On the Use of Low-Cost Computer Peripherals for the Assessment of Motor Dysfunction in Parkinson's Disease Quantification of Bradykinesia Using Target Tracking Tasks

Allen, Danette; Playfer, Jeremy; Aly, Nabil; Duffey, Philip; Heald, A.; Smith, Stephen L.; Halliday, David M.

doi:10.1109/tnsre.2007.897020

Cited by 34 publications

(28 citation statements)

References 21 publications

(31 reference statements)

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“…Although previous studies have tried to quantify the bradykinesia, they have neglected to consider multiple aspects of the bradykinesia [6][7][8][9][10][11]. Furthermore, they have investigated only single joint for quantification.…”

Section: Validation Of the Regression Modelsmentioning

confidence: 99%

“…Recently, there have been studies quantifying the bradykinesia using a musical instrument digital interface keyboard [6], joystick [7], accelerometer [8], magnetic sensor [9,10] and gyro sensor [11]. Various variables to represent the characteristics of bradykinesia were derived from the measured signals.…”

Section: Introductionmentioning

confidence: 99%

“…The consistency of the relationship between each characteristic variable and the clinical score (i.e. UPDRS bradykinesia score) [6,7,11] and the ability of each variable to discriminate different clinical scores [10,11] were evaluated in order to assess how well each variable denote the bradykinesia-specific features. A single characteristic variable may not fully include multiple aspects of bradykinesia such as speed, amplitude, hesitations, fatiguing, and arrests, while clinicians rate a single severity score from the observation and integration of multiple aspects of bradykinesia.…”

Section: Introductionmentioning

confidence: 99%

“…So far, there has been no study where the regression model was used to quantify bradykinesia. In the previous studies, many characteristic variables were shown to be significantly correlated with the clinical score [6,7,11]. When multiple variables are correlated with the clinical score, we need to know the relative importance of each variable on clinical rating of the bradykinesia.…”

Section: Introductionmentioning

confidence: 99%

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Regression models for the quantification of Parkinsonian bradykinesia

Kim

Kwon

Yun

et al. 2015

BME

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Abstract. The aim of this study was to develop regression models for the quantification of parkinsonian bradykinesia. Forty patients with Parkinson's disease participated in this study. Angular velocity was measured using gyro sensor during finger tapping, forearm-rotation, and toe tapping tasks and the severity of bradykinesia was rated by two independent neurologists. Various characteristic variables were derived from the sensor signal. Stepwise multiple linear regression analysis was performed to develop models predicting the bradykinesia score with the characteristic variables as input. To evaluate the ability of the regression models to discriminate different bradykinesia scores, ANOVA and post hoc test were performed. Major determinants of the bradykinesia score differed among clinical tasks and between raters. The regression models were better than any single characteristic variable in terms of the ability to differentiate bradykinesia scores. Specifically, the regression models could differentiate all pairs of the bradykinesia scores (p<0.05) except for one pair in the finger tapping task and one pair in the toe tapping task. In contrast, any single characteristic variable was found not sensitive enough to discriminate many of the pairs, especially in case of the toe tapping task. The results suggest that the multiple regression models reflecting these differences would be beneficial for the quantification of bradykinesia because the cardinal features included in the determination of bradykinesia score differ among tasks as well as among the raters.

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Section: Validation Of the Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regression models for the quantification of Parkinsonian bradykinesia

Kim

Kwon

Yun

et al. 2015

BME

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“…The system consists of the Rutgers Ankle prototype robot, a local PC and a remote PC over the Internet. In [8], exercises were built and used on a laptop with a force feedback joystick and a steering wheel for measuring motor dysfunction in Parkinson's disease. The exercises consist of tracking a continuously moving target (pursuit tracking), or moving to a predetermined target (step tracking).…”

Section: Existing Systemsmentioning

confidence: 99%

A comparison of age difference reaction to computer interface

Riman

Ghusn²

2010

Eur Rev Aging Phys Act

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In this work, a simplified interface system for education of people with disabilities is presented. This platform allows the control of different tools (PC games, robot, and electric wheelchair) using different interfaces (mouse, joysticks, etc.). This interface system can be automatically reconfigured to match user's ability, and it is operated locally or remotely through the internet. Saving of experimental progress data for later analysis can be done. Quantitative indicators are defined for evaluation purposes and to determine the needed assistance and adaptation. The system is tested on different age groups for people without physical disability. Results are presented and discussed.

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Quantitative Measurement of Akinesia in Parkinson's Disease

Lalvay¹,

Lara²,

Mora³

et al. 2016

Movement Disord Clin Pract

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Background There is great interest in developing simple, user‐friendly, and inexpensive tools for the quantification and elucidation of motor deficits in patients with Parkinson's disease (PD). These systems could help to monitor the clinical status of patients with PD, to develop better treatments, and to identify individuals who have subtle motor signs that might pass unnoticed in the conventional neurological examination. Methods Mememtum, a smartphone application that allows for the quantification of several parameters of movement, such as regularity, rhythm, and changes in the number of taps while taping with a single finger and with alternating fingers, was developed and then tested in a pilot study in Madrid and in an extensive study in Quito, Ecuador. Results Almost all patients could successfully perform single‐finger tapping, but approximately 10% of patients with severe parkinsonism had problems taping with alternating fingers. The results revealed changes in the regularity of the pressure applied while tapping and a reduction in the number of taps on the device screen when alternating tapping among patients who had idiopathic PD and vascular parkinsonism compared with controls and individuals who had prediagnostic motor abnormalities of PD. Conclusion Applications available in smartphones could be used for investigation and treatment of patients with PD, but much research is needed to optimize the ideal parameters to be investigated and the potential usefulness of this technique for patients with PD in different stages of the disease.

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On the Use of Low-Cost Computer Peripherals for the Assessment of Motor Dysfunction in Parkinson's Disease Quantification of Bradykinesia Using Target Tracking Tasks

Cited by 34 publications

References 21 publications

Regression models for the quantification of Parkinsonian bradykinesia

Regression models for the quantification of Parkinsonian bradykinesia

A comparison of age difference reaction to computer interface

Quantitative Measurement of Akinesia in Parkinson's Disease

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