Evaluation of mathematic modell for study of fine motor function in individuals with Parkinson's disease using a multi-sensor biometric smart pen, BiSP

• Grant number: 10/15566-1
• Support Opportunities: Regular Research Grants
• Start date: November 01, 2011
• End date: October 31, 2013
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: Silke Anna Theresa Weber
• Grantee: Silke Anna Theresa Weber
• Host Institution: Faculdade de Medicina (FMB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Associated researchers: Arthur Oscar Schelp ; Carlos Alberto dos Santos Filho ; Christian Hook ; Lucas Francisco Guimarães ; Luiz Antonio de Lima Resende ; Michael Pfeifer

Abstract

There is an increasing interest in studying physiologic phenomens by mathematic modells. The biosensor BiSP nanlyses components ot the movement and permits a classification as normal or not-normal. Parkinson's disease has as a characteristic the progressive loss of the voluntary control of fine coordinated movements, therefor, patients are an ideal model for studying movement. Objectives Evaluate a standard of movements in individuals with Parkinson's disease through clinical examination, and data generated by a pen-based multichannel sensor system (Biomedical Smart Pen, BiSP). Correlate the findings with the clinical profile, possible instability of the disease and lack of control of the medication. Method: 40 individuals with Parkinson's disease, both genders, aging 40 - 65, already in outpatient follow-up will be attended every three months in a period of 12 months. Patients taking medication that may affect motor control and presenting co-morbidities that alter the motor control, dementia and decrease of visual acuity compromising the drawings will be excluded. Moreover, the results of biochemical exams and cranial CT and patient record will be obtained. The patients will submit to clinical evaluation, and fill out a form with standard drawings (curl, spiral and circles) during a period of 12 months, at each follow-up visit. 40 adults with similar demographic characteristics, without motor disorders, making up the control group, will be included, in addition to submitting to only one evaluation. The data will be described individually and by group and compared one another. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PAPA, JOAO PAULO; ROSA, GUSTAVO HENRIQUE; PAPA, LUCIENE PATRICI. A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes. PATTERN RECOGNITION LETTERS, v. 100, p. 59-66, DEC 1 2017. (13/07375-0, 14/16250-9, 16/19403-6, 10/15566-1, 14/12236-1, 15/25739-4)

PEREIRA, CLAYTON R.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; ROSA, GUSTAVO H.; PAPA, JOAO; IEEE. Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics. 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 7-pg., 2016-01-01. (15/25739-4, 14/16250-9, 10/15566-1)

PEREIRA, CLAYTON R.; PEREIRA, DANILO R.; ROSA, GUSTAVO H.; ALBUQUERQUE, VICTOR H. C.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO P.. Handwritten dynamics dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. ARTIFICIAL INTELLIGENCE IN MEDICINE, v. 87, p. 67-77, MAY 2018. (13/07375-0, 14/16250-9, 16/19403-6, 10/15566-1, 14/12236-1, 15/25739-4)