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Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis

  • Demyelinating Disorders (J. Bernard & M. Cameron, Section Editors)
  • Published:
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Abstract

Purpose of Review

Walking impairments are highly prevalent in persons with multiple sclerosis (PwMS) and are associated with reduced quality of life. Walking is traditionally quantified with various measures, including patient self-reports, clinical rating scales, performance measures, and advanced lab-based movement analysis techniques. Yet, the majority of these measures do not fully characterize walking (i.e., gait quality) nor adequately reflect walking in the real world (i.e., community ambulation) and have limited timescale (only measure walking at a single point in time). We discuss the potential of wearable sensors to provide sensitive, objective, and easy-to-use assessment of community ambulation in PwMS.

Recent Findings

Wearable technology has the ability to measure all aspects of gait in PwMS yet is under-studied in comparison with other populations (e.g., older adults). Within the studies focusing on PwMS, half that measure pace collected free-living data, while only one study explored gait variability in free-living conditions. No studies explore gait asymmetry or complexity in free-living conditions.

Summary

Wearable technology has the ability to provide objective, comprehensive, and sensitive measures of gait in PwMS. Future research should investigate this technology’s ability to accurately assess free-living measures of gait quality, specifically gait asymmetry and complexity.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Society NMS. What is MS? 2019 [cited 2019; Available from: https://www.nationalmssociety.org/What-is-MS. Accessed 11 June 2019.

  2. Keegan BM, Noseworthy JH. Multiple sclerosis. Annu Rev Med. 2002;53:285–302.

    CAS  PubMed  Google Scholar 

  3. Society NMS. What causes MS? 2019; Available from: https://www.nationalmssociety.org/What-is-MS/What-Causes-MS. Accessed 11 June 2019.

  4. Cameron MH, Nilsagard Y. Balance, gait, and falls in multiple sclerosis. Handb Clin Neurol. 2018;159:237–50.

    PubMed  Google Scholar 

  5. Motl RW, Learmonth YC. Neurological disability and its association with walking impairment in multiple sclerosis: brief review. Neurodegener Dis Manag. 2014;4(6):491–500.

    PubMed  Google Scholar 

  6. VanSwearingen JM, Studenski SA. Aging, motor skill, and the energy cost of walking: implications for the prevention and treatment of mobility decline in older persons. J Gerontol A Biol Sci Med Sci. 2014;69(11):1429–36.

    PubMed  PubMed Central  Google Scholar 

  7. Capaday C. The special nature of human walking and its neural control. Trends Neurosci. 2002;25(7):370–6.

    CAS  PubMed  Google Scholar 

  8. Lord S, Galna B, Rochester L. Moving forward on gait measurement: toward a more refined approach. Mov Disord. 2013;28(11):1534–43.

    PubMed  Google Scholar 

  9. Lipsitz LA, Goldberger AL. Loss of ‘complexity’ and aging. Potential applications of fractals and chaos theory to senescence. JAMA. 1992;267(13):1806–9.

    CAS  PubMed  Google Scholar 

  10. Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging. 2002;23(1):23–6.

    PubMed  Google Scholar 

  11. Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci. 2011;30(5):869–88.

    PubMed  PubMed Central  Google Scholar 

  12. •• Comber L, Galvin R, Coote S. Gait deficits in people with multiple sclerosis: a systematic review and meta-analysis. Gait Posture. 2017;51:25–35. A recent systematic review discussing the impact of multiple sclerosis on spatiotemporal parameters of gait in lab-based settings.

    PubMed  Google Scholar 

  13. Givon U, Zeilig G, Achiron A. Gait analysis in multiple sclerosis: characterization of temporal-spatial parameters using GAITRite functional ambulation system. Gait Posture. 2009;29(1):138–42.

    PubMed  Google Scholar 

  14. Moon Y, et al. Stride-time variability and fall risk in persons with multiple sclerosis. Mult Scler Int. 2015;2015:964790.

    PubMed  PubMed Central  Google Scholar 

  15. Kalron A. Gait variability across the disability spectrum in people with multiple sclerosis. J Neurol Sci. 2016;361:1–6.

