Medical & Biological Engineering & Computing

, Volume 55, Issue 10, pp 1773–1785 | Cite as

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

  • Benedikt Fasel
  • Cyntia Duc
  • Farzin Dadashi
  • Flavien Bardyn
  • Martin Savary
  • Pierre-André Farine
  • Kamiar AminianEmail author
Original Article


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.


Inertial sensor Wrist Walking Cadence Speed 



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.


  1. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 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. 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 CrossRefGoogle Scholar
  4. 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 35:689–699CrossRefPubMedGoogle Scholar
  5. 5.
    Baker R (2006) Gait analysis methods in rehabilitation. J Neuroeng Rehabil 3:4. doi: 10.1186/1743-0003-3-4 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8:135–160CrossRefPubMedGoogle Scholar
  7. 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. 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 CrossRefPubMedGoogle Scholar
  9. 9.
    Brand RA (1989) Can biomechanics contribute to clinical orthopaedic assessments? Iowa Orthop J 9:61–64PubMedCentralGoogle Scholar
  10. 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. 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 CrossRefPubMedGoogle Scholar
  12. 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 PubMedGoogle Scholar
  13. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 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 CrossRefPubMedGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 16.
    Ferraris F, Grimaldi U, Parvis M (1995) Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers. Sensors Mater 7:311–330Google Scholar
  17. 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 CrossRefPubMedGoogle Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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 13:428–437CrossRefPubMedGoogle Scholar
  20. 20.
    He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21:1263–1284. doi: 10.1109/TKDE.2008.239 CrossRefGoogle Scholar
  21. 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 CrossRefPubMedGoogle Scholar
  22. 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 CrossRefPubMedGoogle Scholar
  23. 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. 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 CrossRefPubMedGoogle Scholar
  25. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 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 CrossRefPubMedGoogle Scholar
  27. 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 CrossRefPubMedGoogle Scholar
  28. 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 CrossRefPubMedGoogle Scholar
  29. 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 CrossRefPubMedGoogle Scholar
  30. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Nelles O (2001) Nonlinear system identification. Springer, Berlin. doi: 10.1007/978-3-662-04323-3
  32. 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 30:210–223PubMedGoogle Scholar
  33. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Parviainen J, Kantola J, Collin J (2008) Differential barometry in personal navigation. In: 2008 IEEE/ION position, Locat. Navig. Symp. IEEE, pp 148–152Google Scholar
  35. 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 7,463,997Google Scholar
  36. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 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 CrossRefPubMedGoogle Scholar
  38. 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 17:425–433CrossRefGoogle Scholar
  39. 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 CrossRefPubMedGoogle Scholar
  40. 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 Google Scholar
  41. 41.
    Smith JO (2010) Physical audio signal processing. W3K PublishingGoogle Scholar
  42. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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 CrossRefGoogle Scholar
  44. 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 CrossRefPubMedGoogle Scholar
  45. 45.
    Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors (Basel) 12:2255–2283. doi: 10.3390/s120202255 CrossRefGoogle Scholar
  46. 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 CrossRefPubMedGoogle Scholar
  47. 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 CrossRefPubMedGoogle Scholar
  48. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 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 CrossRefGoogle Scholar
  50. 50.
    Zhao N (2010) Full-featured pedometer design realized with 3-Axis digital accelerometer. Analog Dialogue 44:1–5Google Scholar
  51. 51.
    Zijlstra W, Hof AL (2003) Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18:1–10CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2017

Authors and Affiliations

  1. 1.Laboratory of Movement Analysis and MeasurementEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Electronics and Signal Processing LaboratoryEcole Polytechnique Fédérale de LausanneNeuchâtelSwitzerland

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