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—This paper describes a study on continuous, non-intrusive stress detection from physiological measurements, involving data collection, feature extraction, and model construction. We built a personalized stress detection model based on Support Vector Machines, and evaluated it on the collected data. Experimental results show that our model can detect stress(More)
This study evaluated parental engagement in an 8-week parenting program offered through daycare centers that were randomly assigned to a monetary incentive or nonincentive condition. Of an initial sample of 1,050 parents who rated their intent to enroll in the program, 610 went on to enroll-319 in the incentive and 291 in the nonincentive condition. Results(More)
We consider three aspects of the term episodic. Previous literature shows implicit memory does not make conscious autobiographical reference but does code an item's intrinsic context (e.g., perceptual detail). Here, we consider extrinsic context--namely, that not directly processed as part of item identification and not overtly relevant to the task.(More)
Interaction with mobile devices that are intended for everyday use is challenging since such systems are continuously optimized towards small outlines. Watches are a particularly critical as display size, processing capabilities, and weight are tightly constraint. This work presents a watch device with an integrated gesture recognition interface. We report(More)
We introduce the concept of a Virtual Coach (VC) for providing advice to manual wheelchair users to help them avoid damaging forms of locomotion. The primary form of context for this system is the user's propulsion pattern. The contexts of self vs. external propulsion and the surface over which propulsion is occurring can be used to improve the accuracy of(More)
We analyze the use of selective sampling strategies to aid in power conservation in sensor platforms for context-aware systems. In particular, we study an activity-aware system based on the eWatch sensor and notification platform, developed at CMU. We collected 94 hours of self-annotated activity data from four subjects over several days each. We compare(More)
Self-monitoring (SM) of food intake is central to weight loss treatment. Technology makes it possible to reinforce this behavior change strategy by providing real-time feedback (FB) tailored to the diary entry. To test the feasibility of providing 1-4 daily FB messages tailored to dietary recordings via a smartphone, we conducted a 12-week pilot randomized(More)