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

走路时使用智能手机给城市环境中的人们带来了越来越常见的安全问题。无论是听音乐、发短信还是聊天，沉浸在智能手机中的行人都不太可能注意到重要的危险听觉信号，如驶近的车辆的喇叭和声音，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的微型麦克风来检测和定位驶近的车辆，并警告佩戴者来自汽车、公共汽车、摩托车、卡车和有轨电车的迫在眉睫的危险。该系统包括嵌入可穿戴耳机的多个麦克风、一个超低功耗特征提取和数据处理流水线，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)设计一种由不同嵌入式模块组成的可穿戴系统的体系结构和数据处理流水线；(2)设计一种超低功耗、模拟、信号处理专用集成电路(ASIC)，以实现高能效的板载特征提取；(3)对机器学习分类器进行建模和优化，以实现声事件检测和定位；(4)设计界面和反馈机制，优化用户的感知、认知和运动控制能力。这项研究将有助于减少行人的伤亡，并扩大在设计可穿戴系统方面的知识，以增强城市、工作场所和家庭的安全性。底层框架可以推广到其他系统-使用低功率信号处理器和算法来解决许多类型的实时检测和分类问题。研究产品将向任何人公开，以便在他们自己的系统设计中应用这些技术。嵌入式系统、移动计算和物联网开发的课程模块将在三所参与的大学用于培养本科生和研究生，并将在网上提供。

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.