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Inertial Measurement Units for Gait Analysis of Parkinson’s Disease Patients

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Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation

Part of the book series: Series in BioEngineering ((SERBIOENG))

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Abstract

Wearable and wireless sensors and technologies have been developed to monitor the gait of Parkinson’s Disease (PD) patients. While there is significant research in this field, however, these are yet not being routinely used by clinicians. A systematic, state-of-the-art literature review of the relevant literature published in the last 10 years (from 2009-July 2019) was conducted in this study. The results reveal that many researchers have not considered confounding factors such as age, gender, medication, and size of the patients. Another important observation is that sensor calibration, and methods of denoising have frequently not been reported. One common shortcoming is that often the studies have been conducted with a small number of participants. This review concludes that research needs to be conducted with a larger number of participants, and where the effects of the confounding factors, calibration methods, and denoising details are reported.

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References

  1. Dietz, V., Zijlstra, W., Prokop, T.W.B.: Leg muscle activation during gait in Parkinson’s disease: adaptation and interlimb coordination. Electroencephal. Clin. Neurophy. 97, 408–415 (1995)

    Google Scholar 

  2. Raccagni, C., Nonnekes, J., Bloem, B.R., Peball, M., Boehme, C., Seppi, K., Wenning, G.K.: Gait and postural disorders in parkinsonism: a clinical approach. J. Neurol. 2, 23 (2019)

    Google Scholar 

  3. Zham, P., Kumar, D.K., Dabnichki, P., Poosapadi Arjunan, S., Raghav, S.: Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Front. Neurol. 8, 435–435 (2017)

    Article  Google Scholar 

  4. Zham, P., Kumar, D., Viswanthan, R., Wong, K., Nagao, K.J., Arjunan, S.P., Raghav, S., Kempster, P.: Effect of levodopa on handwriting tasks of different complexity in Parkinson’s disease: a kinematic study. J. Neurol. 266(6), 1376–1382 (2019)

    Article  Google Scholar 

  5. Brabenec, L., Mekyska, J., Galaz, Z., Rektorova, I.: Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation. J. Neural Transm. 124(3), 303–334 (2017)

    Article  Google Scholar 

  6. Tsuboi, T., Watanabe, H., Tanaka, Y., Ohdake, R., Sato, M., Hattori, M., Kawabata, K., Hara, K., Nakatsubo, D., Maesawa, S., Kajita, Y., Katsuno, M., Sobue, G.: Clinical correlates of repetitive speech disorders in Parkinson’s disease. J. Neurol. Sci. 401, 67–71 (2019)

    Article  Google Scholar 

  7. Buckley, C., Alcock, L., McArdle, R., Rehman, R.Z.U., Del Din, S., Mazzà, C., Yarnall, A.J., Rochester, L.: The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: insights from gait and postural Ccontrol. Brain Sci. 9(2), 34 (2019)

    Google Scholar 

  8. Borzi, L., Varrecchia, M., Olmo, G., Artusi, C.A., Fabbri, M., Rizzone, M.G., Romagnolo, A., Zibetti, M., Lopiano, L.: Home monitoring of motor fluctuations in Parkinson’s disease patients. J. Rel. Intel. Envir. 5(3), 145–162 (2019)

    Article  Google Scholar 

  9. Heijmans, M., Habets, J.G.V., Herff, C., Aarts, J., Stevens, A., Kuijf, M.L., Kubben, P.L.: Monitoring Parkinson’s disease symptoms during daily life: a feasibility study. Parkinson's Dis. 5(1), 21 (2019)

    Google Scholar 

  10. Godinho, C., Domingos, J., Cunha, G., Santos, A.T., Fernandes, R.M., Abreu, D., Gonçalves, N., Matthews, H., Isaacs, T., Duffen, J., Al-Jawad, A., Larsen, F., Serrano, A., Weber, P., Thoms, A., Sollinger, S., Graessner, H., Maetzler, W., Ferreira, J.J.: A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. J. NeuroEng. Rehab. 13(1), 24 (2016)

    Google Scholar 

  11. Ferrari, A., Benedetti, M.G., Pavan, E., Frigo, C., Bettinelli, D., Rabuffetti, M., Crenna, P., Leardini, A.: Quantitative comparison of five current protocols in gait analysis. Gait Posture 28(2), 207–216 (2008)

