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.
However several shortcomings in these approaches, as outlined below, prevent them from being effectively used in pedestrian safety applications. For instance, 's work @cite_5 requires the system to keep track of time segments that precede and follow the current one, and thus, unsuitable for pedestrian safety applications that require real-time operation and feedback. Others are not suitable for implementation on resource-constrained mobile and wearable devices, primarily due to their use of complex feature sets and classification functions. As discussed before, one of the main functional requirement for a mobile wearable device based CAR framework for pedestrian safety is computational and energy efficiency. Earlier research efforts in energy-aware recognition mechanisms @cite_10 have achieved a favorable balance between classification accuracy and energy consumption, but these schemes have been successful in recognizing only simple activities, such as standing, walking and sitting, but not concurrent activities. Recently, @cite_17 proposed an energy-aware CAR framework for real-time applications by using a minimal feature set to recognize individual data segments and a hierarchical classification mechanism for concurrent activity recognition. However, their framework employs a specialized wearable device hardware, and may not work for commercial off-the-shelf mobile devices thus making them less likely to be adopted by users.
@cite_5 @cite_10 @cite_17
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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. This paper presents an energy-aware method for recognizing time series acceleration data containing both activities and gestures using a wearable device coupled with a smartphone. In our method, we use a small wearable device to collect accelerometer data from a user's wrist, recognizing each data segment using a minimal feature set chosen automatically for that segment. For each collected data segment, if our model finds that recognizing the segment requires high-cost features that the wearable device cannot extract, such as dynamic time warping for gesture recognition, then the segment is transmitted to the smartphone where the high-cost features are extracted and recognition is performed. Otherwise, only the minimum required set of low-cost features are extracted from the segment on the wearable device and only the recognition result, i.e., label, is transmitted to the smartphone in place of the raw data, reducing transmission costs. Our method automatically constructs this adaptive processing pipeline solely from training data.