Role of Body-Worn movement monitor technology for balance and Gait Rehabilitation

Physical Therapy

Volumn 95, Issue 3, 2015, Pages 461-470

  Horak, Fay ^a,b,c   King, Laurie ^a   Mancini, Martina ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^b PORTLAND VA MEDICAL CENTER   (United States)

^c APDM Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMBULATORY MONITORING; BODY EQUILIBRIUM; DEVICES; GAIT DISORDERS, NEUROLOGIC; HUMAN; PATHOPHYSIOLOGY; PHYSIOLOGY; PHYSIOTHERAPY; SENSATION DISORDERS; WALKING;

GAIT DISORDERS, NEUROLOGIC; HUMANS; MONITORING, AMBULATORY; PHYSICAL THERAPY MODALITIES; POSTURAL BALANCE; SENSATION DISORDERS; WALKING;

EID: 84924250528     PISSN: 00319023     EISSN: 15386724     Source Type: Journal    
DOI: 10.2522/ptj.20140253     Document Type: Article

References (67)

1. Horak FB, Wrisley DM, Frank J. The Balance Evaluation Systems Test (BESTest) to differentiate balance deficits. Phys Ther. 2009;89:484-498.
2. Panel on Prevention of Falls in Older Persons, American Geriatrics Society/British Geriatrics Society. Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. 2011;59:148-157.
3. Moncada LV. Management of falls in older persons: a prescription for prevention. Am Fam Physician. 2011;84:1267-1276.
4. Ganz DA, Bao Y, Shekelle PG, Rubenstein LZ. Will my patient fall? JAMA. 2007;297: 77-86.
5. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51: 314-322.
6. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol A Biol Sci Med Sci. 2009;64:896-901.
7. Godi M, Franchignoni F, Caligari M, et al. Comparison of reliability, validity, and responsiveness of the mini-BESTest and Berg Balance Scale in patients with balance disorders. Phys Ther. 2013;93:158-167.
8. Hobert MA, Maetzler W, Aminian K, Chiari L. Technical and clinical view on ambulatory assessment in Parkinson’s disease. Acta Neurol Scand. 2014;130:139-147.
9. Mortaza N, Abu Osman NA, Mehdikhani M. Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly? Euro J Phys Rehabil Med. 2014;50:677-691.
10. Cavanaugh JT, Ellis TD, Earhart GM, et al. Capturing ambulatory activity decline in Parkinson’s disease. J Neurol Phys Ther. 2012;36:51-57.
11. Rochester L, Chastin SF, Lord S, et al. Understanding the impact of deep brain stimulation on ambulatory activity in advanced Parkinson’s disease. J Neurol. 2012;259:1081-1086.
12. Taraldsen K, Chastin SF, Riphagen II, et al. Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas. 2012;71:13-19.
13. Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag. 2010;29:25-36.
14. de Bruin ED, Hartmann A, Uebelhart D, et al. Wearable systems for monitoring mobility-related activities in older people: a systematic review. Clin Rehabil. 2008; 22:878-895.
15. Mariani B, Hoskovec C, Rochat S, et al. 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J Biomech. 2010;43:2999-3006.
16. Cutti AG, Ferrari A, Garofalo P, et al. “Outwalk”: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput. 2010;48:17-25.
17. Favre J, Aissaoui R, Jolles BM, et al. Functional calibration procedure for 3D knee joint angle description using inertial sensors. J Biomech. 2009;42:2330-2335.
18. Ferrari A, Cutti AG, Garofalo P, et al. First in vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput. 2010;48:1-15.
19. Mariani B, Jime´nez MC, Vingerhoets FJ, Aminian K. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson’s disease. IEEE Trans Biomed Eng. 2013;60:155-158.
20. Mariani B, Rouhani H, Crevoisier X, Aminian K. Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait Posture. 2013;37: 229-234.
21. Greene BR, Kenny RA. Assessment of cognitive decline through quantitative analysis of the timed up and go test. IEEE Trans Biomed Eng. 2012;59:988-995.
