Advertisement

Gait Parameters Estimated Using Inertial Measurement Units

  • Ugo Della Croce
  • Andrea Cereatti
  • Martina Mancini
Reference work entry

Abstract

Gait temporal and spatial parameters are effective indicators of the quality of mobility. They are usually estimated in a controlled and dedicated space using relatively expensive instrumentation. The development of wearable technology allowed for the use of inertial measurement units to estimate various gait parameters. The level of accuracy for their determination, required in clinical contexts, can be achieved by carefully processing the data recorded by the sensors. In this chapter, a survey of approaches and methods proposed in the literature for estimating temporal and spatial gait parameters is presented. They differ in the sensor configuration and in the data processing and are applied to healthy and/or pathologic subject groups.

Moreover, an overview of the use of gait temporal and spatial parameters in both straight-ahead walking and turning is presented in a clinimetric context. Applications in the laboratory or clinic are presented as well as in real-life environments.

Keywords

Human movement analysis Gait Inertial measurement unit Angular velocity Acceleration Temporal and spatial gait parameters Turning Clinimetrics Foot clearance Interfoot distance 

References

  1. Alexander NB (1996) Gait disorders in older adults. J Am Geriatr Soc 44:434–451CrossRefGoogle Scholar
  2. Aminian K, Najafi B, Bula C, Leyvraz PF, Robert P (2002) Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J Biomech 35:689–699CrossRefGoogle Scholar
  3. Bamberg SJ, Benbasat AY, Scarborough DM, Krebs DE, Paradiso JA (2008) Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans Inf Technol Biomed 12:413–423CrossRefGoogle Scholar
  4. Barrett RS, Mills PM, Begg RK (2010) A systematic review of the effect of ageing and falls history on minimum foot clearance characteristics during level walking. Gait Posture 32:429–435CrossRefGoogle Scholar
  5. Beauchet O, Allali G, Annweiler C, Bridenbaugh S, Assal F, Kressig RW, Herrmann FR (2009) Gait variability among healthy adults: low and high stride-to-stride variability are both a reflection of gait stability. Gerontology 55:702–706CrossRefGoogle Scholar
  6. Bertoli M, Cereatti A, Trojaniello D, Ravaschio A, Della Croce U (2016) The identification of multiple U-turns in gait: comparison of four trunk IMU-based methods. Proceedings BODYNETS SubmittedGoogle Scholar
  7. Bertuletti S, Cereatti A, Caldara M, Della Croce U (2016) A proximity sensor for the measurement of the inter-foot distance in static and dynamic tasks. Gait Posture 49(Suppl 1):S15CrossRefGoogle Scholar
  8. Best R, Begg R (2008) A method for calculating the probability of tripping while walking. J Biomech 41:1147–1151CrossRefGoogle Scholar
  9. Bohannon RW, Glenney SS (2014) Minimal clinically important difference for change in comfortable gait speed of adults with pathology: a systematic review. J Eval Clin Pract 20:295–300CrossRefGoogle Scholar
  10. Brach JS, Berthold R, Craik R, VanSwearingen JM, Newman AB (2001) Gait variability in community-dwelling older adults. J Am Geriatr Soc 49:1646–1650CrossRefGoogle Scholar
  11. Brach JS, Berlin JE, VanSwearingen JM, Newman AB, Studenski SA (2005) Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed. J Neuroeng Rehabil 2:21CrossRefGoogle Scholar
  12. Bregou Bourgeois A, Mariani B, Aminian K, Zambelli PY, Newman CJ (2014) Spatio-temporal gait analysis in children with cerebral palsy using, foot-worn inertial sensors. Gait Posture 39:436–442CrossRefGoogle Scholar
