CSR: CHS: Medium: Collaborative Research: Improving Pedestrian Safety in Urban Cities using Intelligent Wearable Systems

CSR：CHS：中：合作研究：利用智能可穿戴系统提高城市行人安全

• 批准号: 1704914
• 负责人: Josh New
• 金额: $ 12.59万
• 依托单位: Barnard College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704914&HistoricalAwards=false
• 关键词: CSR; CHS; Medium; Collaborative; Research

Using smartphones while walking poses an increasingly common safety problem for people in urban environments. Whether listening to music, texting, or talking, pedestrians that are absorbed with their smartphones are considerably less likely to notice important auditory cues of danger, such as the honks and sounds of approaching vehicles, putting pedestrians at far greater risk of being hit. This project aims to develop an intelligent wearable system that uses miniature microphones - embedded in earphones or headsets - to detect and locate approaching vehicles and warn the wearer of imminent dangers from cars, buses, motorbikes, trucks, and trams. The system comprises multiple microphones embedded in a wearable headset, an ultra-low-power feature extraction and data processing pipeline, and a set of machine-learning classifiers running on a smartphone. This project is organized in four research thrusts: (1) designing an architecture and data processing pipeline for a wearable system composed of heterogeneous embedded modules; (2) devising an ultra-low-power, analog, signal-processing application-specific integrated circuit (ASIC) for energy-efficient, on-board feature extraction; (3) modeling and optimizing machine-learning classifiers for acoustic event detection and localization; and (4) designing an interface and feedback mechanisms that are optimized for the users' perceptual, cognitive, and motor control abilities.This research will help reduce pedestrian injuries and fatalities, and expand knowledge on designing wearable systems for enhancing safety in cities, workplaces, and the home. The underlying framework can be generalized to other systems - employing low-power signal processors and algorithms to solve real-time sensing and classification problems of many kinds. Research products will be made publicly available to anyone to apply these techniques in their own system designs. Course modules developed on embedded systems, mobile computing, and Internet-of-Things will be used at the three participating universities to train undergraduate and graduate students, and will be made available online.

在城市环境中，走路时使用智能手机给人们带来了越来越普遍的安全问题。无论是听音乐、发短信还是说话，全神贯注于智能手机的行人都不太可能注意到重要的危险听觉线索，比如喇叭声和驶近车辆的声音，这使得行人被撞的风险要大得多。该项目旨在开发一种智能可穿戴系统，该系统使用嵌入耳机或耳机中的微型麦克风来探测和定位接近的车辆，并警告佩戴者汽车、公共汽车、摩托车、卡车和有轨电车即将到来的危险。该系统包括嵌入在可穿戴耳机中的多个麦克风，超低功耗特征提取和数据处理管道，以及一套运行在智能手机上的机器学习分类器。本项目分为四个研究方向：(1)设计异构嵌入式模块组成的可穿戴系统的架构和数据处理管道；(2)设计一种超低功耗、模拟、信号处理专用集成电路（ASIC），实现节能、板载特征提取；(3)建模和优化用于声事件检测和定位的机器学习分类器；(4)设计针对用户感知、认知和运动控制能力进行优化的界面和反馈机制。这项研究将有助于减少行人伤害和死亡，并扩大设计可穿戴系统的知识，以提高城市、工作场所和家庭的安全。底层框架可以推广到其他系统-采用低功耗信号处理器和算法来解决多种类型的实时传感和分类问题。研究成果将公开提供给任何人，以便在他们自己的系统设计中应用这些技术。在嵌入式系统、移动计算和物联网方面开发的课程模块将在三所参与大学用于培养本科生和研究生，并将在网上提供。