Skip to main content
Springer Nature Link
Log in

A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In daily life, a person’s gait—an important marker for his/her health status—is usually assessed using inertial sensors fixed to lower limbs or trunk. Such sensor locations are not well suited for continuous and long duration measurements. A better location would be the wrist but with the drawback of the presence of perturbative movements independent of walking. The aim of this study was to devise and validate an algorithm able to accurately estimate walking cadence and speed for daily life walking in various environments based on acceleration measured at the wrist. To this end, a cadence likelihood measure was designed, automatically filtering out perturbative movements and amplifying the periodic wrist movement characteristic of walking. Speed was estimated using a piecewise linear model. The algorithm was validated for outdoor walking in various and challenging environments (e.g., trail, uphill, downhill). Cadence and speed were successfully estimated for all conditions. Overall median (interquartile range) relative errors were −0.13% (−1.72 2.04%) for instantaneous cadence and −0.67% (−6.52 6.23%) for instantaneous speed. The performance was comparable to existing algorithms for trunk- or lower limb-fixed sensors. The algorithm’s low complexity would also allow a real-time implementation in a watch.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Abraham P, Noury-Desvaux B, Gernigon M, Mahé G, Sauvaget T, Leftheriotis G, Le Faucheur A (2012) The inter- and intra-unit variability of a low-cost GPS data logger/receiver to study human outdoor walking in view of health and clinical studies. PLoS ONE 7:e31338. doi:10.1371/journal.pone.0031338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ahola T (2010) Pedometer for running activity using accelerometer sensors on the wrist. Med Equip Insights. doi:10.4137/MEI.S3748

    Google Scholar 

  3. Alaqtash M, Yu H, Brower R, Abdelgawad A, Sarkodie-Gyan T (2011) Application of wearable sensors for human gait analysis using fuzzy computational algorithm. Eng Appl Artif Intell 24:1018–1025. doi:10.1016/j.engappai.2011.04.010

    Article  Google Scholar 

  4. Aminian K, Najafi B, Büla C, Leyvraz P-F, Robert P (2002) Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J Biomech 35:689–699

    Article  PubMed  Google Scholar 

  5. Baker R (2006) Gait analysis methods in rehabilitation. J Neuroeng Rehabil 3:4. doi:10.1186/1743-0003-3-4

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8:135–160

    Article  CAS  PubMed  Google Scholar 

  7. Bonato P (2005) Advances in wearable technology and applications in physical medicine and rehabilitation. J Neuroeng Rehabil. doi:10.1186/1743-0003-2-2

    Google Scholar 

  8. Boyer KA, Andriacchi TP, Beaupre GS (2012) The role of physical activity in changes in walking mechanics with age. Gait Posture 36:149–153. doi:10.1016/j.gaitpost.2012.02.007

    Article  PubMed  Google Scholar 

  9. Brand RA (1989) Can biomechanics contribute to clinical orthopaedic assessments? Iowa Orthop J 9:61–64

    PubMed Central  Google Scholar 

  10. Brodie M, Lord S, Coppens M, Annegarn J, Delbaere K (2015) Eight weeks remote monitoring using a freely worn device reveals unstable gait patterns in older fallers. IEEE Trans Biomed Eng 9294:1. doi:10.1109/TBME.2015.2433935

    Google Scholar 

  11. Butte NF, Ekelund U, Westerterp KR (2012) Assessing physical activity using wearable monitors: measures of physical activity. Med Sci Sports Exerc 44:S5–S12. doi:10.1249/MSS.0b013e3182399c0e

    Article  PubMed  Google Scholar 

  12. Cedervall Y, Halvorsen K, Åberg AC (2014) A longitudinal study of gait function and characteristics of gait disturbance in individuals with Alzheimer’s disease. Gait Posture. doi:10.1016/j.gaitpost.2013.12.026

    PubMed  Google Scholar 

  13. El-Amrawy F, Nounou MI (2015) Are currently available wearable devices for activity tracking and heart rate monitoring accurate, precise, and medically beneficial? Healthc Inform Res 21:315–320. doi:10.4258/hir.2015.21.4.315

    Article  PubMed  PubMed Central  Google Scholar 

  14. Elble RJ, Thomas SS, Higgins C, Colliver J (1991) Stride-dependent changes in gait of older people. J Neurol 238:1–5. doi:10.1007/BF00319700

    Article  CAS  PubMed  Google Scholar 

  15. Elhoushi M, Georgy J, Noureldin A, Korenberg MJ (2016) Motion mode recognition for indoor pedestrian navigation using portable devices. IEEE Trans Instrum Meas 65:208–221. doi:10.1109/TIM.2015.2477159