  13. Cereatti A, Trojaniello D, Della Croce U (2015) Accurately measuring human movement using magneto-inertial sensors: techniques and challenges. In: IEEE international symposium on Inertial Sensors and Systems (ISISS) proceedings, pp 1–4Google Scholar
  14. Courtine G, Schieppati M (2003) Human walking along a curved path. II. Gait features and EMG patterns. Eur J Neurosci 18:191–205CrossRefGoogle Scholar
  15. Crenna P, Carpinella I, Rabuffetti M, Calabrese E, Mazzoleni P, Nemni R, Ferrarin M (2007) The association between impaired turning and normal straight walking in Parkinson’s disease. Gait Posture 26:172–178CrossRefGoogle Scholar
  16. Cummings SR, Nevitt MC (1994) Non-skeletal determinants of fractures: the potential importance of the mechanics of falls. Study of Osteoporotic Fractures Research Group. Osteoporos Int 4(Suppl 1):67–70CrossRefGoogle Scholar
  17. Curtze C, Nutt JG, Carlson-Kuhta P, Mancini M, Horak FB (2015) Levodopa is a double-edged sword for balance and gait in people with Parkinson’s disease. Mov Dis 30:1361–1370CrossRefGoogle Scholar
  18. Dadashi F, Mariani B, Rochat S, Bula CJ, Santos-Eggimann B, Aminian K (2013) Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults. Sensors 14:443–457CrossRefGoogle Scholar
  19. Dalton A, Khalil H, Busse M, Rosser A, van Deursen R, Olaighin G (2013) Analysis of gait and balance through a single triaxial accelerometer in presymptomatic and symptomatic Huntington’s disease. Gait Posture 37:49–54CrossRefGoogle Scholar
  20. de Bruin ED, Hubli M, Hofer P, Wolf P, Murer K, Zijlstra W (2012) Validity and reliability of accelerometer-based gait assessment in patients with diabetes on challenging surfaces. J Aging Res 2012:954378CrossRefGoogle Scholar
  21. de Vet HC, Terwee CB, Bouter LM (2003) Current challenges in clinimetrics. J Clin Epidemiol 56:1137–1141CrossRefGoogle Scholar
  22. Del Din S, Godfrey A, Rochester L (2015) Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson’s disease: toward clinical and at home use. IEEE J Biomed Health InformGoogle Scholar
  23. Del Din S, Godfrey A, Mazza C, Lord S, Rochester L (2016) Free-living monitoring of Parkinson’s disease: lessons from the field. Mov Dis 31:1293–1313CrossRefGoogle Scholar
  24. Dite W, Temple VA (2002) Development of a clinical measure of turning for older adults. Am J Phys Med Rehabil/Assoc Acad Physiatrists 81:857–866; quiz 867–858Google Scholar
  25. El-Gohary M, Pearson S, McNames J, Mancini M, Horak F, Mellone S, Chiari L (2013) Continuous monitoring of turning in patients with movement disability. Sensors 14:356–369CrossRefGoogle Scholar
  26. Esser P, Dawes H, Collett J, Feltham MG, Howells K (2011) Assessment of spatio-temporal gait parameters using inertial measurement units in neurological populations. Gait Posture 34:558–560CrossRefGoogle Scholar
  27. Esser P, Dawes H, Collett J, Feltham MG, Howells K (2012) Validity and inter-rater reliability of inertial gait measurements in Parkinson’s disease: a pilot study. J Neurosci Methods 205:177–181CrossRefGoogle Scholar
  28. Fasano A, Bloem BR (2013) Gait disorders. Continuum 19:1344–1382Google Scholar
  29. Feldman F, Robinovitch SN (2007) Reducing hip fracture risk during sideways falls: evidence in young adults of the protective effects of impact to the hands and stepping. J Biomech 40:2612–2618CrossRefGoogle Scholar
  30. Fino PC, Frames CW, Lockhart TE (2015) Classifying step and spin turns using wireless gyroscopes and implications for fall risk assessments. Sensors 15:10676–10685CrossRefGoogle Scholar
