Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review

Dax Steins, Helen Dawes, Patrick Esser, Johnny Collett, Dax Steins, Helen Dawes, Patrick Esser, Johnny Collett

Abstract

Background: Integrating rehabilitation services through wearable systems has the potential to accurately assess the type, intensity, duration, and quality of movement necessary for procuring key outcome measures.

Objectives: This review aims to explore wearable accelerometry-based technology (ABT) capable of assessing mobility-related functional activities intended for rehabilitation purposes in community settings for neurological populations. In this review, we focus on the accuracy of ABT-based methods, types of outcome measures, and the implementation of ABT in non-clinical settings for rehabilitation purposes.

Data sources: Cochrane, PubMed, Web of Knowledge, EMBASE, and IEEE Xplore. The search strategy covered three main areas, namely wearable technology, rehabilitation, and setting.

Study selection: Potentially relevant studies were categorized as systems either evaluating methods or outcome parameters.

Methods: Methodological qualities of studies were assessed by two customized checklists, depending on their categorization and rated independently by three blinded reviewers.

Results: Twelve studies involving ABT met the eligibility criteria, of which three studies were identified as having implemented ABT for rehabilitation purposes in non-clinical settings. From the twelve studies, seven studies achieved high methodological quality scores. These studies were not only capable of assessing the type, quantity, and quality measures of functional activities, but could also distinguish healthy from non-healthy subjects and/or address disease severity levels.

Conclusion: While many studies support ABT's potential for telerehabilitation, few actually utilized it to assess mobility-related functional activities outside laboratory settings. To generate more appropriate outcome measures, there is a clear need to translate research findings and novel methods into practice.

Figures

Figure 1
Figure 1
Procedure for the study selection and organization.
Figure 2
Figure 2
Flowchart of the results from the literature search.
Figure 3
Figure 3
Piechart of the screening results from the literature search. Studies are divided in: (A) motion-sensing technology to assess functional activities in neurological or non neurological conditions; (B) type of neurological conditions; (C) technology intended for rehabilitation purposes; and (D) type of technology.

