2019英特爾ISEF學科最佳作品摘要機器人與智能機器（人工智能）Best of Category Abstract: ROBOTICS AND INTELLIGENT MACHINES

Category: STEM國際競賽, 國際競賽 Date: 2019年9月2日下午2:14

獲獎作品基本信息

年份	2018
學科	機器人與智能機器 ROBOTICS AND INTELLIGENT MACHINES
國家/州	TX, United States of America

獲獎作品名稱

Looking through Walls with Artificial Intelligence: An Innovative Solution for Real-Time Retrieval of the Human Figure behind Visual Obstruction

獲獎作品摘要

Overcoming the visual barrier and developing “see-through vision” has been one of mankind’s long-standing dreams. Unlike visible light, Radio Frequency (RF) signals penetrate opaque obstructions and reflect highly off humans. This project created a breakthrough artificial intelligence model that can be trained to reconstruct continuous video of a 15-point human skeleton even through visual occlusion using RF signals. The AI training process adopted a student/teacher learning procedure inspired by the Feynman Technique for Learning. Video frames and RF data were first collected simultaneously using a co-located setup containing an optical camera and an RF antenna array transceiver. Next, the video frames were processed with a computer-vision-based gait analysis “teacher” module to generate ground-truth human skeletons for each frame. Then, the same type of skeleton was predicted from corresponding RF data using a “student” deep-learning model consisting of a Residual Convolutional Neural Network (CNN), Region Proposal Network (RPN), and Recurrent Neural Network with Long-Short Term Memory (RNN-LSTM) that 1) extracted spatial features from RF images, 2) detected all people present in a scene, and 3) aggregated information over multiple time-steps, respectively. After reducing error between teacher-provided ground-truths and student-created predictions over thousands of training iterations, the model was shown to be capable of accurately and completely predicting the pose of any human behind visual obstruction solely using RF signals. Primary academic contributions include the novel many-to-many imaging methodology, unique RPN/RNN-LSTM integration, newly proposed objective function, and original training pipeline. A simulator was also created.

高中生科研英特爾 Intel ISEF
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高中生科研學術活動英特爾 Intel ISEF 簡介

英特爾國際科學與工程大獎賽，簡稱 "ISEF"，由美國 Society for Science and the Public（科學和公共服務協會）主辦，英特爾公司冠名贊助，是全球規模最大、等級最高的中學生的科研科創賽事。ISEF 的學術活動學科包括了所有數學、自然科學、工程的全部領域和部分社會科學。ISEF 素有全球青少年科學學術活動的“世界杯”之美譽，旨在鼓勵學生團隊協作，開拓創新，長期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學術活動詳細介紹

英特爾 ISEF 全程指導方案

· 數學 · 物理 · 化學 · 生物 · 計算機 · 工程 ·

學科簡介：機器人與智能機器 ROBOTICS AND INTELLIGENT MACHINES

Studies in which the use of machine intelligence is paramount to reducing the reliance on human intervention.

Subcategories:

Biomechanics?(BIE):?Studies and apparatus which mimic the role of mechanics in biological systems.

Cognitive Systems?(COG):?Studies/apparatus that operate similarly to the ways humans think and process information. Systems that provide for increased interaction of people and machines to more naturally extend and magnify human expertise, activity, and cognition.

Control Theory?(CON):?Studies that explore the behavior of dynamical systems with inputs, and how their behavior is modified by feedback. ?This includes new theoretical results and the applications of new and established control methods, system modelling, identification and simulation, the analysis and design of control systems (including computer-aided design), and practical implementation.

Machine Learning?(MAC):?Construction and/or study of algorithms that can learn from data.

Robot Kinematics?(KIN):?The study of movement in robotic systems.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories.?If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.

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