Activity classification based on inertial and barometric pressure sensors at different anatomical locations

被引:86
|
作者
Moncada-Torres, A. [1 ]
Leuenberger, K. [1 ]
Gonzenbach, R. [2 ]
Luft, A. [2 ]
Gassert, R. [1 ]
机构
[1] ETH, Dept Hlth Sci & Technol, Rehabil Engn Lab, Zurich, Switzerland
[2] Univ Zurich Hosp, CH-8091 Zurich, Switzerland
基金
瑞士国家科学基金会;
关键词
accelerometers; inertial measurement unit; wearable sensors; machine learning; activities of daily living; stair ascent; stair descent; TRIAXIAL ACCELEROMETER; ACTIVITY RECOGNITION; PHYSICAL-ACTIVITY; REAL-TIME; LONG-TERM; POSTURE; IDENTIFICATION; RELIABILITY; VALIDATION; VALIDITY;
D O I
10.1088/0967-3334/35/7/1245
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.
引用
下载
收藏
页码:1245 / 1263
页数:19
相关论文
共 50 条
  • [1] Human Activity Classification with Inertial Sensors
    Silva, Joana
    Monteiro, Miguel
    Sousa, Filipe
    PHEALTH 2014, 2014, 200 : 101 - 104
  • [2] Nonintrusive Measurement of Elevator Velocity Based on Inertial and Barometric Sensors in Autonomous Node
    Nikolov, Dimitar N.
    Marinov, Marin B.
    Ganev, Borislav T.
    Djamijkov, Todor S.
    2020 43RD INTERNATIONAL SPRING SEMINAR ON ELECTRONICS TECHNOLOGY (ISSE), 2020,
  • [3] Towards a Wearable Wheelchair Monitor: Classification of push style based on inertial sensors at multiple upper limb locations
    Herrera, Roxana Ramirez
    Heravi, Behzad Momahed
    Barbareschi, Giulia
    Carlson, Tom
    Holloway, Catherine
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 1535 - 1540
  • [4] Evaluation of Supervised Classification Algorithms for Human Activity Recognition with Inertial Sensors
    Zebin, Tahmina
    Scully, Patricia J.
    Ozanyan, Krikor B.
    2017 IEEE SENSORS, 2017, : 1038 - 1040
  • [5] Human Action Recognition Based on Inertial Sensors and Complexity Classification
    Liu, Lijue
    Lei, Xiaoliang
    Chen, Baifan
    Shu, Lei
    JOURNAL OF INFORMATION TECHNOLOGY RESEARCH, 2019, 12 (01) : 18 - 35
  • [6] Research on the Taxonomy of Activity Recognition Based on Inertial Sensors
    Xiao Zi-ming
    Shi Yu-long
    Xue Yong
    Hu Feng
    Wu Yu-chuan
    COMPUTING, CONTROL AND INDUSTRIAL ENGINEERING IV, 2013, 823 : 107 - 110
  • [7] Pressure-Pair-Based Floor Localization System Using Barometric Sensors on Smartphones
    Yi, Chungheon
    Choi, Wonik
    Jeon, Youngjun
    Liu, Ling
    SENSORS, 2019, 19 (16)
  • [8] Evaluation of Hemiplegic Gait Based on Plantar Pressure and Inertial Sensors
    Pan, Zewei
    Gao, Huigang
    Chen, Yan
    Xie, Zekun
    Xie, Longhan
    IEEE SENSORS JOURNAL, 2023, 23 (11) : 12008 - 12017
  • [9] Design and Fabrication of a Flexible Pressure-Sensitive Insole Based on Barometric Tactile Sensors
    Amralizadeh, Arsalan
    Marjani, Trifa
    Masouleh, Mehdi Tale
    Kalhor, Ahmad
    2020 28TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2020, : 371 - 375
  • [10] Human motion classification based on inertial sensors with Extreme Gradient Boosting
    Peng, Zhiqiang
    Zhang, Yue
    2018 INTERNATIONAL CONFERENCE ON IMAGE AND VIDEO PROCESSING, AND ARTIFICIAL INTELLIGENCE, 2018, 10836