    Article  Google Scholar 

  16. Ferraris F, Grimaldi U, Parvis M (1995) Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers. Sensors Mater 7:311–330

    Google Scholar 

  17. Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS (2014) Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther 94:222–229. doi:10.2522/ptj.20120525

    Article  PubMed  Google Scholar 

  18. Harris FJ (1978) On the use of windows for harmonic analysis with the discrete Fourier transform. Proc IEEE 66:51–83. doi:10.1109/PROC.1978.10837

    Article  Google Scholar 

  19. Hausdorff JM, Cudkowicz ME, Firtion R (1998) Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson’ s disease and huntington’ s disease. Mov Disord 13:428–437

    Article  CAS  PubMed  Google Scholar 

  20. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21:1263–1284. doi:10.1109/TKDE.2008.239

    Article  Google Scholar 

  21. Henriksen M, Lund H, Moe-Nilssen R, Bliddal H, Danneskiod-Samsøe B (2004) Test–retest reliability of trunk accelerometric gait analysis. Gait Posture 19:288–297. doi:10.1016/S0966-6362(03)00069-9

    Article  PubMed  Google Scholar 

  22. Jasiewicz JM, Allum JHJ, Middleton JW, Barriskill A, Condie P, Purcell B, Li RCT (2006) Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. Gait Posture 24:502–509. doi:10.1016/j.gaitpost.2005.12.017

    Article  PubMed  Google Scholar 

  23. Karuei I, Schneider OS, Stern B, Chuang M, MacLean KE (2013) RRACE: robust realtime algorithm for cadence estimation. Pervasive Mob Comput. doi:10.1016/j.pmcj.2013.09.006

    Google Scholar 

  24. Macleod CA, Conway BA, Allan DB, Galen SS (2014) Development and validation of a low-cost, portable and wireless gait assessment tool. Med Eng Phys 36:541–546. doi:10.1016/j.medengphy.2013.11.011

    Article  PubMed  Google Scholar 

  25. Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W (2013) Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exerc 45:2193–2203. doi:10.1249/MSS.0b013e31829736d6

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mariani B, Hoskovec C, Rochat S, Büla C, Penders J, Aminian K (2010) 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J Biomech 43:2999–3006. doi:10.1016/j.jbiomech.2010.07.003

    Article  PubMed  Google Scholar 

  27. Mariani B, Rouhani H, Crevoisier X, Aminian K (2013) Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait Posture 37:229–234. doi:10.1016/j.gaitpost.2012.07.012

    Article  PubMed  Google Scholar 

  28. Meyns P, Bruijn SM, Duysens J (2013) The how and why of arm swing during human walking. Gait Posture 38:555–562. doi:10.1016/j.gaitpost.2013.02.006

    Article  PubMed  Google Scholar 

  29. Moe-Nilssen R, Helbostad JL (2004) Estimation of gait cycle characteristics by trunk accelerometry. J Biomech 37:121–126. doi:10.1016/S0021-9290(03)00233-1

    Article  PubMed  Google Scholar 

  30. Morris ME, Iansek R, Matyas TA, Summers JJ (1994) Ability to modulate walking cadence remains intact in Parkinson’s disease. J Neurol Neurosurg Psychiatry 57:1532–1534. doi:10.1136/jnnp.57.12.1532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Nelles O (2001) Nonlinear system identification. Springer, Berlin. doi:10.1007/978-3-662-04323-3

  32. Oberg T, Karsznia A, Oberg K (1993) Basic gait parameters: reference data for normal subjects, 10–79 years of age. J Rehabil Res Dev 30:210–223

    CAS  PubMed  Google Scholar 

  33. Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K (2012) Barcoding human physical activity to assess chronic pain conditions. PLoS ONE 7:e32239. doi:10.1371/journal.pone.0032239

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Parviainen J, Kantola J, Collin J (2008) Differential barometry in personal navigation. In: 2008 IEEE/ION position, Locat. Navig. Symp. IEEE, pp 148–152

  35. Pasolini F, Binda I (2008) Pedometer device and step detection method using an algorithm for self-adaptive computation of acceleration thresholds. U.S. Patent 7,463,997

  36. Quach L, Galica A, Jones R, Procter-Gray E, Manor B, Hannan M, Lipsitz L (2011) The non-linear relationship between gait speed and falls: the mobilize boston study. J Am Geriatr Soc 59:1069–1073. doi:10.1111/j.1532-5415.2011.03408.x.The

