SenSay: a context-aware mobile phone

By D. Siewiorek, Asim Smailagic, Junichi Furukawa, Andreas Krause, N. Moraveji, Kathryn Reiger, J. Shaffer, Fei Lung Wong. Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings., 2003

371 citations recorded by the source corpus.

SenSay is a context-aware mobile phone that adapts to dynamically changing environmental and physiological states. In addition to manipulating ringer volume, vibration, and phone alerts, SenSay can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of the SenSay user. A number of sensors including accelerometers, light, and microphones are mounted at various points on the body to provide data about the user’s context. A decision module uses a set of rules to analyze the sensor data and manage a state machine composed of uninterruptible, idle, active and normal states. Results from our threshold analyses show a clear delineation can be made among several user states by examining sensor data trends. SenSay augments its contextual knowledge by tapping into applications such as electronic calendars, address books, and task lists.

Subfields: Computer Vision and Pattern Recognition, Information Systems and Management, Transportation

View Paper: https://www.semanticscholar.org/paper/a065d87401c047ed53a61d33dc92145979339972

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Citations in the corpus

A framework of energy efficient mobile sensing for automatic user state recognition (2009, background): “Sensay requires input from an external sensor box which is mounted on the user s hip area and the system design does not have energy e.ciency concern.”
Personal privacy through understanding and action: five pitfalls for designers (2004, background): “1 Evidence: falling into the pitfall Some researchers envision context-aware mobile phones that disclose the user’s activity to the caller to help explain why their call was not answered [46].”
Using context-aware computing to reduce the perceived burden of interruptions from mobile devices (2005, background): “vibration, ringer) based on the activity of the user and the social expectation of group behavior (detected using light, accelerometers, and microphones) [26].”
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors (2012, background): “, inferring if the user is walking, sitting, or running [36, 37]).”
Preprocessing techniques for context recognition from accelerometer data (2010, methodology): “Differences [55] Very low Very low Single/int Yes”
A survey on privacy in mobile participatory sensing applications (2011, background): “Similarly to MobAsthma, medical staff may be directly alerted via the mobile phones in case of abnormal behavior or when users fall with SenSay [19].”