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Context plays a key role in impulsive adverse behaviors such as fights, suicide attempts, binge-drinking, and smoking lapse. Several contexts dissuade such behaviors, but some may trigger adverse impulsive behaviors. We define these latter contexts as 'opportunity' contexts, as their passive detection from sensors can be used to deliver context-sensitive interventions. In this paper, we define the general concept of 'opportunity' contexts and apply it to the case of smoking cessation. We operationalize the smoking 'opportunity' context, using self-reported smoking allowance and cigarette availability. We show its clinical utility by establishing its association with smoking occurrences using Granger causality. Next, we mine several informative features from GPS traces, including the novel location context of smoking spots, to develop the SmokingOpp model for automatically detecting the smoking 'opportunity' context. Finally, we train and evaluate the SmokingOpp model using 15 million GPS points and 3,432 self-reports from 90 newly abstinent smokers in a smoking cessation study. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods;

We address the open problem of reliably detecting oral health behaviors passively from wrist-worn inertial sensors. We present our model named mORAL (pronounced em oral) for detecting brushing and flossing behaviors, without the use of instrumented toothbrushes so that the model is applicable to brushing with still prevalent manual toothbrushes. We show that for detecting rare daily events such as toothbrushing, adopting a model that is based on identifying candidate windows based on events, rather than fixed-length timeblocks, leads to significantly higher performance. Trained and tested on 2,797 hours of sensor data collected over 192 days on 25 participants (using video annotations for ground truth labels), our brushing model achieves 100% median recall with a false positive rate of one event in every nine days of sensor wearing. The average error in estimating the start/end times of the detected event is 4.1% of the interval of the actual toothbrushing event. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods;

Ensuring that all the teeth surfaces are adequately covered during daily brushing can reduce the risk of several oral diseases. In this paper, we propose the mTeeth model to detect teeth surfaces being brushed with a manual toothbrush in the natural free-living environment using wrist-worn inertial sensors. To unambiguously label sensor data corresponding to different surfaces and capture all transitions that last only milliseconds, we present a lightweight method to detect the micro-event of brushing strokes that cleanly demarcates transitions among brushing surfaces. Using features extracted from brushing strokes, we propose a Bayesian Ensemble method that leverages the natural hierarchy among teeth surfaces and patterns of transition among them. For training and testing, we enrich a publicly-available wrist-worn inertial sensor dataset collected from the natural environment with time-synchronized precise labels of brushing surface timings and moments of transition. We annotate 10,230 instances of brushing on different surfaces from 114 episodes and evaluate the impact of wide between-person and within-person between-episode variability on machine learning model's performance for brushing surface detection.