    PubMed  Google Scholar 

  16. Socie MJ, Motl RW, Pula JH, Sandroff BM, Sosnoff JJ. Gait variability and disability in multiple sclerosis. Gait Posture. 2013;38(1):51–5.

    PubMed  Google Scholar 

  17. Larocca NG. Impact of walking impairment in multiple sclerosis: perspectives of patients and care partners. Patient. 2011;4(3):189–201.

    PubMed  Google Scholar 

  18. Hobart J, et al. The multiple sclerosis impact scale (MSIS-29): a new patient-based outcome measure. Brain. 2001;124(Pt 5):962–73.

    CAS  PubMed  Google Scholar 

  19. Martin CL, Phillips BA, Kilpatrick TJ, Butzkueven H, Tubridy N, McDonald E, et al. Gait and balance impairment in early multiple sclerosis in the absence of clinical disability. Mult Scler. 2006;12(5):620–8.

    CAS  PubMed  Google Scholar 

  20. Solomon AJ, Jacobs JV, Lomond KV, Henry SM. Detection of postural sway abnormalities by wireless inertial sensors in minimally disabled patients with multiple sclerosis: a case-control study. J Neuroeng Rehabil. 2015;12:74.

    PubMed  PubMed Central  Google Scholar 

  21. Sosnoff JJ, Sandroff BM, Motl RW. Quantifying gait abnormalities in persons with multiple sclerosis with minimal disability. Gait Posture. 2012;36(1):154–6.

    PubMed  Google Scholar 

  22. Goldman MD, Marrie RA, Cohen JA. Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls. Mult Scler. 2008;14(3):383–90.

    PubMed  Google Scholar 

  23. Motl RW, Goldman MD, Benedict RH. Walking impairment in patients with multiple sclerosis: exercise training as a treatment option. Neuropsychiatr Dis Treat. 2010;6:767–74.

    PubMed  PubMed Central  Google Scholar 

  24. Goldman MD, Motl RW, Rudick RA. Possible clinical outcome measures for clinical trials in patients with multiple sclerosis. Ther Adv Neurol Disord. 2010;3(4):229–39.

    PubMed  PubMed Central  Google Scholar 

  25. Motl RW, Cohen JA, Benedict R, Phillips G, LaRocca N, Hudson LD, et al. Validity of the timed 25-foot walk as an ambulatory performance outcome measure for multiple sclerosis. Mult Scler. 2017;23(5):704–10.

    PubMed  PubMed Central  Google Scholar 

  26. Gijbels D, Dalgas U, Romberg A, de Groot V, Bethoux F, Vaney C, et al. Which walking capacity tests to use in multiple sclerosis? A multicentre study providing the basis for a core set. Mult Scler. 2012;18(3):364–71.

    PubMed  Google Scholar 

  27. Hobart J, Blight AR, Goodman A, Lynn F, Putzki N. Timed 25-foot walk: direct evidence that improving 20% or greater is clinically meaningful in MS. Neurology. 2013;80(16):1509–17.

    PubMed  Google Scholar 

  28. Kalron A, Aloni R. Contrasting relationship between depression, quantitative gait characteristics and self-report walking difficulties in people with multiple sclerosis. Mult Scler Relat Disord. 2018;19:1–5.

    PubMed  Google Scholar 

  29. Bennett SE, Bromley LE, Fisher NM, Tomita MR, Niewczyk P. Validity and reliability of four clinical gait measures in patients with multiple sclerosis. Int J MS Care. 2017;19(5):247–52.

    PubMed  PubMed Central  Google Scholar 

  30. Cofre Lizama LE, et al. The use of laboratory gait analysis for understanding gait deterioration in people with multiple sclerosis. Mult Scler. 2016;22(14):1768–76.

    PubMed  Google Scholar 

  31. Peebles AT, Reinholdt A, Bruetsch AP, Lynch SG, Huisinga JM. Dynamic margin of stability during gait is altered in persons with multiple sclerosis. J Biomech. 2016;49(16):3949–55.

