Computer-Based Assessment of Bradykinesia, Akinesia and Rigidity in Parkinson’s Disease

Cunningham, Laura; Nugent, Chris; Ge, Moore; Finlay, Dewar; Craig, David W.

doi:10.1007/978-3-642-02868-7_1

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…From the work explored in the State of the Art phase it stood obvious that PD diagnosis through the analysis of a spiral drawn by a person is effective and accurate. With this approach it is possible to evaluate movement disorders such as tremor, rigidity and dradykinesia (Surangsrirat in this area to prove the concept Liu et al, 2005;Miralles et al, 2006;Aly et al, 2007) and to test several implementations in different platforms using different kinds of spiral (pentagon, octogonal, Archimedian) (Surangsrirat & Thanawattano, 2012;Westin et al, 2010;Wang et al, 2008;Wang et al, 2011;Cunnungham et al, 2009).…”

Section: Spiral Analysismentioning

confidence: 99%

ParkDetect: Early diagnosing Parkinson's Disease

Graça

Castro

Cevada

2014

2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Parkinson's Disease is one of the most common neurodegenerative disorders of the central nervous system that affects elderly. There are six main symptoms: tremors, rigidity, bradykinesia (slow movements), hand asymmetry, posture instability and freezing of gait. Nowadays any type of diagnose for this disorder is done through observation by a health care professional specialized in this area. Therefore a simpler and more efficient method that General Practioners can use to have some grounded information to decide to forward a possible patient to a specialist is needed. With this in mind different systems were studied coming to the conclusion that a mobile application is among the best options. This work can be split in four important phases (see Figure 1): (1) study of the current market for this problem and for the solution to be developed, (2) development of a smartphone application capable of gathering data of the early symptoms of Parkinson's taking into consideration all the smartphone's specifications; (3) use the application to gather data from real patients and a control group and (4) test and select a classification algorithm. The first phase involved two research topics: problem and solution. The problem consisted in studying all the symptoms that could theoretically be detected by the different smartphone components. The solution consisted in studying the different methods used to solve such a problem using data mining techniques (different feature selection and classification algorithms that best take advantage of the nature of the data gathered). The second phase consisted in the development of the smartphone application with four components (spiral analysis, tap analysis, simple questions and gait analysis). The third phase was dedicated in building the control group gathering data from healthy people and a Parkinson patients group for a total of 35 subjects. Finally, the fourth phase was using the studied algorithms to filter the different features- and compare the different algorithms selected. With the available data from the test subjects it was possible to achieve promising results from the gait analysis of the patients where the pelvic sway was a good feature to help differentiate Parkinson patients from healthy ones

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Section: Spiral Analysismentioning

confidence: 99%

ParkDetect: Early diagnosing Parkinson's Disease

Graça

Castro

Cevada

2014

2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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“…Those being computer illiterates and suffering from PD showed a higher variance in terms of accuracy of clicking the target center. In [19], Cunningham et al present their tool's abilities in indicating akinesia, bradykinesia, dyskinesia, rigidity and tremor.…”

Section: Othermentioning

confidence: 99%

Parkinson's Disease Motor Symptoms in Machine Learning: A Review

Ahlrichs¹,

Lawo²

2013

HIIJ

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This paper reviews related work and state-of-the-art publications for recognizing motor symptoms of Parkinson's Disease (PD). It presents research efforts that were undertaken to inform on how well traditional machine learning algorithms can handle this task. In particular, four PD related motor symptoms are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia) and their details summarized. Thus the primary objective of this research is to provide a literary foundation for development and improvement of algorithms for detecting PD related motor symptoms.

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Computer-based assessment of movement difficulties in Parkinson's disease

Cunningham

Nugent

et al. 2012

Computer Methods in Biomechanics and Biomedical Engineering

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The prevalence of Parkinson's disease (PD) is increasing due to an ageing population. It is an unpredictable disease which requires regular assessment and monitoring. Current techniques used to assess PD are subjective. Clinicians observe movements made by a patient and subsequently rate the level of severity of, for example tremor or slowness of movement. Within this work, we have developed and evaluated a prototype computer-based assessment tool capable of collecting information on the movement difficulties present in PD. Twenty participants took part in an assessment of the tool, 10 of whom were diagnosed with PD and 10 were without the disease. Following the usage of the tool, it was found that there was a significant difference (p = 0.038) in the speed of movement between the two groups. We envisage that this tool could have the potential to enable more objective clinical conclusions to be made.

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Computer-Based Assessment of Bradykinesia, Akinesia and Rigidity in Parkinson’s Disease

Cited by 8 publications

References 3 publications

ParkDetect: Early diagnosing Parkinson's Disease

ParkDetect: Early diagnosing Parkinson's Disease

Parkinson's Disease Motor Symptoms in Machine Learning: A Review

Computer-based assessment of movement difficulties in Parkinson's disease

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