Kalman-Filter-Based Walking Distance Estimation for a Smart-Watch

被引：8
|
作者：
Suh, Young Soo [1 ]
Nemati, Ebrahim [2 ]
Sarrafzadeh, Majid [3 ]
机构：
[1] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea
[2] Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90095 USA
[3] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA
来源：
2016 IEEE FIRST INTERNATIONAL CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE) | 2016年
关键词：
NAVIGATION;
D O I：
10.1109/CHASE.2016.21
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
A novel walking distance estimation algorithm using the inertial sensors of the smart-watch is proposed. Firstly, the peaks of the norm of the accelerometer and gyroscope signals are detected. Due to arm swing, walking step detection using these peaks are not reliable. A Kalman filter is used to combine with the peak detection algorithm applied on the accelerometer and gyroscope norm peaks and robustly detect walking steps even if there is large arm swing. Walking distance is estimated using walking step time and walking length relationship. The proposed algorithm was tested on 25 subjects : each subject walked 50 m six times with different walking speed and different arm swing speed. The standard deviation of walking distance estimation error is 3.9 m (without person dependent calibration) and 1.9 m (with person dependent calibration) for a 50m distance.
引用
下载
收藏
页码：150 / 156
页数：7
相关论文
共 50 条
  • [41] Analyses and Enhancement of Linear Kalman-Filter-Based Phase-Locked Loop
    Xu, Wen
    Huang, Chun
    Jiang, Hui
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
  • [42] Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
    Hwang, Hyeongjin
    Lee, Jehwon
    Eum, Sangjune
    Nam, Kanghyun
    ALGORITHMS, 2019, 12 (04):
  • [43] High performance force sensing based on kalman-filter-based disturbance observer utilizing FPGA
    Phuong T.T.
    Mitsantisuk C.
    Ohishi K.
    IEEJ Transactions on Industry Applications, 2011, 131 (03) : 334 - 342+15
  • [44] Influence of Parameters in Kalman-filter-based Method on Image Quality for Electrical Capacitance Tomography
    Wang, Ying
    Xu, Lijun
    Sun, Shijie
    Lu, Xupeng
    Sun, Jiangtao
    2021 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC 2021), 2021,
  • [45] AN APPLICATION-SPECIFIC PROCESSOR DEDICATED TO KALMAN-FILTER-BASED CORRECTION OF SPECTROMETRIC DATA
    BARWICZ, A
    MASSICOTTE, D
    SAVARIA, Y
    PANGO, PA
    MORAWSKI, RZ
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 1995, 44 (03) : 720 - 724
  • [46] Kalman-filter-based tracking algorithm in projected capacitive multi-touch sensing
    Chen, Liquan
    Yuan, Mengting
    Journal of Computational Information Systems, 2014, 10 (22): : 9789 - 9796
  • [47] Kalman-filter-based observer design around optimal control policy for gas pipelines
    Durgut, I
    Leblebicioglu, K
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 1997, 24 (02) : 233 - 245
  • [48] A New Kalman-Filter-Based Framework for Fast and Accurate Visual Tracking of Rigid Objects
    Yoon, Youngrock
    Kosaka, Akio
    Kak, Avinash C.
    IEEE TRANSACTIONS ON ROBOTICS, 2008, 24 (05) : 1238 - 1251
  • [49] Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures
    Cerati, G.
    Elmer, P.
    Krutelyov, S.
    Lantz, S.
    Lefebvre, M.
    Masciovecchio, M.
    McDermott, K.
    Riley, D.
    Tadel, M.
    Wittich, P.
    Wurthwein, F.
    Yagil, A.
    18TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH (ACAT2017), 2018, 1085
  • [50] Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion
    Gan, Q
    Harris, CJ
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2001, 37 (01) : 273 - 280
12345