  31. Fino PC, Nussbaum MA, Brolinson PG (2016) Locomotor deficits in recently concussed athletes and matched controls during single and dual-task turning gait: preliminary results. J Neuroeng Rehabil 13:65CrossRefGoogle Scholar
  32. Fleury A, Noury N, Vuillerme N (2007) A fast algorithm to track changes of direction of a person using magnetometers. In: Annual international conference of the IEEE engineering in medicine and biology – proceeding, pp 2311–2314Google Scholar
  33. Foxlin E (2005) Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput Graph Appl 25:38–46CrossRefGoogle Scholar
  34. Fritz S, Lusardi M (2009) White paper: “walking speed: the sixth vital sign”. J Geriatr Phys Ther 32:46–49CrossRefGoogle Scholar
  35. Gill TM, McGloin JM, Gahbauer EA, Shepard DM, Bianco LM (2001) Two recruitment strategies for a clinical trial of physically frail community-living older persons. J Am Geriatr Soc 49:1039–1045CrossRefGoogle Scholar
  36. Ginis P, Nieuwboer A, Dorfman M, Ferrari A, Gazit E, Canning CG, Rocchi L, Chiari L, Hausdorff JM, Mirelman A (2016) Feasibility and effects of home-based smartphone-delivered automated feedback training for gait in people with Parkinson’s disease: a pilot randomized controlled trial. Parkinsonism Relat Disord 22:28–34CrossRefGoogle Scholar
  37. Glaister BC, Bernatz GC, Klute GK, Orendurff MS (2007) Video task analysis of turning during activities of daily living. Gait Posture 25:289–294CrossRefGoogle Scholar
  38. Goldstein M, Harper DC (2001) Management of cerebral palsy: equinus gait. Dev Med Child Neurol 43:563–569CrossRefGoogle Scholar
  39. Gonzalez RC, Alvarez D, Lopez AM, Alvarez JC (2007) Modified pendulum model for mean step length estimation. In: 2007 29th annual international conference of the IEEE engineering in medicine and biology society, pp 1371–1374Google Scholar
  40. Gonzalez RC, Lopez AM, Rodriguez-Uria J, Alvarez D, Alvarez JC (2010) Real-time gait event detection for normal subjects from lower trunk accelerations. Gait Posture 31:322–325CrossRefGoogle Scholar
  41. Greene BR, McGrath D, O’Neill R, O’Donovan KJ, Burns A, Caulfield B (2010) An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput 48:1251–1260CrossRefGoogle Scholar
  42. Han J, Jeon HS, Yi WJ, Jeon BS, Park KS (2009) Adaptive windowing for gait phase discrimination in parkinsonian gait using 3-axis acceleration signals. Med Biol Eng Comput 47:1155–1164CrossRefGoogle Scholar
  43. Hanlon M, Anderson R (2009) Real-time gait event detection using wearable sensors. Gait Posture 30:523–527CrossRefGoogle Scholar
  44. Hartmann A, Luzi S, Murer K, de Bie RA, de Bruin ED (2009) Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait Posture 29:444–448CrossRefGoogle Scholar
  45. Hausdorff JM, Rios DA, Edelberg HK (2001) Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil 82:1050–1056CrossRefGoogle Scholar
  46. Herman T, Giladi N, Hausdorff JM (2011) Properties of the ‘timed up and go’ test: more than meets the eye. Gerontology 57:203–210CrossRefGoogle Scholar
  47. Horak F, King L, Mancini M (2015) Role of body-worn movement monitor technology for balance and gait rehabilitation. Phys Ther 95:461–470CrossRefGoogle Scholar
  48. Houdijk H, Appelman FM, Van Velzen JM, Van der Woude LH, Van Bennekom CA (2008) Validity of DynaPort GaitMonitor for assessment of spatiotemporal parameters in amputee gait. J Rehabil Res Dev 45:1335–1342CrossRefGoogle Scholar
  49. Howell DR, Oldham JR, DiFabio M, Vallabhajosula S, Hall EE, Ketcham CJ, Meehan WP 3rd, Buckley TA (2016) Single-task and dual-task Gait among collegiate athletes of different sport classifications: implications for concussion management. J Appl Biomech 1–25Google Scholar
