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      Automated Assessment of Movement Impairment in Huntington’s Disease

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          Abstract

          Quantitative assessment of movement impairment in Huntington’s disease (HD) is essential to monitoring of disease progression. This paper aimed to develop and validate a novel low cost, objective automated system for the evaluation of upper limb movement impairment in HD in order to eliminate the inconsistency of the assessor and offer a more sensitive, continuous assessment scale. Patients with genetically confirmed HD and healthy controls were recruited to this observational study. Demographic data, including age (years), gender, and unified HD rating scale total motor score (UHDRS-TMS), were recorded. For the purposes of this paper, a modified upper limb motor impairment score (mULMS) was generated from the UHDRS-TMS. All participants completed a brief, standardized clinical assessment of upper limb dexterity while wearing a tri-axial accelerometer on each wrist and on the sternum. The captured acceleration data were used to develop an automatic classification system for discriminating between healthy and HD participants and to automatically generate a continuous movement impairment score (MIS) that reflected the degree of the movement impairment. Data from 48 healthy and 44 HD participants was used to validate the developed system, which achieved 98.78% accuracy in discriminating between healthy and HD participants. The Pearson correlation coefficient between the automatic MIS and the clinician rated mULMS was 0.77 with a p-value < 0.01. The approach presented in this paper demonstrates the possibility of an automated objective, consistent, and sensitive assessment of the HD movement impairment.

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          Is Open Access

          Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

          Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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            A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data.

            Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time- and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects. Overall, the best feature sets achieved over 95% intersubject classification accuracy.
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              Practical considerations of permutation entropy

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                Author and article information

                Journal
                IEEE Trans Neural Syst Rehabil Eng
                IEEE Trans Neural Syst Rehabil Eng
                0054500
                TNSRE
                ITNSB3
                Ieee Transactions on Neural Systems and Rehabilitation Engineering
                IEEE
                1534-4320
                1558-0210
                October 2018
                06 September 2018
                : 26
                : 10
                : 2062-2069
                Affiliations
                [1 ]divisionSchool of Computing and Communications, institutionThe Open University, ringgold 5488; Milton KeynesMK7 6AAU.K.
                [2 ]divisionSchool of Engineering, institutionCardiff University, ringgold 2112; CardiffCF24 3AAU.K.
                [3 ]divisionSchool of Biosciences, institutionCardiff University, ringgold 2112; CardiffCF10 3AXU.K.
                [4 ]divisionSchools of Biosciences and Medicine, institutionCardiff University, ringgold 2112; CardiffCF10 3AXU.K.
                [5 ]divisionCentre for Trials Research, institutionCardiff University, ringgold 2112; CardiffCF14 4YSU.K.
                [6 ]divisionSEWTU, Centre for Trials Research, institutionCardiff University, ringgold 2112; CardiffCF10 3ATU.K.
                Author notes
                Article
                10.1109/TNSRE.2018.2868170
                6196596
                30334742
                de11a1c8-07ac-4b79-9594-67fcbc749983
                This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
                History
                : 08 March 2018
                : 04 July 2018
                : 01 August 2018
                : 17 October 2018
                Page count
                Figures: 6, Tables: 5, Equations: 4, References: 31, Pages: 8
                Funding
                Funded by: Medical Research Council Confidence in Concept Scheme;
                Funded by: Wellcome Trust, fundref 10.13039/100010269;
                Funded by: Seventh Framework Programme, fundref 10.13039/100011102;
                Award ID: 602245
                Award Recipient : ClinchS. P.
                Funded by: Cardiff University, fundref 10.13039/501100000866;
                Award Recipient : BusseM.
                Funded by: Health and Care Research Wales, fundref 10.13039/100012068;
                Award Recipient : BusseM.
                This work was supported in part by the Medical Research Council Confidence in Concept Scheme and the Wellcome Trust ISSF funds. The work of S. P. Clinch was supported by REPAIR-HD through the European Union’s Seventh Framework Program under Grant 602245. M. Busse is affiliated to the Centre for Trials Research, Cardiff University that is funded by the Wales Assembly Government through Health and Care Research Wales.
                Categories
                Article

                accelerometers,upper-limb assessment,huntington’s disease,movement disorder

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