    PubMed  PubMed Central  Google Scholar 

  32. •• Moon Y, et al. Monitoring gait in multiple sclerosis with novel wearable motion sensors. PLoS One. 2017;12(2):e0171346. This article provides detailed descriptions of validity of flexible adherable sensors to quantify gait quality including rhythm across a range of disability in PwMS. This suggests that sensor-based assessment is able to distinguish levels of disability.

    PubMed  PubMed Central  Google Scholar 

  33. •• McGinnis RS, et al. A machine learning approach for gait speed estimation using skin-mounted wearable sensors: from healthy controls to individuals with multiple sclerosis. PLoS One. 2017;12(6). This article provides a major advancement by showing that machine learning algorithms can be used to accurately measure gait speed using wearable conformal sensors and that gait speed is correlated to EDSS in PwMS. This suggests the feasibility of measuring parameters such as gait speed in the home environment to characterize mobility impairment and its fluctuations.

    PubMed  PubMed Central  Google Scholar 

  34. Spain RI, St. George RJ, Salarian A, Mancini M, Wagner JM, Horak FB, et al. Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait Posture. 2012;35(4):573–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Kieseier BC, Pozzilli C. Assessing walking disability in multiple sclerosis. Mult Scler. 2012;18(7):914–24.

    PubMed  Google Scholar 

  36. • Takayanagi N, et al. Relationship between daily and in-laboratory gait speed among healthy community-dwelling older adults. Sci Rep. 2019;9(1):3496. An investigation detailing the disassociation between lab-based measures of gait speed and free-living gait speed in older adults. The observations highlight the need for both lab-based and free-living assessments of gait in older adults and suggest this multidimensional approach for other clinical populations.

    PubMed  PubMed Central  Google Scholar 

  37. • Van Ancum JM, et al. Gait speed assessed by a 4-m walk test is not representative of daily-life gait speed in community-dwelling adults. Maturitas. 2019;121:28–34. An investigation highlighting that clinical gait assessments do not directly map to free-living gait speed in community-dwelling older adults. The observations highlight the need for both lab-based and free-living assessments of gait in clinical populations.

    PubMed  Google Scholar 

  38. Feys P, Gijbels D, Romberg A, Santoyo C, Gebara B, de Noordhout BM, et al. Effect of time of day on walking capacity and self-reported fatigue in persons with multiple sclerosis: a multi-center trial. Mult Scler. 2012;18(3):351–7.

    CAS  PubMed  Google Scholar 

  39. Powell DJH, Liossi C, Schlotz W, Moss-Morris R. Tracking daily fatigue fluctuations in multiple sclerosis: ecological momentary assessment provides unique insights. J Behav Med. 2017;40(5):772–83.

    PubMed  PubMed Central  Google Scholar 

  40. Dickinson MH, et al. How animals move: an integrative view. Science. 2000;288(5463):100–6.

    CAS  PubMed  Google Scholar 

  41. Pau M, Corona F, Pilloni G, Porta M, Coghe G, Cocco E. Texting while walking differently alters gait patterns in people with multiple sclerosis and healthy individuals. Mult Scler Relat Disord. 2018;19:129–33.

    PubMed  Google Scholar 

  42. Hillel I, Gazit E, Nieuwboer A, Avanzino L, Rochester L, Cereatti A, et al. Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring. Eur Rev Aging Phys Act. 2019;16:6.

    PubMed  PubMed Central  Google Scholar 

  43. Motl RW, Sandroff BM, Sosnoff JJ. Commercially available accelerometry as an ecologically valid measure of ambulation in individuals with multiple sclerosis. Expert Rev Neurother. 2012;12(9):1079–88.

    CAS  PubMed  Google Scholar 

  44. Sasaki JE, Sandroff B, Bamman M, Motl RW. Motion sensors in multiple sclerosis: narrative review and update of applications. Expert Rev Med Dev. 2017;14(11):891–900.

    CAS  Google Scholar 

  45. Uszynski MK, Herring MP, Casey B, Hayes S, Gallagher S, Motl RW, et al. Sources of variability in physical activity among inactive people with multiple sclerosis. Int J Behav Med. 2018;25(2):259–64.