References

    1. Collins PY, Patel V, Joestl SS, March D, Insel TR, Daar AS, Anderson W, Dhansay MA, Phillips A, Shurin S, Walport M, Ewart W, Savill SJ, Bordin IA, Costello EJ, Durkin M, Fairburn C, Glass RI, Hall W, Huang Y, Hyman SE, Jamison K, Kaaya S, Kapur S, Kleinman A, Ogunniyi A, Otero-Ojeda A, Poo MM, Ravindranath V, Sahakian BJ. et al.Grand challenges in global mental health. Nature. 2011;475(7354):27–30. doi: 10.1038/475027a.
    1. De Groot MH, Phillips SJ, Eskes GA. Fatigue associated with stroke and other neurologic conditions: implications for stroke rehabilitation. Arch Phys Med Rehabil. 2003;84(11):1714–1720. doi: 10.1053/S0003-9993(03)00346-0.
    1. Stolze H, Klebe S, Zechlin C, Baecker C, Friege L, Deuschl G. Falls in frequent neurological diseases–prevalence, risk factors and aetiology. J Neurol. 2004;251(1):79–84. doi: 10.1007/s00415-004-0276-8.
    1. Alexander NB, Goldberg A. Gait disorders: search for multiple causes. Cleve Clin J Med. 2005;72(7):586. doi: 10.3949/ccjm.72.7.586. 589-590, 592-584 passim.
    1. MacDonald BK, Cockerell OC, Sander JW, Shorvon SD. The incidence and lifetime prevalence of neurological disorders in a prospective community-based study in the UK. Brain. 2000;123(Pt 4):665–676.
    1. WHO. Neurological disorders: a public health approach. In: World Health Organization, editor. Neurological disorders: public health challenges, Volume 3. Geneva, Switzerland: WHO press; 2006.
    1. Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jonsson B, Olesen J, Allgulander C, Alonso J, Faravelli C. et al.The size and burden of mental disorders and other disorders of the brain in Europe. Eur Neuropsychopharmacol. 2011;21(9):655–679. doi: 10.1016/j.euroneuro.2011.07.018.
    1. Krahn GL. WHO World Report on Disability: a review. Disabil Health J. 2011;4(3):141–142. doi: 10.1016/j.dhjo.2011.05.001.
    1. Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair. 2011;25(9):788–798. doi: 10.1177/1545968311425908.
    1. Berlin JE, Storti KL, Brach JS. Using activity monitors to measure physical activity in free-living conditions. Phys Ther. 2006;86(8):1137–1145.
    1. Bussmann JB, Stam HJ. Techniques for measurement and assessment of mobility in rehabilitation: a theoretical approach. Clin Rehabil. 1998;12(6):455–464. doi: 10.1191/026921598674267844.
    1. Kairy D, Lehoux P, Vincent C, Visintin M. A systematic review of clinical outcomes, clinical process, healthcare utilization and costs associated with telerehabilitation. Disabil Rehabil. 2009;31(6):427–447. doi: 10.1080/09638280802062553.
    1. Johansson T, Wild C. Telerehabilitation in stroke care - a systematic review. J Telemed Telecare. 2011;17(1):1–6. doi: 10.1258/jtt.2010.100105.
    1. Gregory P, Alexander J, Satinsky J. Clinical Telerehabilitation: applications for physiatrists. PM&R. 2011;3(7):647–656.
    1. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil. 2012;9:21. doi: 10.1186/1743-0003-9-21.
    1. Steele BG, Belza B, Cain K, Warms C, Coppersmith J, Howard J. Bodies in motion: monitoring daily activity and exercise with motion sensors in people with chronic pulmonary disease. J Rehabil Res Dev. 2003;40(5 Suppl 2):45–58.
    1. Culhane KM, O'Connor M, Lyons D, Lyons GM. Accelerometers in rehabilitation medicine for older adults. Age Ageing. 2005;34(6):556–560. doi: 10.1093/ageing/afi192.
    1. Zheng H, Black ND, Harris ND. Position-sensing technologies for movement analysis in stroke rehabilitation. Med Biol Eng Comput. 2005;43(4):413–420. doi: 10.1007/BF02344720.
    1. de Bruin ED, Hartmann A, Uebelhart D, Murer K, Zijlstra W. Wearable systems for monitoring mobility-related activities in older people: a systematic review. Clin Rehabil. 2008;22(10–11):878–895.
    1. Allet L, Knols RH, Shirato K, de Bruin ED. Wearable systems for monitoring mobility-related activities in chronic disease: a systematic review. Sensors. 2010;10(10):9026–9052. doi: 10.3390/s101009026.
    1. Gebruers N, Vanroy C, Truijen S, Engelborghs S, De Deyn PP. Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Arch Phys Med Rehabil. 2010;91(2):288–297. doi: 10.1016/j.apmr.2009.10.025.
    1. Cheung VH, Gray L, Karunanithi M. Review of accelerometry for determining daily activity among elderly patients. Arch Phys Med Rehabil. 2011;92(6):998–1014. doi: 10.1016/j.apmr.2010.12.040.
