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

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          Abstract

          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.

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          Most cited references46

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          Statistical pattern recognition: a review

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            Activity identification using body-mounted sensors--a review of classification techniques.

            With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
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              The Motor Activity Log-28: assessing daily use of the hemiparetic arm after stroke.

              Data from monkeys with deafferented forelimbs and humans after stroke indicate that tests of the motor capacity of impaired extremities can overestimate their spontaneous use. Before the Motor Activity Log (MAL) was developed, no instruments assessed spontaneous use of a hemiparetic arm outside the treatment setting. To study the MAL's reliability and validity for assessing real-world quality of movement (QOM scale) and amount of use (AOU scale) of the hemiparetic arm in stroke survivors. Participants in a multisite clinical trial completed a 30-item MAL before and after treatment (n = 106) or an equivalent no-treatment period (n = 116). Participants also completed the Stroke Impact Scale (SIS) and wore accelerometers that monitored arm movement for three consecutive days outside the laboratory. All were 3 to 12 months post-stroke and had mild to moderate paresis of an upper extremity. After an item analysis, two MAL tasks were eliminated. Revised participant MAL QOM scores were reliable (r =0.82). Validity was also supported. During the first observation period, the correlation between QOM and SIS Hand Function scale scores was 0.72. The corresponding correlation for QOM and accelerometry values was 0.52. Participant QOM and AOU scores were highly correlated (r = 0.92). The participant Motor Activity Log is reliable and valid in individuals with subacute stroke. It might be employed to assess the real-world effects of upper extremity neurorehabilitation and detect deficits in spontaneous use of the hemiparetic arm in daily life.
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                Author and article information

                Journal
                applab
                Brain Impairment
                Brain Impairment
                Cambridge University Press (CUP)
                1443-9646
                1839-5252
                March 2016
                November 10 2015
                March 2016
                : 17
                : 01
                : 16-33
                Article
                10.1017/BrImp.2015.21
                90f1529d-cc96-46a5-bd53-777b5b620357
                © 2016
                History

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