    PubMed  Google Scholar 

  46. • Supratak A, et al. Remote monitoring in the home validates clinical gait measures for multiple sclerosis. Front Neurol. 2018;9:561. This article demonstrates the ability to accurately measure gait speed in the home environment, providing the ability to track impairment in daily life. A strong association between the T25W gait speed and the max sustained gait speed at home was also observed. However, these results were achieved with subject-specific models, limiting their ecological value.

    PubMed  PubMed Central  Google Scholar 

  47. Shammas L, Zentek T, von Haaren B, Schlesinger S, Hey S, Rashid A. Home-based system for physical activity monitoring in patients with multiple sclerosis (pilot study). Biomed Eng Online. 2014;13:10.

    PubMed  PubMed Central  Google Scholar 

  48. Sosnoff JJ, Socie MJ, Boes MK, Sandroff BM, Motl RW. Does a waist-worn ActiGraph accelerometer quantify community ambulation in persons with multiple sclerosis? J Rehabil Res Dev. 2012;49(9):1405–10.

    PubMed  Google Scholar 

  49. Motl RW, Weikert M, Suh Y, Sosnoff JJ, Pula J, Soaz C, et al. Accuracy of the actibelt(®) accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis. Gait Posture. 2012;35(2):192–6.

    PubMed  Google Scholar 

  50. Dugan EL, Combs-Miller SA. Physiological complexity of gait is decreased in individuals with chronic stroke. Comput Methods Biomech Biomed Engin. 2019;22(6):658–63.

    PubMed  Google Scholar 

  51. Del Din S, et al. Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length. J Neuroeng Rehabil. 2016;13(1):46.

    PubMed  PubMed Central  Google Scholar 

  52. Mitchell T, Conradsson D, Paquette C. Gait and trunk kinematics during prolonged turning in Parkinson’s disease with freezing of gait. Parkinsonism Relat Disord. 2019.

  53. Nero H, Benka Wallén M, Franzén E, Ståhle A, Hagströmer M. Accelerometer cut points for physical activity assessment of older adults with Parkinson’s disease. PLoS One. 2015;10(9):e0135899.

    PubMed  PubMed Central  Google Scholar 

  54. Raccagni C, Gaßner H, Eschlboeck S, Boesch S, Krismer F, Seppi K, et al. Sensor-based gait analysis in atypical parkinsonian disorders. Brain Behav. 2018;8(6):e00977.

    PubMed  PubMed Central  Google Scholar 

  55. Moore SA, Hickey A, Lord S, del Din S, Godfrey A, Rochester L. Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study. J Neuroeng Rehabil. 2017;14(1):130.

    PubMed  PubMed Central  Google Scholar 

  56. Yang S, Zhang JT, Novak AC, Brouwer B, Li Q. Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors. Gait Posture. 2013;37(3):354–8.

    PubMed  Google Scholar 

  57. Meijer R, Plotnik M, Zwaaftink E, van Lummel RC, Ainsworth E, Martina JD, et al. Markedly impaired bilateral coordination of gait in post-stroke patients: is this deficit distinct from asymmetry? A cohort study. J Neuroeng Rehabil. 2011;8:23.

    PubMed  PubMed Central  Google Scholar 

  58. Taylor-Piliae RE, Mohler MJ, Najafi B, Coull BM. Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study. Top Stroke Rehabil. 2016;23(6):393–9.

    PubMed  Google Scholar 

  59. Sanchez MC, et al. Accelerometric assessment of different dimensions of natural walking during the first year after stroke: recovery of amount, distribution, quality and speed of walking. J Rehabil Med. 2015;47(8):714–21.

    PubMed  Google Scholar 

  60. • Pau M, et al. Clinical assessment of gait in individuals with multiple sclerosis using wearable inertial sensors: Comparison with patient-based measure. Mult Scler Relat Disord. 2016;10:187–91. This article highlights the potential of wearable accelerometers to quantify pace and rhythm during clinical assessment in PwMS with mild to moderate disability. This eobservation further indicates the clinical utility of sensor based gait assessment.