    Article  Google Scholar 

  12. Shipston, O., Hoeritzauer, I., Edwards, M., Reuber, M., Carson, A., Stone, J.: Screening for functional neurological disorders by questionnaire. J. Psychosom. Res. 119, 65–73 (2019)

    Article  Google Scholar 

  13. Morris, M.E., Dreher, T.: Gait and posture virtual special issue “gait complexity in Parkinson’s disease”. Gait & Posture (2018)

    Google Scholar 

  14. Fernandez, N.B., Hars, M., Trombetti, A., Vuilleumier, P.: Age-related changes in attention control and their relationship with gait performance in older adults with high risk of falls. Neuroimage 189, 551–559 (2019)

    Article  Google Scholar 

  15. Modarresi, S., Divine, A., Grahn, J.A., Overend, T.J., Hunter, S.W.: Gait parameters and characteristics associated with increased risk of falls in people with dementia: a systematic review. International Psychogeriatrics, 1–17 (2018)

    Google Scholar 

  16. Din, S., Godfrey, A., Mazzà, C., Lord, S., Rochester, L.: Free‐living monitoring of Parkinson's disease: Lessons from the field. Movement Disorders, 31 (2016)

    Google Scholar 

  17. Thorp, J.E., Adamczyk, P.G., Ploeg, H.-L., Pickett, K.A.: Monitoring motor symptoms during activities of daily living in individuals with Parkinson’s disease. Front. Neurol. 9, 1036–1036 (2018)

    Article  Google Scholar 

  18. Ciuti, G., Ricotti, L., Menciassi, A., Dario, P.: MEMS sensor technologies for human centred applications in healthcare, physical Activities, safety and environmental sensing: a review on research activities in Italy. Sensors (Basel, Switzerland) 15(3), 6441–6468 (2015)

    Article  Google Scholar 

  19. Qiu, S., Liu, L., Zhao, H., Wang, Z., Jiang, Y.: MEMS inertial sensors based gait analysis for rehabilitation assessment via multi-sensor fusion. Micromachines 9(9), 442 (2018)

    Google Scholar 

  20. Mancini, M., Chiari, L., Holmstrom, L., Salarian, A., Horak, F.B.: Validity and reliability of an IMU-based method to detect APAs prior to gait initiation. Gait Posture 43, 125–131 (2016)

    Article  Google Scholar 

  21. Zihajehzadeh, S., Loh, D., Lee, M., Hoskinson, R., Park, E.J.: A cascaded two-step kalman filter for estimation of human body segment orientation using MEMS-IMU. IEEE Eng. Med. Biol. Soc., 6270–6273 (2014)

    Google Scholar 

  22. Pepa, L., Verdini, F., Capecci, M., Ceravolo, M.G.: IMU based detection of freezing of gait and festination in Parkinson’s disease. IEEE International Conference on Consumer Electronics (ICCE), pp. 212–215 (2015)

    Google Scholar 

  23. Lowe, S.A., ÓLaighin, G.: Monitoring human health behaviour in one's living environment: a technological review. Med. Eng. Phys. 36(2), 147–168 (2014)

    Google Scholar 

  24. Weiss, A., Sharifi, S., Plotnik, M., van Vugt, J.P., Giladi, N., Hausdorff, J.M.: Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil. Neural Repair 25(9), 810–818 (2011)

    Article  Google Scholar 

  25. Weiss, A., Herman, T., Giladi, N., Hausdorff, J.M.: Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS ONE 9(5), e96675–e96675 (2014)

    Article  Google Scholar 

  26. Allcock, L.M., Rowan, E.N., Steen, I.N., Wesnes, K., Kenny, R.A., Burn, D.J.: Impaired attention predicts falling in Parkinson’s disease. Parkinsonism & Rel. Disord. 15(2), 110–115 (2009)

    Article  Google Scholar 

  27. Contreras, A., Grandas, F.: Risk of falls in Parkinson’s disease: a cross-sectional study of 160 patients. Parkinson’s Dis. 2012, 1–10 (2012)

    Article  Google Scholar 

  28. Latt, M.D., Lord, S.R., Morris, J.G., Fung, V.S.: Clinical and physiological assessments for elucidating falls risk in Parkinson’s disease. Movem. Disord. 24(9), 1280–1289 (2009)

