A Practical Framework for Preventing Distracted Pedestrian-Related Incidents Using Wrist Wearables
A Practical Framework for Preventing Distracted Pedestrian-Related Incidents Using Wrist Wearables
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DOI：
10.1109/access.2018.2884669
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发表时间：
2018-11
期刊：
影响因子：
3.9
通讯作者：
Nisha Vinayaga-Sureshkanth;Anindya Maiti;Murtuza Jadliwala;Kirsten Crager;Jibo He;Heena Rathore
中科院分区：
文献类型：
--
作者：
Nisha Vinayaga-Sureshkanth;Anindya Maiti;Murtuza Jadliwala;Kirsten Crager;Jibo He;Heena Rathore
分心的行人与分心的司机类似，是城市社区行人事故的日益危险的威胁和先兆，往往会导致重伤和死亡。要减轻这种对行人安全的危害，需要使用能够有效检测到这些危险的行人安全系统和应用程序。设计有效的行人安全框架是可能的，因为有了先进的移动和可穿戴设备，这些设备配备了高精度的车载传感器，能够捕获细粒度的用户动作和背景，特别是分心的活动。然而，考虑到这些设备的内存、计算和通信限制，此类系统设计中的关键技术挑战是以最少的资源实时准确识别干扰。最近发表的几篇论文利用移动和可穿戴传感器数据的复杂活动识别框架来检测行人活动。然而，这些努力的主要重点是实现高检测精度，因此大多数设计要么是资源密集型的，不适合在主流移动设备上实现，要么是计算速度慢，不适用于实时行人安全应用，需要专门的硬件，不太可能被大多数用户采用。在寻求行人安全系统的过程中，我们设计了一种高效、实时的行人分心检测技术，克服了(现有技术的)一些缺点。我们通过在商用移动和可穿戴设备上实现原型并使用从真实行人实验中收集的人类受试者的数据来评估所提出的技术的实用性。通过这些评估，我们表明，与本文中的其他一些技术相比，我们的技术在计算效率、检测精度和能量消耗之间取得了良好的平衡。
Distracted pedestrians, akin to their distracted driver counterparts, 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 the employment of pedestrian safety systems and applications that are effective in detecting them. Designing effective pedestrian safety frameworks is possible with the availability of sophisticated mobile and wearable devices that are equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge in the design of such systems is accurate recognition of distractions with minimal resources in real-time, given the memory, computation, and communication limitations of these devices. Several recently published papers detect pedestrian activities by leveraging on complex activity recognition frameworks using mobile and wearable sensor data. The primary focus of these efforts, however, was on achieving 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, 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 the shortcomings (of existing techniques). We demonstrate the practicality of the proposed technique by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from human subject participants in realistic pedestrian experiments. By means of 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 this paper.