22. Greene BR, O’Donovan A, Romero-Ortuno R, et al. Quantitative falls risk assessment using the timed up and go test. IEEE Trans Biomed Eng. 2010;57:2918-2926.
23. Herman T, Weiss A, Brozgol M, et al. Identifying axial and cognitive correlates in patients with Parkinson’s disease motor subtype using the instrumented Timed Up and Go. Exp Brain Res. 2014;232:713-721.
24. Mellone S, Tacconi C, Chiari L. Validity of a Smartphone-based instrumented Timed Up and Go. Gait Posture. 2012;36:163-165.
25. Mirelman A, Weiss A, Buchman AS, et al. 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. 2014;62:673-678.
26. Palmerini L, Mellone S, Avanzolini G, et al. Quantification of motor impairment in Parkinson’s disease using an instrumented timed up and go test. IEEE Trans Neural Syst Rehabil Eng. 2013;21:664-673.
27. Salarian A, Horak FB, Zampieri C, et al. iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng. 2010;18:303-310.
28. Weiss A, Herman T, Plotnik M, et al. An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers. Physiol Meas. 2011;32:2003-2018.
29. Weiss A, Herman T, Plotnik M, et al. Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson’s disease? Med Eng Phys. 2010;32:119-125.
30. Weiss A, Mirelman A, Buchman AS, et al. Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go. PloS One. 2013;8: e68885.
31. Whitney SL, Roche JL, Marchetti GF, et al. A comparison of accelerometry and center of pressure measures during computerized dynamic posturography: a measure of balance. Gait Posture. 2011;33:594-599.
32. Spain RI, St George RJ, Salarian A, et al. Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait Posture. 2012;35:573-578.
33. Mancini M, King L, Salarian A, et al. Mobility lab to assess balance and gait with synchronized body-worn sensors. J Bioeng Biomed Sci. 2011;(suppl 1):007.
34. King LA, Salarian A, Mancini M, et al. Exploring outcome measures for exercise intervention in people with Parkinson’s disease. Parkinsons Dis. 2013;2013: 572134.
35. Spain RI, Mancini M, Horak FB, Bourdette D. Body-worn sensors capture variability, but not decline, of gait and balance measures in multiple sclerosis over 18 months. Gait Posture. 2014;39:958-964.
36. Mancini M, Carlson-Kuhta P, Zampieri C, et al. Postural sway as a marker of progression in Parkinson’s disease: a pilot longitudinal study. Gait Posture. 2012;36:471-476.
37. Brach JS, Berlin JE, VanSwearingen JM, et al. 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. 2005; 2:21.
38. Hausdorff JM, Edelberg HK, Mitchell SL, et al. Increased gait unsteadiness in community-dwelling elderly fallers. Arch Phys Med Rehabil. 1997;78:278-283.
39. Lee LW, Kerrigan DC. Identification of kinetic differences between fallers and nonfallers in the elderly. Arch Phys Med Rehabil. 1999;78:242-246.
40. Mbourou GA, Lajoie Y, Teasdale N. Step length variability at gait initiation in elderly fallers and non-fallers, and young adults. Gerontology. 2003;49:21-26.
41. Newstead AH, Walden JG, Gitter AJ. Gait variables differentiating fallers from nonfallers. J Geriatr Phys Ther. 2007;30:93-101.
42. Bigelow KE, Berme N. Development of a protocol for improving the clinical utility of posturography as a fall-risk screening tool. J Gerontol A Biol Sci Med Sci. 2011; 66:228-233.
43. Merlo A, Zemp D, Zanda E, et al. Postural stability and history of falls in cognitively able older adults: the Canton Ticino study. Gait Posture. 2012;36:662-666.
44. Zampieri C, Salarian A, Carlson-Kuhta P, et al. The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2010;81:171-176.
