Hostname: page-component-797576ffbb-xmkxb Total loading time: 0 Render date: 2023-12-11T04:07:30.971Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "useRatesEcommerce": true } hasContentIssue false

Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke

Published online by Cambridge University Press:  10 November 2015

Kathryn S. Hayward
Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
Janice J. Eng
Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada Rehabilitation Research Program, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
Lara A. Boyd
Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
Bimal Lakhani
Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
Julie Bernhardt
Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia College of Science Health and Engineering, Latrobe University, Melbourne, Victoria, Australia
Catherine E. Lang*
Program in Physical Therapy, Program in Occupational Therapy, Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA
Address for correspondence: Catherine E. Lang, PhD, Program in Physical Therapy, Washington University School of Medicine in St Louis, 4444 Forest Park, Campus Box 8502, St Louis, MO 63108, USA. E-mail: Phone: +1 (314) 286-1945.


The ultimate goal of upper-limb rehabilitation after stroke is to promote real-world use, that is, use of the paretic upper-limb in everyday activities outside the clinic or laboratory. Although real-world use can be collected through self-report questionnaires, an objective indicator is preferred. Accelerometers are a promising tool. The current paper aims to explore the feasibility of accelerometers to measure upper-limb use after stroke and discuss the translation of this measurement tool into clinical practice. Accelerometers are non-invasive, wearable sensors that measure movement in arbitrary units called activity counts. Research to date indicates that activity counts are a reliable and valid index of upper-limb use. While most accelerometers are unable to distinguish between the type and quality of movements performed, recent advancements have used accelerometry data to produce clinically meaningful information for clinicians, patients, family and care givers. Despite this, widespread uptake in research and clinical environments remains limited. If uptake was enhanced, we could build a deeper understanding of how people with stroke use their arm in real-world environments. In order to facilitate greater uptake, however, there is a need for greater consistency in protocol development, accelerometer application and data interpretation.

Copyright © Australasian Society for the Study of Brain Impairment 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Bailey, R.R., Klaesner, J.W., & Lang, C.E. (2014). An accelerometry-based methodology for assessment of real-world bilateral upper extremity activity. PLOSONE, 9, e103135.Google Scholar
Bailey, R.R., Klaesner, J.W., & Lang, C.E. (2015). Quantifying real-world upper-limb activity in nondisabled adults and adults with chronic stroke. Neurorehabilitation and Neural Repair, 29 969978 Epub ahead of print, DOI pii:10.1177/1545968315583720.Google Scholar
Bailey, R.R., & Lang, C.E. (2014). Upper extremity activity in adults: referent values using accelerometry. Journal of Rehabilitation Research and Development, 50, 12131222.Google Scholar
Bao, L., & Intille, S.S. (2004). Activity recognition from user-annotated acceleration data. In Ferscha, A. & Mattern, F. (Eds.), PERVASIVE 2004, LNCS 3001 (pp. 117). Berlin: Springer-Verlag.Google Scholar
Barak, S., Wu, S.S., Dai, Y., Duncan, P.W., & Behrman, A.L. (2014). Adherence to accelerometry measurement of community ambulation poststroke. Physical Therapy, 94, 101110.Google Scholar
Bernhardt, J., Chan, J., Nicola, I., & Collier, J.M. (2007). Little therapy, little physical activity: rehabilitation within the first 14 days of organized stroke unit care. Journal of Rehabilitation Medicine, 39, 4348.Google Scholar
