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

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Abstract

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.

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References

  1. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801–9.

    Article  Google Scholar 

  2. World Health Organization: The world health report. Geneva. 2002.

  3. Morris JN, Hardman AE. Walking to health. Sports Med. 1997;23(5):306–32.

    Article  Google Scholar 

  4. Tudor-Locke C, Bassett DR. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34(1):1–8.

    Article  Google Scholar 

  5. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, Stave CD, Olkin I, Sirard JR. Using pedometers to increase physical activity and improve health. JAMA. 2007;298(19):2296–304.

    Article  Google Scholar 

  6. Zheng H, Nugent C, McCullagh P, Huang Y, Zhang S, Burns W, Davies R, Black N, Wright P, Mawson S, Eccleston C, Hawley M, Mountain G. Smart self management: assistive technology to support people with chronic disease. J Telemed Telecare. 2010;16(4):224–7.

    Article  Google Scholar 

  7. Ying H, Silex C, Schnitzer A, Leonhardt S, Schiek M. Automatic step detection in the accelerometer signal. The 4th International Workshop on Wearable and Implantable Body Sensor Networks. 2007. pp 80–85.

  8. Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V. SHIMMER™ – A wireless sensor platform for noninvasive biomedical research. IEEE Sensors J. 2010;10(9):1527–34.

    Article  Google Scholar 

  9. Godfrey A, Culhane KM, Lyons GM. Comparison of the performance of the activPAL™ professional physical activity logger to a discrete accelerometer-based activity monitor. Med Eng Phys. 2007;29(8):930–4.

    Article  Google Scholar 

  10. Grant PM, Ryan CG, Tigbe WW, Granat MH. The validation of a novel activity monitor in the measurement of posture and motion during everyday activities. Br J Sports Med. 2006;40(12):992–7.

    Article  Google Scholar 

  11. Tsavourelou A, Rowe P, Babatsikou F, Koutis C. Validation of the activPAL in the health promotion context. Health Sci J. 2009;3(2):105–14.

    Google Scholar 

  12. Ryan CG, Grant PM, Tigbe WW, Granat MH. The validity and reliability of a novel activity monitor as a measure of walking. Br J Sports Med. 2006;40(9):779–84.

    Article  Google Scholar 

  13. Dahlgren G, Carlsson D, Moorhead A, Hager-Ross C, McDonough SM. Test-retest reliability of step counts with the activPAL device in common daily activities. Gait Posture. 2010;32(3):386–90.

    Article  Google Scholar 

  14. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, Stave CD, Olkin I, Sirard JR. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007;298(19):2296–304.

    Article  Google Scholar 

  15. Tudor-Locke C, Lutes L. Why do pedometers work? A reflection upon the factors related to successfully increasing physical activity. Sports Med. 2009;39(12):981–93.

    Article  Google Scholar 

  16. Burns W, Nugent CD, McCullagh PJ, Zheng H, Black ND, Wright P, Mountain GA. Home based self-management of chronic diseases. The 7th International Conference on Smart Homes and Health Telematics (ICOST). 2009. pp 229–232.

  17. Davies RJ, Nugent CD, Donnelly MP, Hettinga M, Meiland FJ, Moelaert F, Mulvenna MD, Bengtsson JE, Craig D, Dröes RM. A user driven approach to develop a cognitive prosthetic to address the unmet needs of people with mild dementia. Pervasive Mob Comput. 2009;5(3):253–67.

    Article  Google Scholar 

  18. Zheng H, Nugent CD, McCullagh PJ, Burns WP, Alexander S, Huang Y, Zhang S, Black ND, Vowles KE, Mawson SJ, Eccleston C, Wright P, Mountain GA. Integration of assistive technology to support self management. The IET conference on Assisted Living. 2009. pp 14–17.

  19. Bardram JE, Mihailidis A, Wan D. Pervasive computing in healthcare. 1st ed. CRC Press; 2006.

  20. Germanakos P, Mourlas C, Samaras G. A mobile agent approach for ubiquitous and personalized eHealth information systems. The Workshop on Personalization for e-Health of the 10th International Conference on User Modeling. Edinburgh, 2005. pp 67–70.

  21. Ingensand H, Mautz R. Message of the conference directors. International Conference on Indoor Positioning and Indoor Navigation (IPIN). Zurich, Switzerland. 2010.

  22. Shin SH, Kim HW, Park CG, Yoo YM. Sit-down and stand-up awareness algorithm for the Pedestrian Dead Reckoning. Naples, Italy: The European Navigation Conference - ENC-GNCC; 2009.

    Google Scholar 

  23. Mladenov M, Mock M. A step counter service for Java-enabled devices using a built-in accelerometer. The 1st International Workshop on Context-Aware Middleware and Services. 2009.

  24. Huang Y, Zheng H, Nugent CD, McCullagh PJ, McDonough SM, Tully MA, Connor SO. Activity monitoring using an intelligent mobile phone – a validation study. The 3rd International Conference on Pervasive Technologies Related to Assistive Environments. 2010.

  25. Zheng H, Yang M, Wang H, McClean S. Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. Studies Comput Intell. 2009;189:57–70.

    Article  Google Scholar 

  26. Boyd J, Sundaram H. A framework to detect and classify activity transitions in low-power applications. The IEEE International Conference on Multimedia and Expo. 2009. pp. 1712–1715.

  27. Yang M, Zheng H, Wang H, McClean S, Harris N. Assessing the utility of smart mobile phones in gait pattern analysis. J Health Technol. 2012. doi:10.1007/s12553-012-0021-8.

  28. Fritz S, Lusardi M. White paper: “walking speed: the sixth vital sign”. J Geriatr Phys Ther. 2009;32(2):2–5.

    Article  Google Scholar 

  29. Brooks GA, Butte NF, Rand WM, Flatt JP, Caballero B. Chronicle of the Institute of Medicine physical activity recommendation: how a physical activity recommendation came to be among dietary recommendations. Am J Clin Nutr. 2004;79(5):921S–30S.

    Google Scholar 

  30. Schneider PL, Crouter S, Bassett D. Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exercise. 2004;36(2):331–5.

    Article  Google Scholar 

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Acknowledgment

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

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Authors and Affiliations

  1. School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Jordownstown, Newtownabbey, Co. Antrim, BT37 0QB, UK

    Yan Huang, Huiru Zheng, Chris Nugent, Paul McCullagh, Norman Black & William Burns

  2. Centre of Excellence for Public Health (NI), Queen’s University Belfast, Northern Ireland, UK

    Mark A. Tully

  3. School of Health Sciences, Health and Rehabilitation Sciences Research Institute, University of Ulster, Jordownstown, Newtownabbey, Co. Antrim, BT37 0QB, UK

    Suzanne M. McDonough

Authors
  1. Yan Huang
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  2. Huiru Zheng
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  3. Chris Nugent
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  4. Paul McCullagh
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  5. Norman Black
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  6. William Burns
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  7. Mark A. Tully
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  8. Suzanne M. McDonough
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Correspondence to Yan Huang.

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Huang, Y., Zheng, H., Nugent, C. et al. An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring. Health Technol. 2, 249–258 (2012). https://doi.org/10.1007/s12553-012-0035-2

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  • DOI: https://doi.org/10.1007/s12553-012-0035-2

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