  50. Hundza SR, Hook WR, Harris CR, Mahajan SV, Leslie PA, Spani CA, Spalteholz LG, Birch BJ, Commandeur DT, Livingston NJ (2014) Accurate and reliable gait cycle detection in Parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng 22:127–137CrossRefGoogle Scholar
  51. Hung TN, Suh YS (2013) Inertial sensor-based two feet motion tracking for gait analysis. Sensors 13:5614–5629CrossRefGoogle Scholar
  52. Huxham F, Gong J, Baker R, Morris M, Iansek R (2006) Defining spatial parameters for non-linear walking. Gait Posture 23:159–163CrossRefGoogle Scholar
  53. Iosa M, Picerno P, Paolucci S, Morone G (2016) Wearable inertial sensors for human movement analysis. Expert Rev Med Devices 13:641–659CrossRefGoogle Scholar
  54. Jahn K, Deutschlander A, Stephan T, Kalla R, Hufner K, Wagner J, Strupp M, Brandt T (2008) Supraspinal locomotor control in quadrupeds and humans. Prog Brain Res 171:353–362CrossRefGoogle Scholar
  55. Jahn K, Zwergal A, Schniepp R (2010) Gait disturbances in old age: classification, diagnosis, and treatment from a neurological perspective. Deutsches Arzteblatt Int 107:306–315; quiz 316Google Scholar
  56. Jankovic J, Nutt JG, Sudarsky L (2001) Classification, diagnosis, and etiology of gait disorders. Adv Neurol 87:119–133Google Scholar
  57. Kavanagh JJ, Menz HB (2008) Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 28:1–15CrossRefGoogle Scholar
  58. Khandoker AH, Lynch K, Karmakar CK, Begg RK, Palaniswami M (2010) Toe clearance and velocity profiles of young and elderly during walking on sloped surfaces. J Neuroeng Rehabil 7:18CrossRefGoogle Scholar
  59. King LA, Mancini M, Priest K, Salarian A, Rodrigues-de-Paula F, Horak F (2012) Do clinical scales of balance reflect turning abnormalities in people with Parkinson’s disease? J Neurol Phys Ther: JNPT 36:25–31CrossRefGoogle Scholar
  60. Kose A, Cereatti A, Della Croce U (2012) Bilateral step length estimation using a single inertial measurement unit attached to the pelvis. J Neuroeng Rehabil 9:9CrossRefGoogle Scholar
  61. Krebs DE, Goldvasser D, Lockert JD, Portney LG, Gill-Body KM (2002) Is base of support greater in unsteady gait? Phys Ther 82:138–147CrossRefGoogle Scholar
  62. Lai DTH, Begg R, Charry E, Palaniswami M, Hill K (2008) Measuring toe clearance using a wireless inertial sensing device. In: ISSNIP 2008 – proceedings of the 2008 international conference on intelligent sensors, sensor networks and information processing, pp 375–380Google Scholar
  63. Lau H, Tong K (2008) The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot. Gait Posture 27:248–257CrossRefGoogle Scholar
  64. Lee JK, Park EJ (2011) Quasi real-time gait event detection using shank-attached gyroscopes. Med Biol Eng Comput 49:707–712CrossRefGoogle Scholar
  65. Lopez-Meyer P, Fulk GD, Sazonov ES (2011) Automatic detection of temporal gait parameters in poststroke individuals. IEEE Trans Inf Technol Biomed 15:594–601CrossRefGoogle Scholar
  66. Lord S, Howe T, Greenland J, Simpson L, Rochester L (2011) Gait variability in older adults: a structured review of testing protocol and clinimetric properties. Gait Posture 34:443–450CrossRefGoogle Scholar
  67. Lord S, Galna B, Coleman S, Yarnall A, Burn D, Rochester L (2014) Cognition and gait show a selective pattern of association dominated by phenotype in incident Parkinson’s disease. Front Aging Neurosci 6:249CrossRefGoogle Scholar
  68. Mancini M, El-Gohary M, Pearson S, McNames J, Schlueter H, Nutt JG, King LA, Horak FB (2015) Continuous monitoring of turning in Parkinson’s disease: rehabilitation potential. NeuroRehabilitation 37:3–10CrossRefGoogle Scholar