    Article  Google Scholar 

  29. Balash, Y., Peretz, C., Leibovich, G., Herman, T., Hausdorff, J.M., Giladi, N.: Falls in outpatients with Parkinson’s disease: frequency, impact and identifying factors. J. Neurol. 252(11), 1310–1315 (2005)

    Article  Google Scholar 

  30. Bloem, B.R., Grimbergen, Y.A., Cramer, M., Willemsen, M., Zwinderman, A.H.: Prospective assessment of falls in Parkinson’s disease. J. Neurol. 248(11), 950–958 (2001)

    Article  Google Scholar 

  31. Goetz, C.G., Tilley, B.C., Shaftman, S.R., Stebbins, G.T., Fahn, S., Martinez-Martin, P., Poewe, W.: Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Movem. Disord. 23(15), 2129–2170 (2008)

    Article  Google Scholar 

  32. Adams, S.T., Leveson, S.H.: Clinical prediction rules. BMJ 344, 8312–8320 (2012)

    Article  Google Scholar 

  33. Kerr, G.K., Worringham, C.J., Cole, M.H., Lacherez, P.F., Wood, J.M., Silburn, P.A.: Predictors of future falls in Parkinson disease. J. Neurol. 75(2), 116–124 (2010)

    Google Scholar 

  34. Matinolli, M., Korpelainen, J.T., Sotaniemi, K.A., Myllyla, V.V., Korpelainen, R.: Recurrent falls and mortality in Parkinson’s disease: a prospective two-year follow-up study. Acta Neurol. Scand. 123(3), 193–200 (2011)

    Article  Google Scholar 

  35. Yang, K., Xiong, W.X., Liu, F.T., Sun, Y.M., Luo, S., Ding, Z.T., Wu, J.J., Wang, J.; Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications. Annals Transl. Med. 4(5), 90 (2016)

    Google Scholar 

  36. Zampieri, C., Salarian, A., Carlson-Kuhta, P., Aminian, K., Nutt, J.G., Horak, F.B.: The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J. Neurol. Neuros. Psyc. 81(2), 171 (2010)

    Google Scholar 

  37. Esser, P., Dawes, H., Collett, J., Feltham, M.G., Howells, K.: Assessment of spatio-temporal gait parameters using inertial measurement units in neurological populations. Gait Posture 34(4), 558–560 (2011)

    Article  Google Scholar 

  38. Espay, A.J., Baram, Y., Dwivedi, A.K., Shukla, R., Gartner, M., Gaines, L., Duker, A.P., Revilla, F.J.: At-home training with closed-loop augmented-reality cueing device for improving gait in patients with Parkinson disease. J. Rehabil. Res. Dev. 47(6), 573–581 (2010)

    Article  Google Scholar 

  39. Salarian, A., Horak, F.B., Zampieri, C., Carlson-Kuhta, P., Nutt, J.G., Aminian, K.: iTUG, a sensitive and reliable measure of mobility. IEEE Trans. Neural Syst. Rehab. Eng. Publ. IEEE Eng. Med. Biol. Soc. 18(3), 303–310 (2010)

    Article  Google Scholar 

  40. Rochester, L., Baker, K., Hetherington, V., Jones, D., Willems, A.-M., Kwakkel, G., van Wegen, E., Lim, I., Nieuwboer, A.: Evidence for motor learning in Parkinson’s disease: acquisition, automaticity and retention of cued gait performance after training with external rhythmical cues. Brain Res. 1319, 103–111 (2010)

    Article  Google Scholar 

  41. Palmerini, L., Mellone, S., Avanzolini, G., Valzania, F., Chiari, L.: Quantification of motor impairment in Parkinson’s disease using an instrumented timed up and go test. IEEE Trans. Neural Syst. Rehab. Eng. 21(4), 664–673 (2013)

    Article  Google Scholar 

  42. Esser, P., Dawes, H., Collett, J., Howells, K.: Insights into gait disorders: Walking variability using phase plot analysis, Parkinson’s disease. Gait Posture 38(4), 648–652 (2013)

    Article  Google Scholar 

  43. Herman, T., Weiss, A., Brozgol, M., Giladi, N., Hausdorff, J.M.: Gait and balance in Parkinson’s disease subtypes: objective measures and classification considerations. J. Neurol. 261(12), 2401–2410 (2014)

