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: 1704899
• 负责人: Xiaofan Jiang
• 金额: $ 76.66万
• 依托单位: Columbia University
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704899&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.

在步行时使用智能手机为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，发短信还是说话，被智能手机吸收的行人都没有注意到重要的危险听觉提示，例如鸣叫和接近车辆的声音，使行人受到击中的风险更大。该项目旨在开发一种智能可穿戴系统，该系统使用微型麦克风（嵌入耳机或耳机中）来检测和找到接近的车辆，并警告佩戴者遇到汽车，公共汽车，摩托车，卡车和电车的危险。该系统包含多个麦克风嵌入到可穿戴耳机中，超低功能功能提取和数据处理管道以及一组在智能手机上运行的机器学习分类器。该项目在四个研究推力中组织：（1）设计一个由异质嵌入模块组成的可穿戴系统的建筑和数据处理管道； （2）设计超低功率，模拟，信号处理特定的集成电路（ASIC），以进行节能，板载功能提取； （3）建模和优化用于声学事件检测和本地化的机器学习分类器； （4）设计针对用户的感知，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，并扩展有关设计可穿戴系统以增强城市，工作场所和房屋安全性的知识。基础框架可以推广到其他系统 - 采用低功率信号处理器和算法来解决许多类型的实时感应和分类问题。将向任何人公开使用研究产品，以在自己的系统设计中应用这些技术。在嵌入式系统，移动计算和图案中开发的课程模块将在三所参与大学使用，以培训本科生和研究生，并将在线提供。

项目成果

CaNRun: Non-Contact, Acoustic-based Cadence Estimation on Treadmills using Smartphones

CaNRun：使用智能手机对跑步机进行非接触式、基于声学的步频估计

• DOI: 10.1145/3576914.3589561
• 发表时间: 2023
• 期刊: CPS-IoT Week '23: Proceedings of Cyber-Physical Systems and Internet of Things Week 2023
• 作者: Xuan, Ziyi;Liu, Ming;Nie, Jingping;Zhao, Minghui;Xia, Stephen;Jiang, Xiaofan
• 通讯作者: Jiang, Xiaofan

City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

• DOI: 10.1145/3341162.3349336
• 发表时间: 2019-09
• 期刊: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
• 作者: Peter Wei;Haocong Shi;Jiaying Yang;J. Qian;Yinan Ji;Xiaofan Jiang

PAMS: Improving Privacy in Audio-Based Mobile Systems

• DOI: 10.1145/3417313.3429383
• 发表时间: 2020-11
• 期刊: Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things
• 作者: S. Xia;Xiaofan Jiang

An Ultra-Low-Power Polarity-Coincidence Feedback Time-Delay-to-Digital Converter for Sound-Source Localization

用于声源定位的超低功耗极性符合反馈延时数字转换器

• DOI: 10.1109/jssc.2019.2950322
• 发表时间: 2020
• 期刊: IEEE Journal of Solid-State Circuits
• 影响因子: 5.4
• 作者: de Godoy, Daniel;Kinget, Peter R.
• 通讯作者: Kinget, Peter R.

Demo Abstract: An Ultra-Low-Power Custom Integrated Circuit based Sound-Source Localization System

演示摘要：基于超低功耗定制集成电路的声源定位系统

• 发表时间: 2018
• 期刊: IEEE/ACM International Conference on Internet-of-Things Design and Implementation
• 作者: de Godoy, Daniel;Xia, Stephen;Fernandez, Wendy;Jiang, Xiaofan;Kinget, Peter R
• 通讯作者: Kinget, Peter R