  69. Mancini M, Schlueter H, El-Gohary M, Mattek N, Duncan C, Kaye J, Horak FB (2016) Continuous monitoring of turning mobility and its association to falls and cognitive function: a pilot study. J Gerontol A Biol Sci Med Sci 71:1102–1108CrossRefGoogle Scholar
  70. Mannini A, Sabatini AM (2012) Gait phase detection and discrimination between walking-jogging activities using hidden Markov models applied to foot motion data from a gyroscope. Gait Posture 36:657–661CrossRefGoogle Scholar
  71. Mannini A, Sabatini AM (2014) Walking speed estimation using foot-mounted inertial sensors: comparing machine learning and strap-down integration methods. Med Eng Phys 36:1312–1321CrossRefGoogle Scholar
  72. Mannini A, Sabatini AM (2015) A smartphone-centered wearable sensor network for fall risk assessment in the elderly. In: BodyNets '15 proceedings of the 10th EAI international conference on body area networks, pp 167–172Google Scholar
  73. Mariani B, Hoskovec C, Rochat S, Bula C, Penders J, Aminian K (2010) 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J Biomech 43:2999–3006CrossRefGoogle Scholar
  74. Mariani B, Lisco G, Aminian K (2012a) New gait analysis method based on wiimote technology and fusion with inertial sensors. In: Proceedings 1st joint world congress ISPGR & Gait and mental functionGoogle Scholar
  75. Mariani B, Rochat S, Bula CJ, Aminian K (2012b) Heel and toe clearance estimation for gait analysis using wireless inertial sensors. IEEE Trans Biomed Eng 59:3162–3168CrossRefGoogle Scholar
  76. 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 37:229–234CrossRefGoogle Scholar
  77. McCamley J, Donati M, Grimpampi E, Mazza C (2012) An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait Posture 36:316–318CrossRefGoogle Scholar
  78. McGrath D, Greene BR, Walsh C, Caulfield B (2011) Estimation of minimum ground clearance (MGC) using body-worn inertial sensors. J Biomech 44:1083–1088CrossRefGoogle Scholar
  79. Mellone S, Tacconi C, Chiari L (2012) Validity of a smartphone-based instrumented timed up and go. Gait Posture 36:163–165CrossRefGoogle Scholar
  80. Mirelman A, Gurevich T, Giladi N, Bar-Shira A, Orr-Urtreger A, Hausdorff JM (2011) Gait alterations in healthy carriers of the LRRK2 G2019S mutation. Ann Neurol 69:193–197CrossRefGoogle Scholar
  81. Mirelman A, Heman T, Yasinovsky K, Thaler A, Gurevich T, Marder K, Bressman S, Bar-Shira A, Orr-Urtreger A, Giladi N, Hausdorff JM, Consortium LAJ (2013) Fall risk and gait in Parkinson’s disease: the role of the LRRK2 G2019S mutation. Mov Dis 28:1683–1690CrossRefGoogle Scholar
  82. Mirelman A, Weiss A, Buchman AS, Bennett DA, Giladi N, Hausdorff JM (2014) Association between performance on timed up and go subtasks and mild cognitive impairment: further insights into the links between cognitive and motor function. J Am Geriatr Soc 62:673–678CrossRefGoogle Scholar
  83. Mizuike C, Ohgi S, Morita S (2009) Analysis of stroke patient walking dynamics using a tri-axial accelerometer. Gait Posture 30:60–64CrossRefGoogle Scholar
  84. Moe-Nilssen R, Helbostad JL (2004) Estimation of gait cycle characteristics by trunk accelerometry. J Biomech 37:121–126CrossRefGoogle Scholar
  85. Moe-Nilssen R, Nordin E, Lundin-Olsson L, Work Package 3 of European Community Research Network Prevention of Falls Network E (2008) Criteria for evaluation of measurement properties of clinical balance measures for use in fall prevention studies. J Eval Clin Pract 14:236–240CrossRefGoogle Scholar