Billinger, S.A., Arena, R., Bernhardt, J., Eng, J.J., Franklin, B.A., Johnson, C.M., . . . Tang, A. (2014). Physical activity and exercise recommendations for stroke survivors: a statement for healthcare professionals from the American Heart Association/American stroke association. Stroke, 45, 25322553.Google Scholar
Billinger, S.A., Coughenour, E., Mackay-Lyons, M.J., & Ivey, F.M. (2012). Reduced cardiorespiratory fitness after stroke: biological consequences and exercise-induced adaptations. Stroke Research and Treatment, 2012, 959120.Google Scholar
Connell, L.A., McMahon, N.E., Redfern, J., Watkins, C.L., & Eng, J.J. (2015). Development of a behaviour change intervention to increase upper-limb exercise in stroke rehabilitation. Implementation Science, 10, 34.Google Scholar
Connell, L.A., McMahon, N.E., Simpson, L.A., Watkins, C.L., & Eng, J.J. (2014). Investigating measures of intensity during a structured upper-limb exercise program in stroke rehabilitation: an exploratory study. Archives of Physical Medicine & Rehabilitation, 95, 24102419.Google Scholar
de Niet, M., Bussmann, J.B., Ribbers, G.M., & Stam, H.J. (2007). The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Archives of Physical Medicine and Rehabilitation, 88, 11211126.Google Scholar
DeJong, S.L., Birkenmeier, R.L., & Lang, C.E. (2012). Person-specific changes in motor performance accompany upper extremity functional gains after stroke. Journal of Applied Biomechanics, 28, 304316.Google Scholar
Del Din, S., Patel, S., Cobelli, C., & Bonato, P. (2011). Estimating Fugl-Meyer clinical scores in stroke survivors using wearable sensors. Paper presented at the IEEE EMBS, Boston MA.Google Scholar
Dobkin, B.H., & Dorsch, A. (2011). The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabilitation and Neural Repair, 25, 788798.Google Scholar
Dorsch, A.K., Thomas, S., Xu, X., Kaiser, W., Dobkin, B.H., & SIRRACT investigators. (2015). SIRRACT: an international randomized clinical trial of activity feedback during inpatient stroke rehabilitation enabled by wireless sensing. Neurorehabilitation and Neural Repair, 29, 407415.Google Scholar
English, C.E., Coates, A., Olds, T., Healy, G., & Bernhardt, J. (2014). Exploring patterns of inactivity and use-of-time in people after stroke (EPIPS). International Journal of Stroke, 9, S10.Google Scholar
Fini, N.A., Holland, A.E., Keating, J., Simek, J., & Bernhardt, J. (2015). How is physical activity monitored in people following stroke? Disability and Rehabilitation, 37, 17171731Google Scholar
Friedman, N., Rowe, J.B., Reinkensmeyer, D.J., & Bachman, M. (2014). The manumeter: a wearable device for monitoring daily use of the wrist and fingers. IEEE Journal of Biomedical Health Informatics, 18, 18041812.Google Scholar
Gebruers, N., Truijen, S., Engelborghs, S., & De Deyn, P.P. (2014). Prediction of upper-limb recovery, general disability, and rehabilitation status by activity measurements assessed by accelerometers or the Fugl-Meyer score in acute stroke. American Journal of Physical Medicine & Rehabilitation/Association of Academic Physiatrists, 93, 245252.Google Scholar
Gebruers, N., Vanroy, C., Truijen, S., Engelborghs, S., & De Deyn, P.P. (2010). Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Archives of Physical Medicine and Rehabilitation, 91, 288297.Google Scholar
Hale, L.A., Pal, J., & Becker, I. (2008). Measuring free-living physical activity in adults with and without neurologic dysfunction with a triaxial accelerometer. Archives of Physical Medicine and Rehabilitation, 89, 17651771.Google Scholar
Hayward, K.S., Barker, R.N., Wiseman, A.H., & Brauer, S.G. (2013). Dose and content of training provided to stroke survivors with severe upper-limb disability undertaking inpatient rehabilitation: an observational study. Brain Impairment, 14, 392405.Google Scholar