    Article  Google Scholar 

  44. Djurić-Jovicić, M.D., Jovicić, N.S., Radovanović, S.M., Kresojević, N.D., Kostić, V.S., Popović, M.B.: Quantitative and qualitative gait assessments in Parkinson’s disease patients. Vojnosanit. Pregl. 71(9), 809–816 (2014)

    Article  Google Scholar 

  45. Brodie, M., Canning, C.G., Beijer, T.R., Lord, S.R.: Uncontrolled head oscillations in people with parkinson’s disease may reflect an inability to respond to perturbations while walking. Physiol. Meas. 36(5), 873–881 (2015)

    Article  Google Scholar 

  46. Trojaniello, D., Ravaschio, A., Hausdorff, J.M., Cereatti, A.: 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(3), 310–316 (2015)

    Article  Google Scholar 

  47. Curtze, C., Nutt, J.G., Carlson-Kuhta, P., Mancini, M., Horak, F.B.: Levodopa is a double-edged sword for balance and gait in people with Parkinson’s disease. Mov. Disord. 30(10), 1361–1370 (2015)

    Article  Google Scholar 

  48. Håkan, N., Martin Benka, W., Erika, F., Agneta, S., Maria, H.: Accelerometer cut points for physical activity assessment of older adults with Parkinson's disease. PLoS One 10(9), e0135899 (2015)

    Google Scholar 

  49. Kleiner, A., Galli, M., Gaglione, M., Hildebrand, D., Sale, P., Albertini, G., Stocchi, F., De Pandis, M.F.: The Parkinsonian gait spatiotemporal parameters quantified by a single inertial sensor before and after automated mechanical peripheral stimulation treatment. Parkinson's Disease 2015 (2015)

    Google Scholar 

  50. Del Din, S., Godfrey, A., Rochester, L.: 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 Inform. 20(3), 838–847 (2016)

    Article  Google Scholar 

  51. Ferrari, A., Ginis, P., Hardegger, M., Casamassima, F., Rocchi, L., Chiari, L.: A mobile kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters. IEEE Trans. Neural Syst. Rehab. Eng. 24(7), 764–773 (2016)

    Article  Google Scholar 

  52. Elshehabi, M.;Maier, K. S.;Hasmann, S. E.;Nussbaum, S.;Herbst, H.;Heger, T.;Berg, D.;Hobert, M. A.;Maetzler, W. Limited effect of dopaminergic medication on straight walking and turning in early-to-moderate Parkinson's disease during single and dual tasking. Frontiers in Aging Neuroscience 2016, 8,(4).

    Google Scholar 

  53. Horak, F.B., Mancini, M., Carlson-Kuhta, P., Nutt, J.G., Salarian, A.: Balance and gait represent independent domains of mobility in Parkinson disease. Phys. Therapy 96(9), 1364 (2016)

    Google Scholar 

  54. Hatanaka, N., Sato, K., Hishikawa, N., Takemoto, M., Ohta, Y., Yamashita, T., Abe, K.: Comparative gait analysis in progressive supranuclear palsy and Parkinson’s disease. Eur. Neurol. 75(5–6), 282–289 (2016)

    Article  Google Scholar 

  55. Curtze, C., Nutt, J.G., Carlson-Kuhta, P., Mancini, M., Horak, F.B.: Objective gait and balance impairments relate to balance confidence and perceived mobility in people with Parkinson disease. Phys. Ther. 96(11), 1734–1743 (2016)

    Article  Google Scholar 

  56. Micó-Amigo, M., Kingma, I., Faber, G., Kunikoshi, A., Uem, J., Lummel, R., Maetzler, W., Dieën, J.: Is the assessment of 5 meters of gait with a single body-fixed-sensor enough to recognize idiopathic Parkinson’s disease-associated gait? Ann. Biomed. Eng. 45(5), 1266–1278 (2017)

    Article  Google Scholar 

  57. Warlop, T., Detrembleur, C., Lopez, M., Stoquart, G., Lejeune, T., Jeanjean, A.; Does nordic walking restore the temporal organization of gait variability in Parkinsons disease? J. NeuroEng. Rehab. 14(1) (2017)

    Google Scholar 

  58. Ginis, P., Heremans, E., Ferrari, A., Dockx, K., Canning, C.G., Nieuwboer, A.: Prolonged walking with a wearable system providing intelligent auditory input in people with Parkinson’s disease. Front. Neurol. 8, 128 (2017)