    PubMed  Google Scholar 

  61. Pau M, Mandaresu S, Pilloni G, Porta M, Coghe G, Marrosu MG, et al. Smoothness of gait detects early alterations of walking in persons with multiple sclerosis without disability. Gait Posture. 2017;58:307–9.

    PubMed  Google Scholar 

  62. Storm FA, Nair KPS, Clarke AJ, van der Meulen JM, Mazzà C. Free-living and laboratory gait characteristics in patients with multiple sclerosis. PLoS One. 2018;13(5):e0196463.

    PubMed  PubMed Central  Google Scholar 

  63. 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. 2016;20(3):838–47.

    PubMed  Google Scholar 

  64. Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G, et al. A validated smartphone-based assessment of gait and gait variability in Parkinson’s disease. PLoS One. 2015;10(10):e0141694.

    PubMed  PubMed Central  Google Scholar 

  65. Schwenk M, Sabbagh M, Lin I, Morgan P, Grewal GS, Mohler J, et al. Sensor-based balance training with motion feedback in people with mild cognitive impairment. J Rehabil Res Dev. 2016;53(6):945–58.

    PubMed  PubMed Central  Google Scholar 

  66. Ijmker T, Lamoth CJ. Gait and cognition: the relationship between gait stability and variability with executive function in persons with and without dementia. Gait Posture. 2012;35(1):126–30.

    PubMed  Google Scholar 

  67. Brodie MA, Psarakis M, Hoang P. Gyroscopic corrections improve wearable sensor data prior to measuring dynamic sway in the gait of people with multiple sclerosis. Comput Methods Biomech Biomed Engin. 2016;19(12):1339–46.

    PubMed  Google Scholar 

  68. Hickey A, Gunn E, Alcock L, del Din S, Godfrey A, Rochester L, et al. Validity of a wearable accelerometer to quantify gait in spinocerebellar ataxia type 6. Physiol Meas. 2016;37(11):N105–17.

    PubMed  Google Scholar 

  69. Urbanek JK, Harezlak J, Glynn NW, Harris T, Crainiceanu C, Zipunnikov V. Stride variability measures derived from wrist- and hip-worn accelerometers. Gait Posture. 2017;52:217–23.

    PubMed  Google Scholar 

  70. Wang K, Delbaere K, Brodie MAD, Lovell NH, Kark L, Lord SR, et al. Differences between gait on stairs and flat surfaces in relation to fall risk and future falls. IEEE J Biomed Health Inform. 2017;21(6):1479–86.

    PubMed  Google Scholar 

  71. Grimpampi E, Oesen S, Halper B, Hofmann M, Wessner B, Mazzà C. Reliability of gait variability assessment in older individuals during a six-minute walk test. J Biomech. 2015;48(15):4185–9.

    PubMed  Google Scholar 

  72. Howcroft J, Kofman J, Lemaire ED, McIlroy WE. Analysis of dual-task elderly gait in fallers and non-fallers using wearable sensors. J Biomech. 2016;49(7):992–1001.

    PubMed  Google Scholar 

  73. • Psarakis M, et al. Wearable technology reveals gait compensations, unstable walking patterns and fatigue in people with multiple sclerosis. Physiol Meas. 2018;39(7):075004. The first study to develop a method based on accelerometry to objectively quantify asymmetries in limb-specific spatial trajectories during individual gait strides in PwMS and healthy controls.

    PubMed  Google Scholar 

  74. Sant’Anna A, Salarian A, Wickstrom N. A new measure of movement symmetry in early Parkinson’s disease patients using symbolic processing of inertial sensor data. IEEE Trans Biomed Eng. 2011;58(7):2127–35.

    PubMed  Google Scholar 

  75. Prajapati SK, Gage WH, Brooks D, Black SE, McIlroy WE. A novel approach to ambulatory monitoring: investigation into the quantity and control of everyday walking in patients with subacute stroke. Neurorehabil Neural Repair. 2011;25(1):6–14.