    Article  PubMed  PubMed Central  Google Scholar 

  37. Rampp A, Barth J, Schülein S, Gaßmann KG, Klucken J, Eskofier BM (2015) Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng 62:1089–1097. doi:10.1109/TBME.2014.2368211

    Article  PubMed  Google Scholar 

  38. Redd CB, Member S, Bamberg SJM, Member S (2012) A wireless sensory feedback device for real-time gait feedback and training. Mechatron IEEEASME Trans 17:425–433

    Article  Google Scholar 

  39. Rochat S, Büla CJ, Martin E, Seematter-Bagnoud L, Karmaniola A, Aminian K, Piot-Ziegler C, Santos-Eggimann B (2010) What is the relationship between fear of falling and gait in well-functioning older persons aged 65 to 70 years? Arch Phys Med Rehabil 91:879–884. doi:10.1016/j.apmr.2010.03.005

    Article  PubMed  Google Scholar 

  40. Samson MM, Crowe A, de Vreede PL, Dessens JA, Duursma SA, Verhaar HJ (2001) Differences in gait parameters at a preferred walking speed in healthy subjects due to age, height and body weight. Aging (Milano) 13:16–21. doi:10.1007/BF03351489

    CAS  Google Scholar 

  41. Smith JO (2010) Physical audio signal processing. W3K Publishing

  42. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, Brach J, Chandler J, Cawthon P, Connor EB, Nevitt M, Visser M, Kritchevsky S, Badinelli S, Harris T, Newman AB, Cauley J, Ferrucci L, Guralnik J (2011) Gait speed and survival in older adults. JAMA 305:50–58. doi:10.1001/jama.2010.1923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Susi M, Renaudin V, Lachapelle G (2013) Motion mode recognition and step detection algorithms for mobile phone users. Sensors (Basel) 13:1539–1562. doi:10.3390/s130201539

    Article  Google Scholar 

  44. Tan H, Wilson AM, Lowe J (2008) Measurement of stride parameters using a wearable GPS and inertial measurement unit. J Biomech 41:1398–1406. doi:10.1016/j.jbiomech.2008.02.021

    Article  PubMed  Google Scholar 

  45. Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors (Basel) 12:2255–2283. doi:10.3390/s120202255

    Article  Google Scholar 

  46. Taraldsen K, Chastin SFM, Riphagen II, Vereijken B, Helbostad JL (2012) Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas 71:13–19. doi:10.1016/j.maturitas.2011.11.003

    Article  PubMed  Google Scholar 

  47. Terrier P, Ladetto Q, Merminod B, Schutz Y (2000) High-precision satellite positioning system as a new tool to study the biomechanics of human locomotion. J Biomech 33:1717–1722. doi:10.1016/S0021-9290(00)00133-0

    Article  CAS  PubMed  Google Scholar 

  48. Trojaniello D, Cereatti A, Pelosin E, Avanzino L, Mirelman A, Hausdorff JM, Della Croce U (2014) Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait. J Neuroeng Rehabil 11:152. doi:10.1186/1743-0003-11-152

    Article  PubMed  PubMed Central  Google Scholar 

  49. Yang S, Li Q (2012) Inertial sensor-based methods in walking speed estimation: a systematic review. Sensors (Basel) 12:6102–6116. doi:10.3390/s120506102

    Article  Google Scholar 

  50. Zhao N (2010) Full-featured pedometer design realized with 3-Axis digital accelerometer. Analog Dialogue 44:1–5

    Google Scholar 

  51. Zijlstra W, Hof AL (2003) Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18:1–10

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was financed by the CTI Grant No 14787.1 PFNM-NM. The authors would like to thank all subjects that agreed walking in various meteorological conditions ranging from cold to hot and from sun to light rain.

Author information

Authors and Affiliations

  1. Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, EPFL STI IBI LMAM, Station 9, 1015, Lausanne, Switzerland

    Benedikt Fasel, Cyntia Duc, Farzin Dadashi & Kamiar Aminian

  2. Electronics and Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, EPFL STI IMT ESPLAB, Rue de la Maladière 71B, Case postale 526, 2002, Neuchâtel, Switzerland

    Flavien Bardyn, Martin Savary & Pierre-André Farine

Authors
  1. Benedikt Fasel
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Cyntia Duc
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Farzin Dadashi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Flavien Bardyn
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Martin Savary
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Pierre-André Farine
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Kamiar Aminian
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kamiar Aminian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fasel, B., Duc, C., Dadashi, F. et al. A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking. Med Biol Eng Comput 55, 1773–1785 (2017). https://doi.org/10.1007/s11517-017-1621-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-017-1621-2

Keywords