Android application for spiral analysis in Parkinson's Disease

Surangsrirat, Decho; Thanawattano, Chusak

doi:10.1109/secon.2012.6196943

Cited by 19 publications

(17 citation statements)

References 12 publications

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“…However the difference between the two groups is quite evident with the PD group having over twice the average pixels or error. These results were similar to the ones obtained in (Surangsrirat & Thanawattano, 2012) which does not discard the use of a device with a smaller screen to obtain relevant data. Analysing the Figure 5.3, even if both of the spirals have similar pixel errors, the control group one have a constant and linear error, while on the PD one the error is not constant and with peaks on opposite directions in short segments of the spiral.…”

Section: Discussionsupporting

confidence: 88%

“…The Figure 2.1 shows a preliminary analysis of two subjects of testing where the left side represents a spiral and respective polar coordinates drawn by an healthy person and the right side represents a patient with PD. It still is in early development and it seems to be very promising, being the future work planned by the authors the development of a classification model to validate the usefulness of the application (Surangsrirat & Thanawattano, 2012). (Surangsrirat & Thanawattano, 2012) The solution for this problem will be a smartphone application.…”

Section: Spiral Analysismentioning

confidence: 99%

“…It still is in early development and it seems to be very promising, being the future work planned by the authors the development of a classification model to validate the usefulness of the application (Surangsrirat & Thanawattano, 2012). (Surangsrirat & Thanawattano, 2012) The solution for this problem will be a smartphone application. Since it is supposed to reach as much users as possible it needs to be implemented in a platform that has high levels of popularity and usage.…”

Section: Spiral Analysismentioning

confidence: 99%

“…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%

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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: Discussionsupporting

confidence: 88%

Section: Spiral Analysismentioning

confidence: 99%

Section: Spiral Analysismentioning

confidence: 99%

Section: Spiral Analysismentioning

confidence: 99%

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ParkDetect: Early diagnosing Parkinson's Disease

Graça

Castro

Cevada

2014

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

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“…A significant supporting tool regarding tremor assessment, which has emerged in recent years, is using the screens of simple mobile devices or graphic tables offering not only the recording of test images, such as, e.g. the Archimedean spirals, but also the possibility of quantitative description of their development dynamics, demonstrated by the measurable pressing force, pen inclination angles and drawing speed [1,9,14].…”

Section: Related Workmentioning

confidence: 99%

Multimodal data acquisition set for objective assessment of Parkinson’s disease

Chmielińska

Białek

Potulska‐Chromik

et al. 2020

Radioelectronic Systems Conference 2019

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Parkinson's disease is a relatively common illness, constantly progressing, evoking anxiety and depression resulting from significantly restricted independence. Its dominating symptoms include muscle rigidity, rest tremor, slowness and postural disorders. They can lead to progressing gait disorders, hypomimia, extrapyramidal dysarthria and micrography. Acquisition of data imaging the condition of a patient and their objective evaluation can constitute a valuable tool improving the diagnostics and treatment monitoring. The developed data acquisition set enables recording data while the patient is performing selected tests, based on the UDPRS scale. The set consists of an infrared camera, a camera operating within the visible range, a microphone with a preamplifier, a graphic tablet and a laptop. A set of recording devices controlled by a graphic user interface guarantees the acquisition of data for the purposes of studying primarily motor symptoms. The set of recorded data includes a face image in the visible light range and in infrared for studying hypomimia, video images of the limbs for studying finger tapping movement regularity, hand movement regularity, lower limb agility and gait, and spontaneous and forced speech samples to evaluate the strength of the voice, its timbre and quality. In addition, the used graphic tablet enables collecting handwriting samples for testing writing speed and the force used. The suggested solution enables non-invasive quantitative measurements and archiving multimodal data describing the condition of a patient, which after processing can be used in diagnostics, evaluating treatment effectiveness and studying the progression of the disease.

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Quantitative wearable sensors for objective assessment of Parkinson's disease

et al. 2013

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There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology may allow for continuous, unobtrusive, objective, and ecologically valid data collection. Also, this approach may improve patient-doctor interaction, influence therapeutic decisions, and ultimately ameliorate patients' global health status. In addition, such measures have the potential to be used as outcome parameters in clinical trials, allowing for frequent assessments; eg, in the home setting. This review discusses promising wearable technology, addresses which parameters should be prioritized in such assessment strategies, and reports about studies that have already investigated daily life issues in PD using this new technology.

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Android application for spiral analysis in Parkinson's Disease

Cited by 19 publications

References 12 publications

ParkDetect: Early diagnosing Parkinson's Disease

ParkDetect: Early diagnosing Parkinson's Disease

Multimodal data acquisition set for objective assessment of Parkinson’s disease

Quantitative wearable sensors for objective assessment of Parkinson's disease

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