    PubMed  Google Scholar 

  76. Lien WC, Cheng YH, Kuan TS, Zheng YL, Hsieh CH, Wang WF. Comparison of gait symmetry between poststroke fallers and nonfallers during level walking using triaxial accelerometry: a STROBE-compliant cross-sectional study. Medicine (Baltimore). 2017;96(9):e5990.

    PubMed Central  Google Scholar 

  77. Oyake K, Yamaguchi T, Sugasawa M, Oda C, Tanabe S, Kondo K, et al. Validity of gait asymmetry estimation by using an accelerometer in individuals with hemiparetic stroke. J Phys Ther Sci. 2017;29(2):307–11.

    PubMed  PubMed Central  Google Scholar 

  78. Wang L, Sun Y, Li Q, Liu T. Estimation of step length and gait asymmetry using wearable inertial sensors. IEEE Sens J. 2018;18(9):3844–51.

    Google Scholar 

  79. Zhang W, et al. Gait symmetry assessment with a low back 3D accelerometer in post-stroke patients. Sensors (Basel). 2018;18(10).

    Google Scholar 

  80. Qiu S, Liu L, Wang Z, Li S, Zhao H, Wang J, et al. Body sensor network-based gait quality assessment for clinical decision-support via multi-sensor fusion. IEEE Access. 2019;7:59884–94.

    Google Scholar 

  81. Craig JJ, Bruetsch AP, Lynch SG, Huisinga JM. Altered visual and somatosensory feedback affects gait stability in persons with multiple sclerosis. Hum Mov Sci. 2019;66:355–62.

    PubMed  Google Scholar 

  82. Craig JJ, et al. The relationship between trunk and foot acceleration variability during walking shows minor changes in persons with multiple sclerosis. Clin Biomech (Bristol, Avon). 2017;49:16–21.

    Google Scholar 

  83. Huisinga JM, Mancini M, St. George RJ, Horak FB. Accelerometry reveals differences in gait variability between patients with multiple sclerosis and healthy controls. Ann Biomed Eng. 2013;41(8):1670–9.

    PubMed  Google Scholar 

  84. •• Peebles AT, et al. Dynamic balance in persons with multiple sclerosis who have a falls history is altered compared to non-fallers and to healthy controls. J Biomech. 2017; This investigation utilized accelerometry-based assessment of gait and quantified various gait quality parameters including variability and complexity. It was observed that fallers with MS walked with less complexity and increased the variability of step length, stride time, and both anterior-posterior and mediolateral margin of stability compared with non-fallers and healthy controls. This provides evidence of the clinical import of these gait quality metrics.

  85. Afsar O, Tirnakli U, Kurths J. Entropy-based complexity measures for gait data of patients with Parkinson’s disease. Chaos. 2016;26(2):023115.

    PubMed  Google Scholar 

  86. Warlop T, Detrembleur C, Stoquart G, Lejeune T, Jeanjean A. Gait complexity and regularity are differently modulated by treadmill walking in Parkinson’s disease and healthy population. Front Physiol. 2018;9:68.

    PubMed  PubMed Central  Google Scholar 

  87. Combs-Miller SA, et al. Physiological complexity of gait between regular and non-exercisers with Parkinson’s disease. Clin Biomech (Bristol, Avon). 2019;68:23–8.

    Google Scholar 

  88. Jehu D, Nantel J. Fallers with Parkinson’s disease exhibit restrictive trunk control during walking. Gait Posture. 2018;65:246–50.

    PubMed  Google Scholar 

  89. Liao F, Wang J, He P. Multi-resolution entropy analysis of gait symmetry in neurological degenerative diseases and amyotrophic lateral sclerosis. Med Eng Phys. 2008;30(3):299–310.

    PubMed  Google Scholar 

  90. Tamburini P, Mazzoli D, Stagni R. Towards an objective assessment of motor function in sub-acute stroke patients: relationship between clinical rating scales and instrumental gait stability indexes. Gait Posture. 2018;59:58–64.

    CAS  PubMed  Google Scholar 

  91. Kasser SL, Goldstein A, Wood PK, Sibold J. Symptom variability, affect and physical activity in ambulatory persons with multiple sclerosis: understanding patterns and time-bound relationships. Disabil Health J. 2017;10(2):207–13.