Hayward, K.S., & Brauer, S.G. (2015). Dose of arm activity training during acute and subacute rehabilitation post stroke: a systematic review of the literature. Clinical Rehabilitation, Epub ahead of print, DOI: 10.1177/0269215514565395.Google Scholar
Jain, A.K., Duin, R.P.W., & Mao, J. (2000). Statistical pattern recognition: a review IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 438.Google Scholar
Jones, F., Mandy, A., & Partridge, C. (2009). Changing self-efficacy in individuals following a first time stroke: preliminary study of a novel self-management intervention. Clinical Rehabilitation, 23, 522533.Google Scholar
Jones, F., & Riazi, A. (2009). Self-efficacy and self-management after stroke: a systematic review. Disability & Rehabilitation, 33, 797810.Google Scholar
Kilbreath, S.L., & Heard, R.C. (2005). Frequency of hand use in healthy older persons. The Australian Journal of Physiotherapy, 51, 119122.Google Scholar
Klassen, T.D., Eng, J. J., Chan, C., Hassall, Z., Lim, S., Louie, R., . . . Zbogar, D. (2014). Step count monitor for individuals post-stroke: accuracy of the fitbit one. Stroke, 45, e259e298.Google Scholar
Kokotilo, K.J., Eng, J.J., McKeown, M.J., & Boyd, L.A. (2010). Greater activation of secondary motor areas is related to less arm use after stroke. Neurorehabilitation and Neural Repair, 24, 7887.Google Scholar
Krakauer, J.W., Carmichael, S.T., Corbett, D., & Wittenberg, G.F. (2012). Getting neurorehabilitation right: What can be learned from animal models? Neurorehabilitation and Neural Repair, 26, 923931.Google Scholar
Lakhani, B., Borich, M.R., Jackson, J.N., Wadden, K.P., Peters, S., Villamayor, A., MacKay, A., Vavasour, I., Rauscher, A., Boyd, L.A. Multimodal imaging to assess structural and functional changes associated with motor skill acquisition. Society for Neuroscience Annual Meeting, Chicago, USA, 16–21 October 2015.Google Scholar
Lang, C.E. (2012). Impaired motor control. In Guccione, A., Wong, R. & Avers, D. (Eds.), Geriatric physical therapy (3rd ed.). Elsevier, St Louis Missouri USA, 272291.Google Scholar
Lang, C.E., Bland, M.D., Bailey, R.R., Schaefer, S.Y., & Birkenmeijer, R.L. (2013). Assessment of upper extremity impairment, function and activity after stroke: foundations for clinical decision making. Journal of Hand Therapy, 26, 104115.Google Scholar
Lang, C.E., Wagner, J.M., Edwards, D.F., & Dromerick, A.W. (2007). Upper extremity use in people with hemiparesis in the first few weeks after stroke. Journal of Neurologic Physical Therapy, 31, 5663.Google Scholar
Lemmens, R., Seelen, H., Timmermans, A., Schnackers, M., Eerden, A., Smeets, R., & Janssen-Potten, Y. (2015). To what extent can arm hand skill performance of both healthy adults and children be recorded reliably using multiple body worn sensor devices? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23, 581590. doi: 10.1109/TNSRE.2015.2396082.Google Scholar
Lemmens, R., Timmermans, A.A.A., Janssen-Potten, Y.J.M., Pulles, S.A.N.T.D., Geers, R.P.J., Bakx, W.G.M., . . . Seelen, H.A. (2014). Accelerometry measuring the outcome of robot-supported upper-limb training in chronic stroke: a randomised controlled trial. PLOSONE, 9, e96414.Google Scholar
Levin, M.F., Kleim, J.A., & Wolf, S.L. (2009). What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke? Neurorehabilitation and Neural Repair, 29, 847857, 23, 313–319.Google Scholar
Mannini, A., & Sabatini, A.M. (2011). Accelerometry-based classification of human activities using Markov modeling. Computational Intelligence Neuroscience, 2011, 647858.Google Scholar
Mansfield, A., Wong, J.S., Bryce, J., Brunton, K., Inness, E.L., Knorr, S., . . . McIlroy, W.E. (2015). Use of accelerometer-based feedback of walking activity for appraising progress with walking-related goals in inpatient stroke rehabilitation: a randomized controlled trial. Neurorehabilitation and Neural Repair, 29, 847857. DOI pii: 1545968314567968.Google Scholar