    Article  Google Scholar 

  59. de Souza Fortaleza, A.C., Mancini, M., Carlson-Kuhta, P., King, L.A., Nutt, J.G., Chagas, E.F., Freitas, I.F., Horak, F.B.: Dual task interference on postural sway, postural transitions and gait in people with Parkinson’s disease and freezing of gait. Gait Posture 56, 76–81 (2017)

    Article  Google Scholar 

  60. Gougeon, M.-A., Zhou, L., Nantel, J.: Nordic Walking improves trunk stability and gait spatial-temporal characteristics in people with Parkinson disease. NeuroRehabilitation 41(1), 205–210 (2017)

    Article  Google Scholar 

  61. Kristina, B., Felix, B., Jennifer, S., Markus, A.H., Marc, H., Till, G., Janet, M.T.v.U., Inga, L.-S., Walter, M.: No relevant association of kinematic gait parameters with health-related quality of life in Parkinson's disease. PLoS One 12(5), e0176816 (2017)

    Google Scholar 

  62. Montero-Odasso, M., Pieruccini-Faria, F., Bartha, R., Black, S., Finger, E., Freedman, M., Greenberg, B., Grimes, D., Hegele, R., Hudson, C., Kleinstiver, P., Lang, A., Masellis, M., McLaughlin, P., Munoz, D., Strother, S., Swartz, R., Symons, S., Tartaglia, M., Zinman, L., Strong, M., McIlroy, W.: Motor phenotype in neurodegenerative disorders: gait and balance platform study design protocol for the ontario neurodegenerative research initiative. J. Alzheimers Dis. 59(2), 707–721 (2017)

    Article  Google Scholar 

  63. Johannes, C.M.S., Jens, B., Franz, M., Julia, G., Zacharias, K., Samuel, R., Heiko, G., Kamiar, A., Bjoern, M.E., Jürgen, W., Jochen, K.: Wearable sensors objectively measure gait parameters in Parkinson's disease. PLoS One 12(10), e0183989 (2017)

    Google Scholar 

  64. Rovini, E., Maremmani, C., Cavallo, F.: How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front. Neurosci. 11, 555 (2017)

    Article  Google Scholar 

  65. Raccagni, C., Gaßner, H., Eschlboeck, S., Boesch, S., Krismer, F., Seppi, K., Poewe, W., Eskofier, B.M., Winkler, J., Wenning, G., Klucken, J.: Sensor‐based gait analysis in atypical parkinsonian disorders. Brain Behav. 8(6) (2018)

    Google Scholar 

  66. Bertoli, M., Cereatti, A., Trojaniello, D., Avanzino, L., Pelosin, E., Del Din, S., Rochester, L., Ginis, P., Bekkers, E.M.J., Mirelman, A., Hausdorff, J.M., Della Croce, U.: Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults. Biomed. Eng. Online 17(1) (2018)

    Google Scholar 

  67. Zago, M., Sforza, C., Pacifici, I., Cimolin, V., Camerota, F., Celletti, C., Condoluci, C., De. Pandis, M.F., Galli, M.: Gait evaluation using inertial measurement units in subjects with Parkinson’s disease. J. Electromyogr. Kinesiol. 42, 44–48 (2018)

    Article  Google Scholar 

  68. Creaby, M.W., Cole, M.H.: Gait characteristics and falls in Parkinson’s disease: a systematic review and meta-analysis. Parkinsonism Relat. Disord. 57, 1–8 (2018)

    Article  Google Scholar 

  69. Pau, M., Corona, F., Pili, R., Casula, C., Guicciardi, M., Cossu, G., Murgia, M.: Quantitative assessment of gait parameters in people with Parkinson’s disease in laboratory and clinical setting: are the measures interchangeable? Neurol. Int. 10(2), 7729–7729 (2018)

    Article  Google Scholar 

  70. Caramia, C., Torricelli, D., Schmid, M., Munoz-Gonzalez, A., Gonzalez-Vargas, J., Grandas, F., Pons, J.L.: IMU-based classification of Parkinson’s disease from gait: a sensitivity analysis on sensor location and feature selection. IEEE J. Biomed. Health Inform. 22(6), 1765–1774 (2018)