    PubMed  Google Scholar 

  92. Patel S, et al. A wearable computing platform for developing cloud-based machine learning models for health monitoring applications. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2016.

  93. Gurchiek RD, et al. Remote gait analysis using wearable sensors detects asymmetric gait patterns in patients recovering from ACL reconstruction. in 2019 IEEE International Conference on Body Sensor Networks (BSN); 2019.

  94. Liu Q, Williamson J, Li K, Mohrman W, Lv Q, Dick RP, et al. Gazelle: energy-efficient wearable analysis for running. IEEE Trans Mob Comput. 2017;16(9):2531–44.

    Google Scholar 

  95. Sun R, Moon Y, McGinnis RS, Seagers K, Motl RW, Sheth N, et al. Assessment of postural sway in individuals with multiple sclerosis using a novel wearable inertial sensor. Digital Biomarkers. 2018;2(1):1–10.

    Google Scholar 

  96. Sen-Gupta E, Wright DE, Caccese JW, Wright Jr JA, Jortberg E, Bhatkar V, et al. A pivotal study to validate the performance of a novel wearable sensor and system for biometric monitoring in clinical and remote environments. Digital Biomarkers. 2019;3(1):1–13.

    Google Scholar 

  97. McGinnis RS, et al. Skin mounted accelerometer system for measuring knee range of motion. in 2016 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2016.

  98. Fasel B, Duc C, Dadashi F, Bardyn F, Savary M, Farine PA, et al. A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking. Med Biol Eng Comput. 2017;55(10):1773–85.

    PubMed  Google Scholar 

  99. Soltani A, Dejnabadi H, Savary M, Aminian K. Real-world gait speed estimation using wrist sensor: a personalized approach. IEEE J Biomed Health Inform. 2019:1–1.

  100. M-Sense Research Group. GitHub. https://github.com/search?l=Text&q=M-Sense+Research+Group&type=Code.

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Authors and Affiliations

  1. Motor Control Research Lab, Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, 301 Freer Hall 906 S Goodwin Ave, Urbana, IL, 61801, USA

    Mikaela L. Frechette & Jacob J. Sosnoff

  2. M-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, 05405, USA

    Brett M. Meyer, Lindsey J. Tulipani, Reed D. Gurchiek & Ryan S. McGinnis

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  1. Mikaela L. Frechette
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  2. Brett M. Meyer
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Correspondence to Jacob J. Sosnoff.

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Conflict of Interest

Mikaela L. Frechette, Brett Meyer, Lindsey Tulipani, and Reed D. Gurchiek each declare no potential conflicts of interest.

Jacob J. Sosnoff reports personal fees from Abbvie, Inc. and grants from Permobile, Inc., National Multiple Sclerosis Society, National Institute of Health, National Institute on Disability, Independent Living, and Rehabilitation Research, outside the submitted work.

Ryan S. McGinnis reports other from Impellia, Inc. (Scientific advisor, consultant) and other from MC10, Inc. (Own stock, consultant), outside the submitted work; in addition, Dr. McGinnis has a patent METHODS AND APPARATUS FOR PROVIDING PERSONALIZED BIOFEEDBACK FOR THE TREATMENT OF PANIC ATTACKS pending (Claims and description include language about providing personalized biofeedback for preventing falls), a patent Method and System for Neuromodulation and Stimulation pending, a patent Method and System for Crowd-Sourced Algorithm Development pending, a patent Automated detection and configuration of wearable devices based on on-body status, location, and/or orientation pending, a patent Athlete Speed Prediction Method Using Data from Attached Inertial Measurement Unit issued, and a patent Apparatus and Methods for Employing Miniature IMU’s for Deducing Forces and Moments on Bodies pending.

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Frechette, M.L., Meyer, B.M., Tulipani, L.J. et al. Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis. Curr Neurol Neurosci Rep 19, 80 (2019). https://doi.org/10.1007/s11910-019-0997-9

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  • DOI: https://doi.org/10.1007/s11910-019-0997-9

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