Markopoulos, P., Timmermans, A.A.A., Beursgens, L., van Donselaar, R., & Seelen, H.A. (2011). Us’em: the user-centered design of a device for motivating stroke patients to use their impaired arm-hand in daily life activities. International Conference of the IEEE EMBS, 11, 51825187.Google Scholar
McCombe Waller, S., & Whitall, J. (2008). Bilateral arm training: why and who benefits? NeuroRehabilitation, 23, 2941.Google Scholar
Meijer, R., Plotnik, M., Zwaaftink, E.G., van Lummel, R.C., Ainsworth, E., Martina, J.D., & Hausdorff, J.M. (2011). Markedly impaired bilateral coordination of gait in post-stroke patients: Is this deficit distinct from asymmetry? A cohort study. J Neuroeng Rehabil, 8, 23.Google Scholar
Mudge, S., & Stott, N.S. (2008). Test-retest reliability of the StepWatch activity monitor outputs in individuals with chronic stroke. Clinical Rehabilitation, 22, 871877.Google Scholar
Patterson, K.K., Mansfield, A., Biasin, L., Brunton, K., Inness, E.L., & McIlroy, W.E. (2015). Longitudinal changes in poststroke spatiotemporal gait asymmetry over inpatient rehabilitation. Neurorehabil Neural Repair, 29, 153162.Google Scholar
Paul, D.R., Kramer, M., Moshfegh, A.J., Baer, D.J., & Rumpler, W.V. (2007). Comparison of two different physical activity monitors. BMC Medical Research Methodology, 7, 2632.Google Scholar
Preece, S.J., Goulermas, J.Y., Kenney, L.P., Howard, D., Meijer, K., & Crompton, R. (2009). Activity identification using body-mounted sensors–a review of classification techniques. Physiological Measurement, 30, R1R33.Google Scholar
Rand, D., & Eng, J.J. (2010). Arm-hand use in healthy older adults. The American Journal of Occupational Therapy, 64, 877–855.Google Scholar
Rand, D., & Eng, J.J. (2012). Disparity between functional recovery and daily use of the upper and lower extremities during subacute stroke rehabilitation. Neurorehabilitation and Neural Repair, 26, 7684.Google Scholar
Rand, D., & Eng, J.J. (2015). Predicting daily use of the affected upper extremity 1 year after stroke. Journal of Stroke and Cerebrovascular Diseases, 24, 274283.Google Scholar
Rand, D., Givon, N., Weingarden, H., Nota, A., & Zeilig, G. (2014). Eliciting upper extremity purposeful movements using video games: a comparison with traditional therapy for stroke rehabilitation. Neurorehabil Neural Repair, 28, 733739.Google Scholar
Redmond, D.P., & Hegge, F.W. (1985). Observations on the design and specification of a wrist-worn human activity monitoring system. Behavioural Research Methods, 17, 659669.Google Scholar
Reisman, D.S., Wityk, R., Silver, K., & Bastian, A.J. (2007). Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke. Brain, 130, 18611872.Google Scholar
Reiterer, V., Sauter, C., Klosch, G., Lalouschek, W., & Zeitlhofer, J. (2008). Actigraphy - a useful tool for motor activity monitoring in stroke patients. European Journal of Neurology, 60, 285291.Google Scholar
Roos, M.A., Rudolph, K.S., & Reisman, D.S. (2012). The structure of walking activity in people after stroke compared with older adults without disability: a cross-sectional study. Physical Therapy, 92, 11411147.Google Scholar
Simpson, L.A., Eng, J.J., Backman, C.L., & Miller, W.C. (2013). Rating of everyday arm-use in the community and home (REACH) scale for capturing affected arm-use after stroke: development, reliability, and validity. PLOSONE, 8, e83405.Google Scholar
Stergiou, N., & Decker, L.M. (2011). Human movement variability, nonlinear dynamics, and pathology: Is there a connection? Human Movement Science, 30, 869888.Google Scholar
Straker, L., & Campbell, A. (2012). Translation equations to compare ActiGraph GT3X and Actical accelerometers activity counts. BMC Medical Research Methodology, 12, 5461.Google Scholar