45. King LA, Horak FB. Delaying mobility disability in people with Parkinson disease using a sensorimotor agility exercise program. Phys Ther. 2009;89:384-393.
46. Ciavarella AR. Complicated billing requirements challenge physical therapy industry, creating inefficiencies and confusion. J Health Care Finance. 2012;39:51-78.
47. King LA, Horak FB, Mancini M, et al. Instrumenting the balance error scoring system for use with patients reporting persistent balance problems after mild traumatic brain injury. Arch Phys Med Rehabil. 2014;95:353-359.
48. King LA, Mancini M, Priest K, et al. Do clinical scales of balance reflect turning abnormalities in people with Parkinson’s disease? J Neurol Phys Ther. 2012;36:25-31.
49. King LA, Priest KC, Salarian A, et al. Comparing the Mini-BESTest with the Berg Balance Scale to evaluate balance disorders in Parkinson’s disease. Parkinsons Dis. 2012;2012:375419.
50. Chiu SL, Osternig L, Chou LS. Concussion induces gait inter-joint coordination variability under conditions of divided attention and obstacle crossing. Gait Posture. 2013;38:717-722.
51. Tate JJ, Milner CE. Real-time kinematic, temporospatial, and kinetic biofeedback during gait retraining in patients: a systematic review. Phys Ther. 2010;90:1123-1134.
52. Dozza M, Chiari L, Horak FB. Audiobiofeedback improves balance in patients with bilateral vestibular loss. Arch Phys Med Rehabil. 2005;86:1401-1403.
53. Dozza M, Horak FB, Chiari L. Auditory biofeedback substitutes for loss of sensory information in maintaining stance. Exp Brain Res. 2007;178:37-48.
54. Dozza M, Wall C III, Peterka RJ, et al. Effects of practicing tandem gait with and without vibrotactile biofeedback in subjects with unilateral vestibular loss. J Vestib Res. 2007;17:195-204.
55. Bloem BR, Grimbergen YA, Cramer M, et al. Prospective assessment of falls in Parkinson’s disease. J Neurol. 2001;248:950-958.
56. Weiss A, Brozgol M, Dorfman M, et al. 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. 2013;27:742-752.
57. Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PloS One. 2014;9:e96675.
58. El-Gohary M, Pearson S, McNames J, et al. Continuous monitoring of turning in patients with movement disability. Sensors (Basel). 2013;14:356-369.
59. Riemann BL, Guskiewicz KM. Effects of mild head injury on postural stability as measured through clinical balance testing. J Athl Train. 2000;35:19-25.
60. Mulligan IJ, Boland MA, McIlhenny CV. The balance error scoring system learned response among young adults. Sports Health. 2013;5:22-26.
61. Buatois S, Gueguen R, Gauchard GC, et al. Posturography and risk of recurrent falls in healthy non-institutionalized persons aged over65. Gerontology. 2006;52:345-352.
62. Sparto PJ, Jennings JR, Furman JM, Redfern MS. Lateral step initiation behavior in older adults. Gait Posture. 2014;39:799-803.
63. Uemura K, Yamada M, Nagai K, Ichihashi N. Older adults at high risk of falling need more time for anticipatory postural adjustment in the precrossing phase of obstacle negotiation. J Gerontol A Biol Sci Med Sci. 2011;66:904-909.
64. Galan-Mercant A, Cuesta-Vargas AI. Differences in trunk accelerometry between frail and non-frail elderly persons in functional tasks. BMC Res Notes. 2014;7:100.
65. Zakaria NA, Kuwae Y, Tamura T, et al. Quantitative analysis of fall risk using TUG test. Comput Methods Biomech Biomed Engin. 2015;18:426-437.
66. Doi T, Hirata S, Ono R, et al. The harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study. J Neuroeng Rehabil. 2013;10:7.
67. Riva F, Toebes MJ, Pijnappels M, et al. Estimating fall risk with inertial sensors using gait stability measures that do not require step detection. Gait Posture. 2013;38: 170-174.