    1. Yang CC, Hsu YL. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors. 2010;10(8):7772–7788. doi: 10.3390/s100807772.
    1. Cuesta-Vargas AI, Galán-Mercant A, Williams JM. The use of inertial sensors system for human motion analysis. Phys Ther Rev. 2010;15(6):462–473. doi: 10.1179/1743288X11Y.0000000006.
    1. Wandersman A, Duffy J, Flaspohler P, Noonan R, Lubell K, Stillman L, Blachman M, Dunville R, Saul J. Bridging the gap between prevention research and practice: the interactive systems framework for dissemination and implementation. Am J Community Psychol. 2008;41(3–4):171–181.
    1. Giampaoli S, Van Oyen H, Devillé W, Verschuren M. European Commission on Public Health. Printed in Luxembourg by services of the European Commission; 2007. Major and Chronic Diseases - Report 2007; p. 305.
    1. World Health Organization, editor. WHO. International Classification of Functioning, Disability and Health: ICF Short Version. Geneva, Switzerland: WHO press; 2001. p. 228.
    1. PEDro Scale.
    1. CONSORT Statement.
    1. The Trend Statement. .
    1. Barth J, Klucken J, Kugler P, Kammerer T, Steidl R, Winkler J, Hornegger J, Eskofier B. Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease. Proceeding of the 33rd Annual Conf IEEE Eng Med Biol Soc 2011. 2011. pp. 868–871.
    1. Cancela J, Pansera M, Arredondo MT, Estrada JJ, Pastorino M, Pastor-Sanz L, Villalar JL. A comprehensive motor symptom monitoring and management system: the bradykinesia case. Proceeding of the 32nd Annual Conf IEEE Eng Med Biol Soc 2010. 2010. pp. 1008–1011.
    1. Dobkin BH, Xu X, Batalin M, Thomas S, Kaiser W. Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke. Stroke. 2011;42(8):2246–2250. doi: 10.1161/STROKEAHA.110.611095.
    1. Lau H-Y, Tong K-Y, Zhu H. Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Hum Mov Sci. 2009;28(4):504–514. doi: 10.1016/j.humov.2008.12.003.
    1. Mera TO, Heldman DA, Espay AJ, Payne M, Giuffrida JP. Feasibility of home-based automated Parkinson's disease motor assessment. J Neurosci Methods. 2012;203(1):152–156. doi: 10.1016/j.jneumeth.2011.09.019.
    1. Mitoma H, Yoneyama M, Orimo S. 24-Hour Recording of Parkinsonian Gait Using a Portable Gait Rhythmogram. Intern Med. 2010;49(22):2401–2408. doi: 10.2169/internalmedicine.49.3511.
    1. Mizuike C, Ohgi S, Morita S. Analysis of stroke patient walking dynamics using a tri-axial accelerometer. Gait & Posture. 2009;30(1):60–64. doi: 10.1016/j.gaitpost.2009.02.017.
    1. Moore ST, MacDougall HG, Gracies JM, Cohen HS, Ondo WG. Long-term monitoring of gait in Parkinson's disease. Gait & Posture. 2007;26(2):200–207. doi: 10.1016/j.gaitpost.2006.09.011.
    1. Motoi K, Higashi Y, Kuwae Y, Yuji T, Tanaka S, Yamakoshi K. Development of a wearable device capable of monitoring human activity for use in rehabilitation and certification of eligibility for long-term care. Proceeding of the 27th Annual Conf IEEE Eng Med Biol Soc 2005, Volmue 1. 2005. pp. 1004–1007.
    1. 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. doi: 10.1177/1545968310374189.
    1. Sabelman E, Fiene A, Timbie A. Accelerometric activity identification for remote assessment of quality of movement. Proceeding of the 26th Annual International Conf IEEE Eng Biol Soc 2004, Volume 26. 2004. pp. 4781–4784.
    1. Salarian A, Russmann H, Vingerhoets FJG, Burkhard PR, Aminian K. Ambulatory monitoring of physical activities in patients with Parkinson's disease. IEEE Trans Biomed Eng. 2007;54(12):2296–2299.
    1. Yang C-C, Hsu Y-L, Shih K-S, Lu J-M. Real-time gait cycle parameter recognition using a wearable accelerometry system. Sensors. 2011;11(8):7314–7326.
    1. Zampieri C, Salarian A, Carlson-Kuhta P, Nutt JG, Horak FB. Assessing mobility at home in people with early Parkinson's disease using an instrumented Timed Up and Go test. Parkinsonism Relat Disord. 2011;17(4):277–280. doi: 10.1016/j.parkreldis.2010.08.001.
    1. Zwartjes DGM, Heida T, van Vugt JPP, Geelen JAG, Veltink PH. Ambulatory Monitoring of Activities and Motor Symptoms in Parkinson's Disease. IEEE Trans Biomed Eng. 2010;57(11):2778–2786.
    1. Higashi Y, Sekimoto M, Horiuchi F, Kodama T, Yuji T, Fujimoto T, Sekine M, Tamura T. Monitoring rehabilitation training for hemiplegic patients by using a tri-axial accelerometer. Proceeding of the 23rd Annual International Conf IEEE Eng Med Biol Soc 2001, Volume 2. 2001. pp. 1472–1474.
