Radar and RGB-Depth Sensors for Fall Detection: A Review

被引:136
|
作者
Cippitelli, Enea [1 ]
Fioranelli, Francesco [2 ]
Gambi, Ennio [1 ]
Spinsante, Susanna [1 ]
机构
[1] Univ Politecn Marche, Dept Informat Engn, I-60131 Ancona, Italy
[2] Univ Glasgow, Sch Engn, Glasgow G12 8QQ, Lanark, Scotland
关键词
Radar sensors; RGB-D sensors; micro-Doppler; fall detection; human movements analysis; ambient assisting living; feature extraction and classification; MICRO-DOPPLER CLASSIFICATION; FEATURE-SELECTION; WEARABLE SENSORS; HUMAN MOVEMENT; DATA FUSION; RECOGNITION; SIGNATURES; SYSTEM; INFORMATION; FEATURES;
D O I
10.1109/JSEN.2017.2697077
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 paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users' acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.
引用
收藏
页码:3585 / 3604
页数:20
相关论文
共 50 条
  • [21] Depth Image Rectification Based on an Effective RGB-Depth Boundary Inconsistency Model
    Cao, Hao
    Zhao, Xin
    Li, Ang
    Yang, Meng
    [J]. ELECTRONICS, 2024, 13 (16)
  • [22] FloW Vision: Depth Image Enhancement by Combining Stereo RGB-Depth Sensor
    Waskitho, Suryo Aji
    Alfarouq, Ardiansyah
    Sukaridhoto, Sritrusta
    Pramadihanto, Dadet
    [J]. 2016 INTERNATIONAL CONFERENCE ON KNOWLEDGE CREATION AND INTELLIGENT COMPUTING (KCIC), 2016, : 182 - 187
  • [23] RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
    Wang, Haowen
    Che, Zhengping
    Yang, Yufan
    Wang, Mingyuan
    Xu, Zhiyuan
    Qiao, Xiuquan
    Qi, Mengshi
    Feng, Feifei
    Tang, Jian
    [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46 (11) : 7088 - 7101
  • [24] CMIGNet: Cross-Modal Inverse Guidance Network for RGB-Depth salient object detection
    Zhu, Hegui
    Ni, Jia
    Yang, Xi
    Zhang, Libo
    [J]. PATTERN RECOGNITION, 2024, 155
  • [25] Human Activities Recognition with RGB-Depth Camera using HMM
    Dubois, Amandine
    Charpillet, Francois
    [J]. 2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2013, : 4666 - 4669
  • [26] 利用RGB-Depth相机的机械模型建模
    林帅
    程志全
    [J]. 系统仿真学报, 2013, 25 (09) : 2044 - 2049
  • [27] A guided-based approach for deepfake detection: RGB-depth integration via features fusion
    Leporoni, Giorgio
    Maiano, Luca
    Papa, Lorenzo
    Amerini, Irene
    [J]. Pattern Recognition Letters, 2024, 181 : 99 - 105
  • [28] A guided-based approach for deepfake detection: RGB-depth integration via features fusion
    Leporoni, Giorgio
    Maiano, Luca
    Papa, Lorenzo
    Amerini, Irene
    [J]. PATTERN RECOGNITION LETTERS, 2024, 181 : 99 - 105
  • [29] Precision mapping through an RGB-Depth camera and deep learning
    Petrakis, Georgios
    Partsinevelos, Panagiotis
    [J]. 25TH AGILE CONFERENCE ON GEOGRAPHIC INFORMATION SCIENCE ARTIFICIAL INTELLIGENCE IN THE SERVICE OF GEOSPATIAL TECHNOLOGIES, 2022, 3
  • [30] SASE: RGB-Depth Database for Human Head Pose Estimation
    Lusi, Iiris
    Escarela, Sergio
    Anbarjafari, Gholamreza
    [J]. COMPUTER VISION - ECCV 2016 WORKSHOPS, PT III, 2016, 9915 : 325 - 336