Health and Technology

, Volume 2, Issue 4, pp 249–258 | Cite as

An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

  • Yan HuangEmail author
  • Huiru Zheng
  • Chris Nugent
  • Paul McCullagh
  • Norman Black
  • William Burns
  • Mark A. Tully
  • Suzanne M. McDonough
Original Paper


In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.


Smart phone Step detection Worn position Accelerometer Self-management 



This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/F001959/1.


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Copyright information

© IUPESM and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yan Huang
    • 1
    Email author
  • Huiru Zheng
    • 1
  • Chris Nugent
    • 1
  • Paul McCullagh
    • 1
  • Norman Black
    • 1
  • William Burns
    • 1
  • Mark A. Tully
    • 2
  • Suzanne M. McDonough
    • 3
  1. 1.School of Computing and Mathematics, Computer Science Research InstituteUniversity of UlsterJordownstownUK
  2. 2.Centre of Excellence for Public Health (NI)Queen’s University BelfastNorthern IrelandUK
  3. 3.School of Health Sciences, Health and Rehabilitation Sciences Research InstituteUniversity of UlsterJordownstownUK

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