    Article  Google Scholar 

  71. Felix, P.B., Jennifer, S., Kristina, B., Markus, A.H., Carina, A., Yvonne, G.W., Sven, P., Nils, G.M., Christian, S., Clint, H., Walter, M.: Wearables for gait and balance assessment in the neurological ward—study design and first results of a prospective cross-sectional feasibility study with 384 inpatients. BMC Neurol. 18(1), 1–8 (2018)

    Google Scholar 

  72. Aich, S., Pradhan, P.M., Park, J., Sethi, N., Vathsa, V.S.S., Kim, H.C.: A validation study of Freezing of Gait (FoG) detection and machine-learning-based FoG prediction using estimated gait characteristics with a wearable accelerometer. Sensors (Basel, Switzerland) 18(10), E3287 (2018)

    Google Scholar 

  73. Keloth, S., Viswanathan, R., Jelfs, B., Arjunan, S., Raghav, S., Kumar, D.: Which gait parameters and walking patterns show the significant differences between Parkinson's disease and healthy participants? Biosensors 9(2) (2019)

    Google Scholar 

  74. Raffegeau, T.E., Krehbiel, L.M., Kang, N., Thijs, F.J., Altmann, L.J.P., Cauraugh, J.H., Hass, C.J.: A meta-analysis: Parkinson’s disease and dual-task walking. Parkinsonism Relat. Disord. 62, 28–35 (2019)

    Article  Google Scholar 

  75. Brognara, L., Palumbo, P., Grimm, B., Palmerini, L.: Assessing gait in parkinson’s disease using wearable motion sensors: a systematic review. Diseases 7(1), 18 (2019)

    Google Scholar 

  76. Mirelman, A., Bonato, P., Camicioli, R., Ellis, T.D., Giladi, N., Hamilton, J.L., Hass, C.J., Hausdorff, J.M., Pelosin, E., Almeida, Q.J.: Gait impairments in Parkinson’s disease. Lancet Neurol. 18(7), 697–708 (2019)

    Article  Google Scholar 

  77. Sweeney, D., Quinlan, L.R., Browne, P., Richardson, M., Meskell, P.G.O.L.: A technological review of wearable cueing devices addressing freezing of gait in Parkinson's disease. Sensors (Basel, Switzerland) 19(6) (2019)

    Google Scholar 

  78. Krishnan, S., Wu, Y.: Statistical analysis of gait rhythm in patients with Parkinson’s disease. IEEE Trans. Neur. Syst. Rehab. Eng. 18(2), 150–158 (2010)

    Article  Google Scholar 

  79. Bryant, M.S., Rintala, D.H., Hou, J.G., Charness, A.L., Fernandez, A.L., Collins, R.L., Baker, J., Lai, E.C., Protas, E.J.: Gait variability in Parkinson’s disease: influence of walking speed and dopaminergic treatment. Neurol. Res. 33(9), 959–964 (2011)

    Article  Google Scholar 

  80. Roemmich, R.T., Nocera, J.R., Vallabhajosula, S., Amano, S., Naugle, K.M., Stegemoller, E.L., Hass, C.J.: Spatiotemporal variability during gait initiation in Parkinson’s disease. Gait Posture 36(3), 340–343 (2012)

    Article  Google Scholar 

  81. Hove, M.J., Suzuki, K., Uchitomi, H., Orimo, S., Miyake, Y.: Interactive rhythmic auditory stimulation reinstates natural 1/ f timing in gait of Parkinson's patients. PLoS One 7(3), e32600 (2012)

    Google Scholar 

  82. Galna, B., Lord, S., Rochester, L.: Is gait variability reliable in older adults and Parkinson’s disease? Towards an optimal testing protocol. Gait Posture 37(4), 580–585 (2013)

    Article  Google Scholar 

  83. Kirchner, M., Schubert, P., Liebherr, M., Haas, C.T.: Detrended fluctuation analysis and adaptive fractal analysis of stride time data in Parkinson's disease: stitching together short gait trials. PLoS One 9(1), e85787 (2014)

    Google Scholar 

  84. Bello, O., Sánchez, J.A., Vazquez-Santos, C., Fernandez-Del-Olmo, M.: Spatiotemporal parameters of gait during treadmill and overground walking in Parkinson’s disease. J. Parkinsons Dis. 4, 33–36 (2014)

