Activity classification using realistic data from wearable sensors

被引:464
|
作者
Pärkkä, J
Ermes, M
Korpipää, P
Mäntyjärvi, J
Peltola, J
Korhonen, I
机构
[1] VTT Informat Technol, Tampere 33101, Finland
[2] VTT Elect, Oulu 90571, Finland
来源
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE | 2006年 / 10卷 / 01期
关键词
activity classification; context awareness; physical activity; wearable sensors;
D O I
10.1109/TITB.2005.856863
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree,, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.
引用
收藏
页码:119 / 128
页数:10
相关论文
共 50 条
  • [21] Classification of Daily Activities for the Elderly Using Wearable Sensors
    Liu, Jian
    Sohn, Jeehoon
    Kim, Sukwon
    JOURNAL OF HEALTHCARE ENGINEERING, 2017, 2017
  • [22] Classification and visualization of skateboard tricks using wearable sensors
    Groh, Benjamin H.
    Fleckenstein, Martin
    Kautz, Thomas
    Eskofier, Bjoern M.
    PERVASIVE AND MOBILE COMPUTING, 2017, 40 : 42 - 55
  • [23] Activity classification and dead reckoning for pedestrian navigation with wearable sensors
    Sun, Zuolei
    Mao, Xuchu
    Tian, Weifeng
    Zhang, Xiangfen
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2009, 20 (01)
  • [24] Body Activity Recognition using Wearable Sensors
    Cheng, Long
    You, Chenyu
    Guan, Yani
    Yu, Yiyi
    2017 COMPUTING CONFERENCE, 2017, : 756 - 765
  • [25] Recognizing Gym Exercises Using Acceleration Data from Wearable Sensors
    Koskimaki, Heli
    Siirtola, Pekka
    2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), 2014, : 321 - 328
  • [26] Inferring Human Activity Using Wearable Sensors
    Chawathe, Sudarshan S.
    2021 IEEE 12TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), 2021, : 413 - 419
  • [27] Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data
    Papagiannaki, Aimilia
    Zacharaki, Evangelia I.
    Kalouris, Gerasimos
    Kalogiannis, Spyridon
    Deltouzos, Konstantinos
    Ellul, John
    Megalooikonomou, Vasileios
    SENSORS, 2019, 19 (04)
  • [28] Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification
    Jayasinghe, Udeni
    Harwin, William S.
    Hwang, Faustina
    SENSORS, 2020, 20 (01)
  • [29] Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning
    Hussein Lopez-Nava, Irvin
    Valentin-Coronado, Luis M.
    Garcia-Constantino, Matias
    Favela, Jesus
    SENSORS, 2020, 20 (17) : 1 - 21
  • [30] Using an in-Ear Wearable to Annotate Activity Data across Multiple Inertial Sensors
    Hoelzemann, Alexander
    Odoemelem, Henry
    Van Laerhoven, Kristof
    EARCOMP 2019: FIRST INTERNATIONAL WORKSHOP ON EARABLE COMPUTING, 2019, : 14 - 19