Estimation of Walking Exercise Intensity Using 3-D Acceleration Sensor

被引:11
|
作者
Kurihara, Yosuke [1 ]
Watanabe, Kajiro [2 ]
Yoneyama, Mitsuru [3 ]
机构
[1] Seikei Univ, Dept Comp & Informat Sci, Fac Sci & Technol, Tokyo 1808633, Japan
[2] Hosei Univ, Dept Syst Control Engn, Fac Engn, Tokyo 1848584, Japan
[3] Mitsubishi Chem Grp Sci & Technol Res Ctr Inc, Kanagawa 2278502, Japan
关键词
Accelerometer; exercise intensity; metabolic equivalents (METs); walking;
D O I
10.1109/TSMCC.2011.2130522
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes a method to estimate the measure of human energy expenditure referred to as metabolic equivalents (METs). The measure is defined by an index of the amount of energy a person expends through normal daily activities. The average MET values are defined by the type of activity; for example, brisk walking corresponds to 4 METs. The index lists 107 activities and the corresponding MET values. However, to determine the energy consumed in a day, each type of activity and its duration must be noted, which is inconvenient and time consuming. Here, we consider a quantitative method to estimate the METs from the acceleration of waist motion in our activities. We consider a simple physical model of amoving human body, define the power output for an activity, and then relate the maximum oxygen intake rate per minute, which is proportional to the energy expenditure per minute with the power output. Based on these factors, we developed an automatic MET estimator using a 3-D accelerometer. The system was examined during various selected activities, and the validity of the MET estimation method was tested in activities, excluding very intense activities. The exercise intensity for walking at various speeds was estimated with an average error of -0.6 METs.
引用
收藏
页码:495 / 500
页数:6
相关论文
共 50 条
  • [21] Prototype for the Estimation and Evaluation of Walking Velocity Using Acceleration Transducers
    Tondo, F.
    Salerno, L.
    Becker, R.
    2014 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC) PROCEEDINGS, 2014, : 360 - 365
  • [22] Saccadic Eyes Recognition Using 3-D Shape Data from a 3-D Near Infrared Sensor
    Guo, Shenwen
    Tang, Jinshan
    Parakkat, Julia B.
    Robinette, Kathleen M.
    MOBILE MULTIMEDIA/IMAGE PROCESSING, SECURITY, AND APPLICATIONS 2012, 2012, 8406
  • [23] Current Dipole Estimation from Magnetospinogram with 3-D Planar Sensor Array
    Honda, Satoshi
    2017 56TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE), 2017, : 916 - 921
  • [24] A Tactile Sensing Concept for 3-D Displacement and 3-D Force Measurement Using Light Angle and Intensity Sensing
    Leslie, Olivia
    Bulens, David Cordova
    Ulloa, Pablo Martinez
    Redmond, Stephen J.
    IEEE SENSORS JOURNAL, 2023, 23 (18) : 21172 - 21188
  • [25] The 3-D parameter estimation using a well database and GOCAD
    Bonomi, T
    Cavallin, A
    NEW APPROACHES CHARACTERIZING GROUNDWATER FLOW, VOLS 1 AND 2, 2001, : 263 - 267
  • [26] Modified GIC Estimation Using 3-D Earth Conductivity
    Kelbert, Anna
    Lucas, Greg M.
    SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS, 2020, 18 (08):
  • [27] Defect 3-D Profile Estimation Using SEM Images
    Agashe, Shashank S.
    Kumar, Gaurav
    Kulasekaran, Sathyanarayanan
    Cho, Yunje
    IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, 2023, 36 (03) : 351 - 358
  • [28] Sensorless freehand 3-D ultrasound using regression of the echo intensity
    Prager, RW
    Gee, AH
    Treece, GM
    Cash, CJC
    Berman, LH
    ULTRASOUND IN MEDICINE AND BIOLOGY, 2003, 29 (03): : 437 - 446
  • [29] Frequency domain estimation of 3-D rigid motion based on range and intensity data
    Lucchese, L
    Doretto, D
    Cortelazzo, GM
    INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS, 1997, : 107 - 112
  • [30] Head Posture Estimation by Deep Learning Using 3-D Point Cloud Data From a Depth Sensor
    Sasaki, Seiji
    Premachandra, Chinthaka
    IEEE SENSORS LETTERS, 2021, 5 (07)