  86. Moon Y, Sung J, An R, Hernandez ME, Sosnoff JJ (2016) Gait variability in people with neurological disorders: a systematic review and meta-analysis. Hum Mov Sci 47:197–208CrossRefGoogle Scholar
  87. Nagano H, Begg RK, Sparrow WA, Taylor S (2011) Ageing and limb dominance effects on foot-ground clearance during treadmill and overground walking. Clin Biomech 26:962–968CrossRefGoogle Scholar
  88. Nguyen HP, Ayachi F, Lavigne-Pelletier C, Blamoutier M, Rahimi F, Boissy P, Jog M, Duval C (2015) Auto detection and segmentation of physical activities during a timed-up-and-go (TUG) task in healthy older adults using multiple inertial sensors. J Neuroeng Rehabil 12:36CrossRefGoogle Scholar
  89. Novak D, Gorsic M, Podobnik J, Munih M (2014) Toward real-time automated detection of turns during gait using wearable inertial measurement units. Sensors 14:18800–18822CrossRefGoogle Scholar
  90. Owings TM, Grabiner MD (2004) Step width variability, but not step length variability or step time variability, discriminates gait of healthy young and older adults during treadmill locomotion. J Biomech 37:935–938CrossRefGoogle Scholar
  91. Perry J, Burnfield JM (1992) Gait analysis: normal and pathological functionGoogle Scholar
  92. Peruzzi A, Della Croce U, Cereatti A (2011) Estimation of stride length in level walking using an inertial measurement unit attached to the foot: a validation of the zero velocity assumption during stance. J Biomech 44:1991–1994CrossRefGoogle Scholar
  93. Rebula JR, Ojeda LV, Adamczyk PG, Kuo AD (2013) Measurement of foot placement and its variability with inertial sensors. Gait Posture 38:974–980CrossRefGoogle Scholar
  94. Riley PO, Benda BJ, Gill-Body KM, Krebs DE (1995) Phase plane analysis of stability in quiet standing. J Rehabil Res Dev 32:227–235Google Scholar
  95. Sabatini AM (2011) Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing. Sensors 11:1489–1525CrossRefGoogle Scholar
  96. Sabatini AM, Martelloni C, Scapellato S, Cavallo F (2005) Assessment of walking features from foot inertial sensing. IEEE Trans Biomed Eng 52:486–494CrossRefGoogle Scholar
  97. Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR, Aminian K (2004) Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng 51:1434–1443CrossRefGoogle Scholar
  98. Salarian A, Zampieri C, Horak FB, Carlson-Kuhta P, Nutt JG, Aminian K (2009) Analyzing 180 degrees turns using an inertial system reveals early signs of progression of Parkinson’s disease. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference 2009, pp 224–227Google Scholar
  99. Salarian A, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Aminian K (2010) iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng 18:303–310CrossRefGoogle Scholar
  100. Salzman B (2010) Gait and balance disorders in older adults. Am Fam Physician 82:61–68Google Scholar
  101. Sant’Anna A, Salarian A, Wickstrom N (2011) A new measure of movement symmetry in early Parkinson’s disease patients using symbolic processing of inertial sensor data. IEEE Trans Biomed Eng 58:2127–2135CrossRefGoogle Scholar
  102. Schwenk M, Hauer K, Zieschang T, Englert S, Mohler J, Najafi B (2014) Sensor-derived physical activity parameters can predict future falls in people with dementia. Gerontology 60:483–492CrossRefGoogle Scholar
  103. Selles RW, Formanoy MA, Bussmann JB, Janssens PJ, Stam HJ (2005) Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls. IEEE Trans Neural Syst Rehabil Eng 13:81–88CrossRefGoogle Scholar
  104. Shin SH, Park CG (2011) Adaptive step length estimation algorithm using optimal parameters and movement status awareness. Med Eng Phys 33:1064–1071CrossRefGoogle Scholar
