In this work, the drawings collected from healthy individuals and people with PD were classified by RFC. The proposed method employed pencil drawings digitized from ordinary sheet of paper, making it very simple to be applied in the context of scarce financial resources. Despite the small number of images in the available data set (51 per class), the obtained results were satisfactory and accurate by discriminating drawings of healthy people from those with PD. HOG parameters were tested in default values (10x10 pixels per cell, 2x2 cells per block and 9 bins in the histogram with 0-180° orientation) focus on good performance showed by Dalal and Triggs [4] and the HOG result was passed to the classifier. This is the first reported study considering the application of HOG estimates in combination with the RFC applied to the automatic classification of data obtained from people with PD. In the future, it will be necessary to obtain more image drawings and different shapes to increase the database and test more parameters. ACKNOWLEDGEMENTS DISCUSSION AND CONCLUSION Parkinson’s disease (PD) is a neurological disorder that is progressive and causes losses of dopaminergic neurons from the substantia nigra, a region in the human brain. The decrease of dopamine in this area implies the worsening of motor symptoms such as tremors, bradykinesia, rigidity, gait impairment, and non-motor symptoms such as depression, loss of cognitive functions, sleep problems and nerve pain [1]. PD affects 1% of the world’s population aged 60 years and over, and despite scientific advancement, the disease remains incurable. The diagnosis of PD is complex, with a seasoned specialist being necessary to make it [1, 2]. Tremors are a common symptom in PD and it can be classified into many types: resting tremor, postural tremor, kinetic, essential, cerebellar, and others. Each type manifests in different situations and frequency ranges [3]. This work proposes to classify images of handwritten drawings collected from healthy individuals and people with PD. The identification and discrimination of motor symptoms in PD is a fundamental step in the diagnosis and follow-up of the disorder. INTRODUCTION 1. de Lau LM, Breteler MM (2006) Epidemiology of Parkinson’s disease. Lancet Neurol 5:525–535. doi: 10.1016/S1474-4422(06)70471-9 2. Cancela J, Mascato SV, Gatsios D, et al (2016) Monitoring of motor and non-motor symptoms of Parkinson’s disease through a mHealth platform. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. IEEE, pp 663–666 3. Andrade AO, Pereira AA, Almeida MFS de, et al (2013) Human Tremor: Origins, Detection and Quantification. In: Andrade AO (ed) Practical Applications in Biomedical Engineering. InTech, Croatia 4. Dalal N, Triggs B (2018) Histograms of Oriented Gradients for Human Detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE, pp 886–893. The present work was carried out with the support of the National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES – Program CAPES/DFATD- 88887.159028/2017-00) and the Foundation for Research Support of the State of Minas Gerais (FAPEMIG-APQ-00942-17). A. O. Andrade, A. A. Pereira and M. F. Vieira are a fellow of CNPq, Brazil (304818/2018-6, 310911/2017-6, and 306205/2017-3). RESULTS REFERENCES The Federal University of Uberlândia’s Research Ethics Committee approved the research under the number 07075413.6.0000.515. In the study, 51 images were collected from each group, i.e., healthy individuals and people with PD. A total of 102 images were available. MATERIALS AND METHODS Group Total Sex (F/M) Age (years) Health 12 8/4 60.08 ± 6.13 PD 15 7/8 65.33 ± 9.17 Fig. 1. (A) Samples of handwritten drawings collected from people with Parkinson’s disease (PD) and healthy individuals (H); (B) Pre-processed ima