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      Using Inertial Measurement Units and Electromyography to Quantify Movement during Action Research Arm Test Execution

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          Abstract

          In patients after stroke, ability of the upper limb is commonly assessed with standardised clinical tests that provide a complete upper limb assessment. This paper presents quantification of upper limb movement during the execution of Action research arm test (ARAT) using a wearable system of inertial measurement units (IMU) for kinematic quantification and electromyography (EMG) sensors for muscle activity analysis. The test was executed with each arm by a group of healthy subjects and a group of patients after stroke allocated into subgroups based on their clinical scores. Tasks were segmented into movement and manipulation phases. Each movement phase was quantified with a set of five parameters: movement time, movement smoothness, hand trajectory similarity, trunk stability, and muscle activity for grasping. Parameters vary between subject groups, between tasks, and between task phases. Statistically significant differences were observed between patient groups that obtained different clinical scores, between healthy subjects and patients, and between the unaffected and the affected arm unless the affected arm shows normal performance. Movement quantification enables differentiation between different subject groups within movement phases as well as for the complete task. Spearman’s rank correlation coefficient shows strong correlations between patient’s ARAT scores and movement time as well as movement smoothness. Weak to moderate correlations were observed for parameters that describe hand trajectory similarity and trunk stability. Muscle activity correlates well with grasping activity and the level of grasping force in all groups.

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          Most cited references38

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          A performance test for assessment of upper limb function in physical rehabilitation treatment and research.

          R C Lyle (1981)
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            Movement smoothness changes during stroke recovery.

            Smoothness is characteristic of coordinated human movements, and stroke patients' movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth metric, the movements of patients with recent stroke grew less smooth over the course of therapy. This pattern was reproduced in a computer simulation of recovery based on submovement blending, suggesting that progressive blending of submovements underlies stroke recovery.
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              On the analysis of movement smoothness

              Quantitative measures of smoothness play an important role in the assessment of sensorimotor impairment and motor learning. Traditionally, movement smoothness has been computed mainly for discrete movements, in particular arm, reaching and circle drawing, using kinematic data. There are currently very few studies investigating smoothness of rhythmic movements, and there is no systematic way of analysing the smoothness of such movements. There is also very little work on the smoothness of other movement related variables such as force, impedance etc. In this context, this paper presents the first step towards a unified framework for the analysis of smoothness of arbitrary movements and using various data. It starts with a systematic definition of movement smoothness and the different factors that influence smoothness, followed by a review of existing methods for quantifying the smoothness of discrete movements. A method is then introduced to analyse the smoothness of rhythmic movements by generalising the techniques developed for discrete movements. We finally propose recommendations for analysing smoothness of any general sensorimotor behaviour. Electronic supplementary material The online version of this article (doi:10.1186/s12984-015-0090-9) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                22 August 2018
                September 2018
                : 18
                : 9
                : 2767
                Affiliations
                [1 ]Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, Slovenia; marko.munih@ 123456robo.fe.uni-lj.si (M.M.); matjaz.mihelj@ 123456robo.fe.uni-lj.si (M.M.)
                [2 ]Faculty of Health Sciences, University of Ljubljana, Zdravstvena pot 5, 1000 Ljubljana, Slovenia; urska.puh@ 123456zf.uni-lj.si
                [3 ]The University Rehabilitation Institute, Republic of Slovenia, Linhartova 51, 1000 Ljubljana, Slovenia; nika.goljar@ 123456ir-rs.si
                Author notes
                [* ]Correspondence: eva.repnik@ 123456fe.uni-lj.si ; Tel.: +386-147-68-195
                Author information
                https://orcid.org/0000-0002-0096-0373
                Article
                sensors-18-02767
                10.3390/s18092767
                6164634
                30135413
                84061f9e-6770-4138-b053-88b974c60031
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 July 2018
                : 20 August 2018
                Categories
                Article

                Biomedical engineering
                stroke,upper-limb movement,quantification,arat,inertial measurement unit,electromyography

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