  105. Spain RI, St George RJ, Salarian A, Mancini M, Wagner JM, Horak FB, Bourdette D (2012) Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait Posture 35:573–578CrossRefGoogle Scholar
  106. Stolze H, Kuhtz-Buschbeck JP, Drucke H, Johnk K, Illert M, Deuschl G (2001) Comparative analysis of the gait disorder of normal pressure hydrocephalus and Parkinson’s disease. J Neurol Neurosurg Psychiatry 70:289–297CrossRefGoogle Scholar
  107. Streiner DL, Norman GR (1995) Health measurement scales. In: A practical guide to their development and use. Oxford University Press, OxfordGoogle Scholar
  108. 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 305:50–58CrossRefGoogle Scholar
  109. Sudarsky L (2001a) Gait disorders: prevalence, morbidity, and etiology. Adv Neurol 87:111–117Google Scholar
  110. Sudarsky L (2001b) Neurologic disorders of gait. Curr Neurol Neurosci Rep 1:350–356CrossRefGoogle Scholar
  111. Takakusaki K, Tomita N, Yano M (2008) Substrates for normal gait and pathophysiology of gait disturbances with respect to the basal ganglia dysfunction. J Neurol 255(Suppl 4):19–29CrossRefGoogle Scholar
  112. Tate JJ, Milner CE (2010) Real-time kinematic, temporospatial, and kinetic biofeedback during gait retraining in patients: a systematic review. Phys Ther 90:1123–1134CrossRefGoogle Scholar
  113. Thigpen MT, Light KE, Creel GL, Flynn SM (2000) Turning difficulty characteristics of adults aged 65 years or older. Phys Ther 80:1174–1187Google Scholar
  114. Trojaniello D, Cereatti A, Pelosin E, Avanzino L, Mirelman A, Hausdorff JM, Della Croce U (2014a) 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 11:152CrossRefGoogle Scholar
  115. Trojaniello D, Cereatti A, Della Croce U (2014b) Accuracy, sensitivity and robustness of five different methods for the estimation of gait temporal parameters using a single inertial sensor mounted on the lower trunk. Gait Posture 40:487–492CrossRefGoogle Scholar
  116. Trojaniello D, Cereatti A, Bourke A, Aminian K, Della Croce U (2014c) A wearable system for the measurement of the inter-foot distance during gait. In: 20th IMEKO T4 international symposium, pp 765–769.Google Scholar
  117. Trojaniello D, Cereatti A, Ravaschio A, Bandettini M, Della Croce U (2014d) Assessment of gait direction changes during straight-ahead walking in healthy elderly and Huntington disease patients using a shank worn MIMU. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference 2014, pp 2508–2511Google Scholar
  118. Trojaniello D, Ravaschio A, Hausdorff JM, Cereatti A (2015a) Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects. Gait Posture 42:310–316CrossRefGoogle Scholar
  119. Trojaniello D, Cereatti A, Della Croce U (2015b) Foot clearance estimation during overground walking and vertical obstacle passing using shank-mounted MIMUs in healthy and pathological subjects. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference 2015, pp 5505–5508Google Scholar
  120. Valeri N, Trojaniello D, Cereatti A, Aminian K, Della Croce U (2016) Inter-foot distance measured during gait with wearable IMU and IRR sensors. In: Proceedings of the GNB2016 conferenceGoogle Scholar
  121. van Schooten KS, Pijnappels M, Rispens SM, Elders PJ, Lips P, van Dieen JH (2015) Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults. J Gerontol A Biol Sci Med SciGoogle Scholar
  122. Veltink PH, Slycke P, Hemssems J, Buschman R, Bultstra G, Hermens H (2003) Three dimensional inertial sensing of foot movements for automatic tuning of a two-channel implantable drop-foot stimulator. Med Eng Phys 25:21–28CrossRefGoogle Scholar