    1. Shannon CE. The mathematical theory of communication. MD computing computers in medical practice. 1963;27:306–317. 1948.
    1. Najafi B, Aminian K, Loew F, Blanc Y, Robert PA. Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans Biomed Eng. 2002;49(8):843–851. doi: 10.1109/TBME.2002.800763.
    1. Najafi B, Aminian K, Paraschiv-Ionescu A, Loew F, Bula CJ, Robert P. Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans Biomed Eng. 2003;50(6):711–723. doi: 10.1109/TBME.2003.812189.
    1. Salarian A, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Aminian K. iTUG, a Sensitive and Reliable Measure of Mobility. IEEE Trans Neural Syst Rehab Eng. 2010;18(3):303–310.
    1. Salarian A, Zampieri C, Horak FB, Carlson-Kuhta P, Nutt JG, Aminian K. Analyzing 180 degrees Turns Using an Inertial System Reveals Early Signs of Progression of Parkinson's Disease. Proceeding of the 31st Annual Conf IEEE Eng Med Biol Soc 2009. 2009. pp. 224–227.
    1. Miyazaki S. Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope. IEEE Trans Biomed Eng. 1997;44(8):753–759. doi: 10.1109/10.605434.
    1. Aminian K, Najafi B, Bula C, Leyvraz PF, Robert P. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J Biomech. 2002;35(5):689–699. doi: 10.1016/S0021-9290(02)00008-8.
    1. Moore ST, MacDougall HG, Ondo WG. Ambulatory monitoring of freezing of gait in Parkinson's disease. J Neurosci Methods. 2008;167(2):340–348. doi: 10.1016/j.jneumeth.2007.08.023.
    1. Patterson SL, Forrester LW, Rodgers MM, Ryan AS, Ivey FM, Sorkin JD, Macko RF. Determinants of walking function after stroke: differences by deficit severity. Arch Phys Med Rehabil. 2007;88(1):115–119. doi: 10.1016/j.apmr.2006.10.025.
    1. Berg Balance Scale. .
    1. Zampieri C, Salarian A, Carlson-Kuhta P, Aminian K, Nutt JG, Horak FB. The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson's disease. J Neurol Neurosurg Psychiatry. 2010;81(2):171–176. doi: 10.1136/jnnp.2009.173740.
    1. Domingos P. A few useful things to know about machine learning. Communications of the ACM. 2012. pp. 78–87. (10).
    1. Theodoridis S, Koutroumbas K. Pattern Recognition. 4 2008.
    1. Zhou H, Hu H. Human motion tracking for rehabilitation—A survey. Biomed Signal Process Control. 2008;3:1–18. doi: 10.1016/j.bspc.2007.09.001.
    1. Zhou H, Hu H, Member S. Reducing Drifts in the Inertial Measurements of Wrist and Elbow Positions. IEEE Trans Instrum Meas. 2010;59:575–585.
    1. Blumrosen G, Luttwak A. Human body parts tracking and kinematic features assessment based on RSSI and inertial sensor measurements. Sensors (Basel) 2013;13(9):11289–11313. doi: 10.3390/s130911289.
    1. Xu X, Batalin MA, Kaiser WJ, Dobkin B. Robust hierarchical system for classification of complex human mobility characteristics in the presence of neurological disorders. Proceeding International Conf on Body Sensor Networks 2011. 2011. pp. 65–77.
    1. Salarian A, Russmann H, Wider C, Burkhard PR, Vingerhoets FJ, Aminian K. Quantification of tremor and bradykinesia in Parkinson's disease using a novel ambulatory monitoring system. IEEE Trans Biomed Eng. 2007;54(2):313–322.
    1. Hoff JI, van den Plas AA, Wagemans EA, van Hilten JJ. Accelerometric assessment of levodopa-induced dyskinesias in Parkinson's disease. Mov Disord. 2001;16(1):58–61. doi: 10.1002/1531-8257(200101)16:1<58::AID-MDS1018>;2-9.
    1. Siderowf A, McDermott M, Kieburtz K, Blindauer K, Plumb S, Shoulson I. Test-retest reliability of the unified Parkinson's disease rating scale in patients with early Parkinson's disease: results from a multicenter clinical trial. Mov Disord. 2002;17(4):758–763. doi: 10.1002/mds.10011.
    1. Weiss A, Sharifi S, Plotnik M, van Vugt JP, Giladi N, Hausdorff JM. Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil Neural Repair. 2011;25(9):810–818. doi: 10.1177/1545968311424869.
    1. Macko RF, Haeuber E, Shaughnessy M, Coleman KL, Boone DA, Smith GV, Silver KH. Microprocessor-based ambulatory activity monitoring in stroke patients. Med Sci Sports Exerc. 2002;34(3):394–399. doi: 10.1097/00005768-200203000-00002.
    1. Bonato P. Advances in wearable technology and applications in physical medicine and rehabilitation. J Neuroeng Rehabil. 2005;2(1):2. doi: 10.1186/1743-0003-2-2.

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