper presents algorithms for programatically detecting and modeling temporal patterns from a record of online presence data. We describe analytic and end-user visualizations of rhythmic patterns and the tradeoffs between them. We conducted a design study that explored the accuracy of the derived rhythm models compared to user perceptions, user preference among the visualization alternatives, and users' privacy preferences. We also present a prototype application based on the rhythm model that detects when a person is "away" for an extended period and predicts their return. We discuss the implications of this technology on the design of computer-mediated communication.

‘I Know That You Know’ - Ascertaining Mutual Awareness of Recipient’s Availability Status in Instant Messaging Applications

“…However, such manual solutions, although considered as rich and at the same time providing sufficient space for ambiguity, are imperfect: people tend to forget to update them when their situation changes [24,29,1]. Therefore, many works concentrated around designing systems deriving one's communicative state based on automatically detected availability cues [7,6,14,31,32,30]. Availability indications were provided through video-streaming [11,22], by representing the content of agendas or daily rhythms [7,6,32] or by showing computer activities and various sensory data captured from people's environments [13].…”

“…Therefore, many works concentrated around designing systems deriving one's communicative state based on automatically detected availability cues [7,6,14,31,32,30]. Availability indications were provided through video-streaming [11,22], by representing the content of agendas or daily rhythms [7,6,32] or by showing computer activities and various sensory data captured from people's environments [13]. Evaluations of many systems showed, however, that presenting availability status alone appears to be insufficient for screening unwanted interruptions [7,6,31].…”

A Practical Activity Capture Framework for Personal, Lifetime User Modeling

“…The intended modeling tasks we were targeting include building predictive models of user activity, identifying recurring patterns or routines in user behavior (as in [2]), identifying key collaborators or resources (as [13]), and aiding human memory through reminder and recall [11]. A description of using PLUM's activity logs for latent task analysis be found in [15].…”

“…A practical issue remaining, however, surrounds whether users can trust applications needing access to their protected activity logs; for this we are currently considering whether OS-kernel level data isolation and labelling approaches (such as those demonstrated in Asbestos [6]) could be applied. 2 …”

The Five Commandments of Activity-Aware Ubiquitous Computing Applications

“…In recent years, researchers have demonstrated the potential for extracting patterns from users' behavior by employing sensors [1][2][3]. There are various applications for detecting the user's activities.…”