Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey

被引:4
|
作者
Bian, Sizhen [1 ]
Liu, Mengxi [2 ]
Zhou, Bo [2 ]
Lukowicz, Paul [2 ]
Magno, Michele [1 ]
机构
[1] Swiss Fed Inst Technol, Zurich, Switzerland
[2] DFKI, Kaiserslautern, Germany
关键词
Electric field sensing; capacitive sensing; body-area network; body-area sensing; human activity recognition; human machine interaction; wearable; SENSORS; DESIGN; LOCALIZATION; HEALTH; SKIN;
D O I
10.1145/3643555
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the fact that roughly sixty percent of the human body is essentially composed of water, the human body is inherently a conductive object, being able to, firstly, form an inherent electric field from the body to the surroundings and secondly, deform the distribution of an existing electric field near the body. Body-area capacitive sensing, also called body-area electric field sensing, is becoming a promising alternative for wearable devices to accomplish certain tasks in human activity recognition (HAR) and human-computer interaction (HCI). Over the last decade, researchers have explored plentiful novel sensing systems backed by the body-area electric field, like the ring-form smart devices for sign language recognition, the room-size capacitive grid for indoor positioning, etc. On the other hand, despite the pervasive exploration of the body-area electric field, a comprehensive survey does not exist for an enlightening guideline. Moreover, the various hardware implementations, applied algorithms, and targeted applications result in a challenging task to achieve a systematic overview of the subject. This paper aims to fill in the gap by comprehensively summarizing the existing works on body-area capacitive sensing so that researchers can have a better view of the current exploration status. To this end, we first sorted the explorations into three domains according to the involved body forms: body-part electric field, whole-body electric field, and body-to-body electric field, and enumerated the state-of-art works in the domains with a detailed survey of the backed sensing tricks and targeted applications. We then summarized the three types of sensing frontends in circuit design, which is the most critical part in body-area capacitive sensing, and analyzed the data processing pipeline categorized into three kinds of approaches. The outcome will benefit researchers for further body-area electric field explorations. Finally, we described the challenges and outlooks of body-area electric sensing, followed by a conclusion, aiming to encourage researchers to further investigations considering the pervasive and promising usage scenarios backed by body-area capacitive sensing.
引用
收藏
页数:49
相关论文
共 50 条
  • [21] Vision-Based Hand Gesture Recognition for Human-Computer Interaction——A Survey
    GAO Yongqiang
    LU Xiong
    SUN Junbin
    TAO Xianglin
    HUANG Xiaomei
    YAN Yuxing
    LIU Jia
    Wuhan University Journal of Natural Sciences, 2020, 25 (02) : 169 - 184
  • [22] Interaction and Resistance: The Recognition of Intentions in New Human-Computer Interaction
    Mueller, Vincent C.
    TOWARD AUTONOMOUS, ADAPTIVE, AND CONTEXT-AWARE MULTIMODAL INTERFACES: THEORETICAL AND PRACTICAL ISSUES, 2011, 6456 : 1 - 7
  • [23] Empowering Human-Computer Interaction in Securing Smartphone Sensing
    Rauen, Zachary
    Anjomshoa, Fazel
    Kantarci, Burak
    2018 IEEE 23RD INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS (CAMAD), 2018, : 266 - 271
  • [24] A hand gesture recognition technique for human-computer interaction
    Kiliboz, Nurettin Cagri
    Gudukbay, Ugur
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2015, 28 : 97 - 104
  • [25] Gender and gaze gesture recognition for human-computer interaction
    Zhang, Wenhao
    Smith, Melvyn L.
    Smith, Lyndon N.
    Farooq, Abdul
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2016, 149 : 32 - 50
  • [26] Intentional Microgesture Recognition for Extended Human-Computer Interaction
    Kandoi, Chirag
    Jung, Changsoo
    Mannan, Sheikh
    VanderHoeven, Hannah
    Meisman, Quincy
    Krishnaswamy, Nikhil
    Blanchard, Nathaniel
    HUMAN-COMPUTER INTERACTION, HCI 2023, PT I, 2023, 14011 : 499 - 518
  • [27] The Applications of Facial Expression Recognition in Human-computer Interaction
    Wang, Huan-Huan
    Gu, Jing-Wei
    PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON ADVANCED MANUFACTURING (IEEE ICAM), 2018, : 288 - 291
  • [28] Recognition of Emotional states in Natural Human-Computer Interaction
    Milanova, Mariofanna
    Sirakov, Nikolay
    ISSPIT: 8TH IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, 2008, : 186 - +
  • [29] Human-computer interaction system based on gesture recognition
    Li, Wei
    Zhang, Honglei
    Zhang, Zhilong
    Li, Chuwei
    SECOND INTERNATIONAL CONFERENCE ON OPTICS AND IMAGE PROCESSING (ICOIP 2022), 2022, 12328
  • [30] Face and hand gesture recognition for human-computer interaction
    Hongo, H
    Ohya, M
    Yasumoto, M
    Yamamoto, K
    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS, 2000, : 921 - 924