    Article  Google Scholar 

  85. Bryant, M.S., Rintala, D.H., Hou, J.G., Collins, R.L., Protas, E.J.: Gait variability in Parkinson’s disease: levodopa and walking direction. Acta Neurol. Scand. 134(1), 83–86 (2016)

    Article  Google Scholar 

  86. Keloth, S., Arjunan, S.P., Kumar, D.: Computing the variations in the self-similar properties of the various gait intervals in Parkinson disease patients. Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2017, 2434–2437 (2017)

    Google Scholar 

  87. Rennie, L., Löfgren, N., Moe-Nilssen, R., Opheim, A., Dietrichs, E., Franzén, E.: The reliability of gait variability measures for individuals with Parkinson’s disease and healthy older adults—the effect of gait speed. Gait Posture 62, 505–509 (2018)

    Article  Google Scholar 

  88. Jankovic, J., McDermott, M., Carter, J., Gauthier, S., Goetz, C., Golbe, L., Huber, S., Koller, W., Olanow, C., Shoulson, I., et al.: Variable expression of Parkinson’s disease: a base-line analysis of the DATATOP cohort. The Parkinson study group. Neurology 40(10), 1529–1534 (1990)

    Article  Google Scholar 

  89. Olmo, D., Cudeiro, J.: Temporal variability of gait in Parkinson disease: effects of a rehabilitation programme based on rhythmic sound cues. Parkinsonism Relat. Disord. 11(1), 25–33 (2005)

    Article  Google Scholar 

  90. Frenkel, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., Hausdorff, J.M.: Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson’s disease. Movem. Disord. 20(9), 1109–1114 (2005)

    Article  Google Scholar 

  91. Baltadjieva, R., Giladi, N., Gruendlinger, L., Peretz, C., Hausdorff, J.M.: Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson’s disease. Eur. J. Neurosci. 24, 1815–1820 (2006)

    Google Scholar 

  92. Bartsch, Plotnik, M., Kantelhardt, J.M., Havlin, S., Giladi, N.: Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of parkinson’s disease. Physica A 383, 455–465 (2007)

    Google Scholar 

  93. Henmi, O., Shiba, Y., Saito, T., Tsuruta, H., Takeuchi, A., Shirataka, M., Obuchi, S., Kojima, M.N.I.: Spectral analysis of gait variability of stride interval time seires: comparison of young, elderly and Parkinson's disease patients. J. Phys. Therapy Sci. 21(2), 105–111 (2009)

    Google Scholar 

  94. Hausdorff, J.M.: Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos Interdisc. J. Nonlin. Sci. 19(2), 026113 (2009)

    Google Scholar 

  95. Henmi, O., Shiba, Y., Saito, T., Tsuruta, H., Takeuchi, A., Shirataka, M., Obuchi, S., Kojima, M.N.I.: Spectral analysis of gait variability of stride interval time series: comparison of young, elderly and Parkinson's disease patients. J. Phy. Therapy Sci. 21(2), 105–111 (2009)

    Google Scholar 

  96. Olmo, D.J.C.: Temporal variability of gait in Parkinson disease: effects of a rehabilitation programme based on rhythmic sound cues. Parkinsonism Relat. Disord. 11(1), 25–33 (2005)

    Google Scholar 

  97. Zampieri, C., Salarian, A., Carlson-Kuhta, P., Aminian, K., Nutt, J.G., Horak, F.B.: The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 81(2), 171–176 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Biosignals Lab, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia

    Sana M. Keloth, Peter John Radcliffe & Dinesh Kumar

  2. Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Chennai, India

    Sridhar P. Arjunan

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  1. Sana M. Keloth
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  2. Sridhar P. Arjunan
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  3. Peter John Radcliffe
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  4. Dinesh Kumar
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Correspondence to Sridhar P. Arjunan .

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  1. Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

    Sridhar P. Arjunan

  2. Biosignals lab, School of Engineering, RMIT University, Melbourne, VIC, Australia

    Dinesh Kant Kumar

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Keloth, S.M., Arjunan, S.P., Radcliffe, P.J., Kumar, D. (2022). Inertial Measurement Units for Gait Analysis of Parkinson’s Disease Patients. In: Arjunan, S.P., Kumar, D.K. (eds) Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-3056-9_6

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