Hand, belt, pocket or bag: Practical activity tracking with mobile phones

Stephen A. Antos*, Mark V. Albert, Konrad P. Kording

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


For rehabilitation and diagnoses, an understanding of patient activities and movements is important. Modern smartphones have built in accelerometers which promise to enable quantifying minute-by-minute what patients do (e.g. walk or sit). Such a capability could inform recommendations of physical activities and improve medical diagnostics. However, a major problem is that during everyday life, we carry our phone in different ways, e.g. on our belt, in our pocket, in our hand, or in a bag. The recorded accelerations are not only affected by our activities but also by the phone's location. Here we develop a method to solve this kind of problem, based on the intuition that activities change rarely, and phone locations change even less often. A hidden Markov model (HMM) tracks changes across both activities and locations, enabled by a static support vector machine (SVM) classifier that probabilistically identifies activity-location pairs. We find that this approach improves tracking accuracy on healthy subjects as compared to a static classifier alone. The obtained method can be readily applied to patient populations. Our research enables the use of phones as activity tracking devices, without the need of previous approaches to instruct subjects to always carry the phone in the same location.

Original languageEnglish (US)
Pages (from-to)22-30
Number of pages9
JournalJournal of Neuroscience Methods
StatePublished - Jul 15 2014


  • Accelerometer
  • Activity recognition
  • Classification
  • Context awareness
  • Parkinson's disease
  • Smartphone

ASJC Scopus subject areas

  • Neuroscience(all)


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