title: Identifying typical physical activity on smartphone with varying positions and orientations.
abstract: BACKGROUND
  Traditional activity recognition solutions are not widely applicable due to a high cost and inconvenience to use with numerous sensors. This paper aims to automatically recognize physical activity with the help of the built-in sensors of the widespread smartphone without any limitation of firm attachment to the human body.
  
  
  METHODS
  By introducing a method to judge whether the phone is in a pocket, we investigated the data collected from six positions of seven subjects, chose five signals that are insensitive to orientation for activity classification. Decision trees (J48), Naive Bayes and Sequential minimal optimization (SMO) were employed to recognize five activities: static, walking, running, walking upstairs and walking downstairs.
  
  
  RESULTS
  The experimental results based on 8,097 activity data demonstrated that the J48 classifier produced the best performance with an average recognition accuracy of 89.6% during the three classifiers, and thus would serve as the optimal online classifier.
  
  
  CONCLUSIONS
  The utilization of the built-in sensors of the smartphone to recognize typical physical activities without any limitation of firm attachment is feasible.
authors.affiliation: The Key Lab for Health Informatics of Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. fen.miao@siat.ac.cn.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China. fen.miao@siat.ac.cn.
authors.forename: Fen
authors.initials: F
authors.lastname: Miao
authors.affiliation: High-field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. yi.he@tuebingen.mpg.de.
authors.forename: Yi
authors.initials: Y
authors.lastname: He
authors.affiliation: The Key Lab for Health Informatics of Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 2007liujinlei@163.com.|School of Electrical & Mechanical Engineering, Guangdong University of Technology, Guangzhou, China. 2007liujinlei@163.com.
authors.forename: Jinlei
authors.initials: J
authors.lastname: Liu
authors.affiliation: The Key Lab for Health Informatics of Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. ye.li@siat.ac.cn.
authors.forename: Ye
authors.initials: Y
authors.lastname: Li
authors.affiliation: Industrial Design, Technische Universiteit Eindhoven, 5612, Eindhoven, AZ, the Netherlands. i.b.i.ayoola@tue.nl.
authors.forename: Idowu
authors.initials: I
authors.lastname: Ayoola
mesh_terms: D000054:Acceleration; D056044:Actigraphy / Q000295:instrumentation / Q000379:methods*; D000328:Adult; D015992:Body Mass Index; D003020:Clothing; D003663:Decision Trees; D005260:Female; D006112:Gravitation; D006801:Humans; D008027:Light; D008280:Magnetics; D008297:Male; D009043:Motor Activity; D011187:Posture; D012016:Reference Values; D012399:Rotation; D012420:Running; D000068997:Smartphone; D016138:Walking