  123. Verghese J, Wang C, Lipton RB, Holtzer R, Xue X (2007) Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiatry 78:929–935CrossRefGoogle Scholar
  124. Visser JE, Voermans NC, Oude Nijhuis LB, van der Eijk M, Nijk R, Munneke M, Bloem BR (2007) Quantification of trunk rotations during turning and walking in Parkinson’s disease. Clin Neurophysiol 118:1602–1606CrossRefGoogle Scholar
  125. Wada C, Ikeda S, Wada F, Hachisuka K, Ienaga T, Kimuro Y, Tsuji T (2012) Improvement study for measurement accuracy on wireless shoe-type measurement device to support walking rehabilitation. In: Proceedings ICME, pp 471–474Google Scholar
  126. Weiss A, Brozgol M, Dorfman M, Herman T, Shema S, Giladi N, Hausdorff JM (2013) Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabil Neural Repair 27:742–752CrossRefGoogle Scholar
  127. Weiss A, Herman T, Giladi N, Hausdorff JM (2014) Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One 9:e96675CrossRefGoogle Scholar
  128. Weiss A, Herman T, Giladi N, Hausdorff JM (2015) New evidence for gait abnormalities among Parkinson’s disease patients who suffer from freezing of gait: insights using a body-fixed sensor worn for 3 days. J Neural Transm 122:403–410CrossRefGoogle Scholar
  129. Winter DA (1992) Foot trajectory in human gait: a precise and multifactorial motor control task. Phys Ther 72:45–53; discussion 54–46Google Scholar
  130. Yamada M, Higuchi T, Mori S, Uemura K, Nagai K, Aoyama T, Ichihashi N (2012) Maladaptive turning and gaze behavior induces impaired stepping on multiple footfall targets during gait in older individuals who are at high risk of falling. Arch Gerontol Geriatr 54:e102–e108CrossRefGoogle Scholar
  131. Yang CC, Hsu YL, Shih KS, Lu JM (2011) Real-time gait cycle parameter recognition using a wearable accelerometry system. Sensors 11:7314–7326CrossRefGoogle Scholar
  132. Yang S, Zhang JT, Novak AC, Brouwer B, Li Q (2013) Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors. Gait Posture 37:354–358CrossRefGoogle Scholar
  133. Yuwono M, Su SW, Moulton BD, Nguyen HT (2012) Gait cycle spectrogram analysis using a torso-attached inertial sensor. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference 2012, pp 6539–6542Google Scholar
  134. Zampieri C, Salarian A, Carlson-Kuhta P, Aminian K, Nutt JG, Horak FB (2010) The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J Neurol Neurosurg Psychiatry 81:171–176CrossRefGoogle Scholar
  135. Zampieri C, Salarian A, Carlson-Kuhta P, Nutt JG, Horak FB (2011) Assessing mobility at home in people with early Parkinson’s disease using an instrumented timed up and go test. Parkinsonism Relat Disord 17:277–280CrossRefGoogle Scholar
  136. Zijlstra W, Hof AL (2003) Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18:1–10CrossRefGoogle Scholar
  137. Zok M, Mazza C, Della Croce U (2004) Total body Centre of mass displacement estimated using ground reactions during transitory motor tasks: application to step ascent. Med Eng Phys 26:791–798CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ugo Della Croce
    • 1
    • 2
  • Andrea Cereatti
    • 1
    • 2
    • 3
  • Martina Mancini
    • 4
  1. 1.Department of POLCOMINGUniversity of SassariSassariItaly
  2. 2.Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal SystemUniversity of SassariSassariItaly
  3. 3.Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly
  4. 4.Department of NeurologyOregon Health and Science UniversityPortlandUSA

Section editors and affiliations

  • William Scott Selbie
    • 1
  1. 1.Has-Motion Inc.KingstonCanada

Personalised recommendations