Gesture Recognition Using mm-Wave Sensor for Human-Car Interface

被引:86
|
作者
Smith, Karly A. [1 ]
Csech, Clement [2 ]
Murdoch, David [3 ]
Shaker, George [3 ,4 ]
机构
[1] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada
[2] Univ Technol Compiegne, Dept Biomech & Bioengn, F-60200 Compiegne, France
[3] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[4] Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 301, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Microwave/millimeter wave sensors; human-car interface; 60 GHz mm-wave radar; gesture sensing; random forest classifier; machine learning;
D O I
10.1109/LSENS.2018.2810093
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article details the development of a gesture recognition technique using a mm-wave radar sensor for in-car infotainment control. Gesture recognition is becoming a more prominent form of human-computer interaction and can be used in the automotive industry to provide a safe and intuitive control interface that will limit driver distraction. We use a 60 GHz mm-wave radar sensor to detect precise features of fine motion. Specific gesture features are extracted and used to build a machine learning engine that can perform real-time gesture recognition. This article discusses the user requirements and in-car environmental constraints that influenced design decisions. Accuracy results of the technique are presented, and recommendations for further research and improvements are made.
引用
收藏
页数:4
相关论文
共 41 条
  • [1] RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar
    Arsalan, Muhammad
    Santra, Avik
    Issakov, Vadim
    IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2022, 70 (04) : 2451 - 2461
  • [2] mm-Wave Radar Based Gesture Recognition: Development and Evaluation of a Low-Power, Low-Complexity System
    Patra, Avishek
    Geuer, Philipp
    Munari, Andrea
    Maehoenen, Petri
    PROCEEDINGS OF THE 2ND ACM WORKSHOP ON MILLIMETER WAVE NETWORKS AND SENSING SYSTEMS (MMNETS'18), 2018, : 51 - 56
  • [3] Glucose Levels Detection Using mm-Wave Radar
    Omer, Ala Eldin
    Shaker, George
    Safavi-Naeini, Safieddin
    Murray, Kevin
    Hughson, Richard
    IEEE SENSORS LETTERS, 2018, 2 (03)
  • [4] RayPet: Unveiling Challenges and Solutions for Activity and Posture Recognition in Pets Using FMCW Mm-Wave Radar
    Sadeghi, Ehsan
    van Raalte, Abel
    Chiumento, Alessandro
    Havinga, Paul
    PROCEEDINGS OF NINTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, VOL 5, ICICT 2024, 2024, 1000 : 303 - 318
  • [5] IR-UWB Radar Sensor for Human Gesture Recognition by Using Machine Learning
    Park, Junbum
    Cho, Sung Ho
    PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS), 2016, : 1246 - 1249
  • [6] K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor
    Subburaj, Sathishkumar
    Yeh, Chih-Ho
    Patel, Brijesh
    Huang, Tsung-Han
    Hung, Wei-Song
    Chang, Ching-Yuan
    Wu, Yu-Wei
    Lin, Po Ting
    ELECTRONICS, 2023, 12 (01)
  • [7] Designing mm-wave electromagnetic engineered surfaces using generative adversarial networks
    Sanaz Mohammadjafari
    Ozan Ozyegen
    Mucahit Cevik
    Emir Kavurmacioglu
    Jonathan Ethier
    Ayse Basar
    Neural Computing and Applications, 2021, 33 : 11309 - 11323
  • [8] Designing mm-wave electromagnetic engineered surfaces using generative adversarial networks
    Mohammadjafari, Sanaz
    Ozyegen, Ozan
    Cevik, Mucahit
    Kavurmacioglu, Emir
    Ethier, Jonathan
    Basar, Ayse
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (17): : 11309 - 11323
  • [9] Trajectory Gesture Recognition Using Convolutional Neural Networks in Intelligent Teaching Interface
    Qiao, Yu
    Feng, Zhi-quan
    Zhou, Xiao-yan
    Xu, Tao
    Yang, Xiao-hui
    2018 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND NETWORK TECHNOLOGY (CCNT 2018), 2018, 291 : 458 - 464
  • [10] MM-Wave Radar-Based Recognition of Multiple Hand Gestures Using Long Short-Term Memory (LSTM) Neural Network
    Grobelny, Piotr
    Narbudowicz, Adam
    ELECTRONICS, 2022, 11 (05)