Huntington's Disease Assessment Using Tri Axis Accelerometers

Bennasar, Mohamed; Hicks, Yulia; Clinch, Susanne; Jones, Phillip John; Rosser, Anne Elizabeth; Busse, Monica; Holt, Catherine Avril

doi:10.1016/j.procs.2016.08.163

Cited by 21 publications

(18 citation statements)

References 18 publications

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“…All participants completed the Money Box Test (MBT) [13] , [20] - a functional upper limb dexterity assessment involving a series of token transfer tasks ( Fig. 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…More recently, there have been a number of attempts to evaluate chorea, dystonia and bradykinesia in people with movement disorders using Inertial Measurement Units (IMUs) or electromagnetic motion sensors [6] , [8] – [13] . The majority have relied on statistical methods to assess movement impairment [10] , [11] , however more recently machine-learning techniques are being applied to the data to achieve automated assessment of the presence or absence of the symptoms or their severity [9] , [13] – [15] . Application of machine learning methods for wearable sensor data in Parkinson’s disease (PD) was advocated and explained in detail in a recent review where the possibility of using wearable sensor data for clinical PD measurement was also highlighted [16] .…”

Section: Introductionmentioning

confidence: 99%

“…There has been a fair amount of research in the area of applying machine learning methods for the automatic assessment of the movement disorders associated with PD, particularly tremor and bradykinesia [14] , [15] , [17] – [19] . At the same time, there were only two reports on the application of these methods in HD [9] , [13] . In the two latter studies, machine learning techniques were used to automatically classify people into HD and healthy controls groups based on gait analysis [9] or arm movements [13] and data from IMUs.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, there were only two reports on the application of these methods in HD [9] , [13] . In the two latter studies, machine learning techniques were used to automatically classify people into HD and healthy controls groups based on gait analysis [9] or arm movements [13] and data from IMUs. However, these studies are still falling short of proposing an automatic system capable of assessing HD movement impairment using a more sensitive, continuous scale necessary for monitoring the disease progression.…”

Section: Introductionmentioning

confidence: 99%

“…The selected features were subsequently used in the classification and quantitative assessment tasks. An ensemble classifier was proposed to distinguish between healthy individuals and those with a diagnosis of HD, which significantly improved the accuracy of the previously proposed simple SVM classifier [13] . Linear regression model was created to generate continuous scale sensitive assessment of movement impairment in HD, which has not been attempted in previous research.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automated Assessment of Movement Impairment in Huntington’s Disease

Bennasar

Hicks

Clinch

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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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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“…All participants completed the Money Box Test (MBT) [13] , [20] - a functional upper limb dexterity assessment involving a series of token transfer tasks ( Fig. 1 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Assessment of Movement Impairment in Huntington’s Disease

Bennasar

Hicks

Clinch

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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CNN‐Based Neurodegenerative Disease Classification Using QR‐Represented Gait Data

Erdaş,

Sümer

2024

Brain and Behavior

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PurposeThe primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns.MethodsThe research evaluates the CNN‐based classification approach using QR‐represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. The dataset includes recordings from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), and amyotrophic lateral sclerosis (n = 13), and from 16 healthy controls.ResultsThe accuracy rates obtained through 10‐fold cross‐validation were as follows: 94.86% for NDD versus control, 95.81% for PD versus control, 93.56% for HD versus control, 97.65% for ALS versus control, and 84.65% for PD versus HD versus ALS versus control. These results demonstrate the potential of the proposed system in distinguishing between different neurodegenerative diseases and control groups.ConclusionThe results indicate that the designed system may serve as a complementary tool for the diagnosis of neurodegenerative diseases, particularly in individuals who already present with varying degrees of motor impairment. Further validation and research are needed to establish its wider applicability.

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