Taub, E., Uswatte, G., Bowman, M.H., Mark, V.W., Delgado, A., Bryson, C., . . . Bishop-McKay, S. (2013). Constraint-induced movement therapy combined with conventional neurorehabilitation techniques in chronic stroke patients with plegic hands: a case series. Archives of Physical Medicine and Rehabilitation, 94, 8694.Google Scholar
Taub, E., Uswatte, G., Mark, V.W., & Morris, D.M. (2006). The learned nonuse phenomenon: implications for rehabilitation. Europa Medicophysica, 42, 241256.Google Scholar
Thrane, G., Emaus, N., Askim, T., & Anke, A. (2011). Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. Journal of Rehabilitation Medicine, 43, 299304.Google Scholar
Timmermans, A.A.A., Verbunt, J.A., van Woerden, R., Moennekens, M., Pernot, D.H., & Seelen, H.A. (2014). Effect of mental practice on the improvement of function and daily activity performance of the upper extremity in patients with subacute stroke: a Randomized clinical trial. Journal of the American Medical Directors Association, 14, 204212.Google Scholar
Trost, S.G., McIver, K.L., & Pate, R.R. (2005). Conducting accelerometer-based activity assessments in field-based research. MMSE: Medicine and Science in Sports and Exercise, 37, S531S543.Google Scholar
Urbin, M.A., Bailey, R.R., & Lang, C.E. (2015). Validity of body-worn sensor acceleration metrics to index upper extremity function in hemiparetic stroke. Journal of Neurologic Physical Therapy, 39, 111118.Google Scholar
Urbin, M.A., Hong, X., Lang, C.E., & Carter, A.R. (2014). Resting-state functional connectivity and its association with multiple domains of upper-extremity function in chronic stroke. Neurorehabilitation and Neural Repair, 28, 761769.Google Scholar
Urbin, M.A., Waddell, K.J., & Lang, C.E. (2015). Acceleration metrics are responsive to change in upper extremity function of stroke survivors. Archives of Physical Medicine and Rehabilitation, 96, 854861. DOI 10.1016/j.apmr.2014.11.018.Google Scholar
Uswatte, G., Foo, W.L., Olmstead, H., Lopez, K., Holand, A., & Simms, L.B. (2005). Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Archives of Physical Medicine and Rehabilitation, 86, 14981501.Google Scholar
Uswatte, G., Miltner, W.H.R., Foo, B., Varma, M., Moran, S., & Taub, E. (2000). Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke, 31, 662667.Google Scholar
Uswatte, G., Taub, E., Morris, D., Light, K., & Thompson, P.A. (2006). The motor activity log-28: assessing daily use of the hemiparetic arm after stroke. Neurology, 67, 11891194.Google Scholar
van der Pas, S.C., Verbunt, J.A., Breukelaar, D.E., van Woerden, R., & Seelen, H.A. (2011). Assessment of arm activity using triaxial accelerometry in patients with a stroke. Archives of Physical Medicine and Rehabilitation, 92, 14371442.Google Scholar
Veerbeek, J.M., van Wegen, E., van Peppen, R., van der Wees, P.J., Hendriks, E., Rietberg, M., & Kwakkel, G. (2014). What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One, 9, e87987.Google Scholar
Vega-Gonzalez, A., & Granat, M.H. (2005). Continuous monitoring of upper-limb activity in a free-living environment. Archives of Physical Medicine and Rehabilitation, 86, 541548.Google Scholar
Waddell, K.J., Birkenmeier, R.L., Moore, J.L., Hornby, T.G., & Lang, C.E. (2014). Feasibility of high-repetition, task-specific training for individuals with upper-extremity paresis. American Journal of Occupational Therapy, 68, 444453.Google Scholar
Wade, E., Chen, C., & Winstein, C.J. (2014). Spectral analyses of wrist motion in individuals poststroke: the development of a performance measure with promise for unsupervised settings. Neurorehabil Neural Repair, 28, 169178.Google Scholar
World Health Organisation. (2001). International classification of functioning, disability and health. Geneva: World Health Organisation.Google Scholar
Xiao, Z.G., & Menon, C. (2014). Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities. Journal of NeuroEngineering and Rehabilitation, 11, 215.Google Scholar