Distracted pedestrians, like distracted drivers, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing such frameworks is possible with the availability of sophisticated mobile and wearable devices equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge is accurate recognition of distractions with minimal resources in real-time given the computation and communication limitations of these devices. Several recently published works improve distracted pedestrian safety by leveraging on complex activity recognition frameworks using mobile and wearable sensors to detect pedestrian distractions. Their primary focus, however, was to achieve high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient and real-time pedestrian distraction detection technique that overcomes some of these shortcomings. We demonstrate its practicality by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from participants in realistic pedestrian experiments. Using these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy and energy consumption compared to some other techniques in the literature.
The problem of detecting distracted pedestrians can be generalized as a concurrent activity recognition or CAR problem where the goal is to detect concurrent pedestrian activities of being mobile (e.g., walking, running or climbing descending stairs) and being distracted (e.g., texting, eating or reading). CAR techniques that can distinguish different combinations of elementary activities have been extensively used in the literature for complex human activity recognition. For instance, @cite_25 @cite_5 used and data, from two smartphones, one in trouser pocket and the other on the wrist, to recognize activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. @cite_9 also employed multi-sensor time series data to recognize sequential, concurrent, and generic complex activities by building a dictionary of the time series patterns (called ) to represent atomic activities.
@cite_5 @cite_9 @cite_25
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Smoking is known to be one of the main causes for premature deaths. A reliable smoking detection method can enable applications for an insight into a user's smoking behaviour and for use in smoking cessation programs. However, it is difficult to accurately detect smoking because it can be performed in various postures or in combination with other activities, it is less-repetitive, and it may be confused with other similar activities, such as drinking and eating. In this paper, we propose to use a two-layer hierarchical smoking detection algorithm (HLSDA) that uses a classifier at the first layer, followed by a lazy context-rule-based correction method that utilizes neighbouring segments to improve the detection. We evaluated our algorithm on a dataset of 45 hours collected over a three month period where 11 participants performed 17 hours (230 cigarettes) of smoking while sitting, standing, walking, and in a group conversation. The rest of 28 hours consists of other similar activities, such as eating, and drinking. We show that our algorithm improves recall as well as precision for smoking compared to a single layer classification approach. For smoking activity, we achieve an F-measure of 90-97 in person-dependent evaluations and 83-94 in person-independent evaluations. In most cases, our algorithm corrects up to 50 of the misclassified smoking segments. Our algorithm also improves the detection of eating and drinking in a similar way. We make our dataset and data logger publicly available for the reproducibility of our work. The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.