Sensor Technologies for Fall Detection Systems: A Review

被引:79
|
作者
Singh, Anuradha [1 ]
Rehman, Saeed Ur [2 ]
Yongchareon, Sira [3 ]
Chong, Peter Han Joo [1 ]
机构
[1] Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1142, New Zealand
[2] Flinders Univ S Australia, Coll Sci & Engn, Adelaide, SA 5042, Australia
[3] Auckland Univ Technol, Dept IT & Software Engn, Auckland 1142, New Zealand
关键词
Sensor systems; Accelerometers; Robustness; Detectors; Wearable sensors; Cameras; Assisted living; elderly assisted living; fall detection; smart homes; sensor technology; wearable sensor; SOURCE SEPARATION; RISK-FACTORS; RADAR; FLOOR; PREVENTION; CLASSIFICATION; INFORMATION; ALGORITHMS; SIGNATURE; FUSION;
D O I
10.1109/JSEN.2020.2976554
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
The risk of falls in older adults restrict their social life and independent living. The assisted living devices help older adults to live independently in their home, giving a psychological boost, and releasing the burden on the caregiver and the healthcare providers. A robust and accurate fall detection system is essential to provide immediate help and to reduce the severe post-fall consequences, and the associated medical care cost significantly. This review aims to provide a comprehensive technical insight into the existing fall detection system, to classify various approaches and the challenges encountered during implementation. The fall detectors are broadly classified into three categories, namely wearable, ambiance-based, and hybrid sensing detectors, which are further explored by the sensor technology. This review provides a comprehensive overview of each competing sensor technology ranging from an accelerometer, pressure sensor, and radar to camera-based and their infusion into a complete fall detection system. It outlines the strength and limitations of different sensor fall detection systems in terms of feature extraction, classification, performance, and experimental dataset. The user adaptability, installation complexity, and power requirement of the systems are the main areas, which are not addressed adequately in the literature. In the end, the review provides a basic framework in deciding the technology for a specific scenario or location according to the prerequisites for the deployment.
引用
收藏
页码:6889 / 6919
页数:31
相关论文
共 50 条
  • [21] A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults
    Chen, Manting
    Wang, Hailiang
    Yu, Lisha
    Yeung, Eric Hiu Kwong
    Luo, Jiajia
    Tsui, Kwok-Leung
    Zhao, Yang
    SENSORS, 2022, 22 (18)
  • [22] Recent advances in electrochemical sensor technologies for THC detection—a narrative review
    Kaveh Amini
    Ali Sepehrifard
    Ali Valinasabpouri
    Jennifer Safruk
    Davide Angelone
    Tiago de Campos Lourenco
    Journal of Cannabis Research, 4
  • [23] Fall Detection Using Kinect Sensor and Fall Energy Image
    Kwolek, Bogdan
    Kepski, Michal
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, 2013, 8073 : 294 - 303
  • [24] Technologies for Photonic Sensor Systems
    Leheny, Robert F.
    McCants, Carl E.
    PROCEEDINGS OF THE IEEE, 2009, 97 (06) : 957 - 970
  • [25] Intrusion Detection Systems in Wireless Sensor Networks: A Review
    Alrajeh, Nabil Ali
    Khan, S.
    Shams, Bilal
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2013,
  • [26] A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies
    Hassan, Ch. Anwar Ul
    Karim, Faten Khalid
    Abbas, Assad
    Iqbal, Jawaid
    Elmannai, Hela
    Hussain, Saddam
    Ullah, Syed Sajid
    Khan, Muhammad Sufyan
    SUSTAINABILITY, 2023, 15 (05)
  • [27] Involvement of older people in the development of fall detection systems: a scoping review
    Thilo, Friederike J. S.
    Hurlimann, Barbara
    Hahn, Sabine
    Bilger, Selina
    Schols, Jos M. G. A.
    Halfens, Ruud J. G.
    BMC GERIATRICS, 2016, 16
  • [28] Involvement of older people in the development of fall detection systems: a scoping review
    Friederike JS Thilo
    Barbara Hürlimann
    Sabine Hahn
    Selina Bilger
    Jos MGA Schols
    Ruud JG Halfens
    BMC Geriatrics, 16
  • [29] Fall Detection Systems at Night
    Elagovan, Ramanujam
    Perumal, Thinagaran
    Krishnan, Shankar
    COMPUTER, 2023, 56 (06) : 44 - 51
  • [30] Bed-Fall Detection and Prediction: A Generic Classification and Review of Bed-Fall Related Systems
    Ibrahim, Ali
    Chaccour, Kabalan
    El Hassani, Amir Hajjam
    Andres, Emmanuel
    IEEE SENSORS JOURNAL, 2021, 21 (05) : 5678 - 5686