Apresentação - FEBRACE 2021

Summary

This research explores the development of an open-source system for diagnosing and predicting Parkinson's disease using inertial data, primarily focusing on tremor, a key symptom. The system includes a custom-built hardware platform based on Arduino® for data collection and a user-friendly interface for data visualization. By applying dimensional reduction and clustering techniques, the system differentiates between individuals with Parkinson's disease and those without. The project aims to provide a non-invasive, cost-effective solution for researchers, with all components freely available for further development and use in the medical and research community.

Takeaways

• 😀 Wanghley Martins is a student and researcher at the Federal Institute of Brasilia, focusing on Parkinson's disease and individualized medicine.
• 😀 The research aims to develop a system for diagnosing and predicting Parkinson's disease, based on the primary symptom: tremor.
• 😀 Parkinson's disease is a neurodegenerative disorder with no current cure, primarily affecting motor functions and causing tremors in body parts like the hands.
• 😀 Few computer systems are currently used for diagnosing or prognosing Parkinson's disease, presenting an opportunity for innovation in this field.
• 😀 The research introduces a non-invasive, open-source system for diagnosing and predicting Parkinson's disease using inertial data collection.
• 😀 The system includes hardware based on the Arduino® platform, capable of collecting inertial data wirelessly.
• 😀 The developed interface is user-friendly and modern, designed to facilitate easy interaction with the hardware.
• 😀 Dimensional reduction techniques were used to convert inertial data into two-dimensional graphs for analysis.
• 😀 Clustering methods were employed to differentiate between Parkinson’s patients (Parkinsonians) and non-Parkinsonians based on collected data.
• 😀 The entire system, both hardware and software, is open-source, allowing other researchers to modify and use it without cost.
• 😀 More details, including published articles and source code, are available in the open-source repository linked in the description for further research.

Q & A

What is the main objective of the research presented in the transcript?

The main objective of the research is to develop a system capable of diagnosing and prognosing Parkinson's disease using its primary symptom, tremor, in a non-invasive way for the patient.

What is Parkinson's disease, and what are its primary symptoms?

Parkinson's disease is a neurodegenerative and incurable condition that mainly affects a person's movement. The primary symptom is tremor, which is usually located in the peripheral parts of the body, such as the hands.

Why is this research significant in the context of Parkinson's disease diagnosis and prognosis?

This research is significant because it aims to create a non-invasive system for diagnosing and prognosing Parkinson's disease, which is not widely available through computer systems at present.

What makes the system developed in this research different from other existing systems?

The system is open-source, meaning both its hardware and software are freely available for modification and use by other researchers. This allows for cost-effective implementation in further research and applications.

What type of data does the hardware developed in this research collect?

The hardware collects inertial data, such as acceleration and rotation of body movement, which can be used to analyze tremor characteristics.

How was the hardware for this system built?

The hardware was built using the Arduino® platform, with wireless communication capabilities, enabling the collection of inertial data from the patient.

What role does the user interface play in this research?

The user interface developed for this research is modern and user-friendly, designed to make it easier for users to operate the system and visualize the inertial data collected by the hardware.

How does the system differentiate between people with and without Parkinson's disease?

The system uses dimensional reduction techniques to transform inertial data into two-dimensional graphs. These graphs help differentiate between the characteristics of Parkinson's patients and non-Parkinson's individuals based on clustering analysis.

Is the system developed in this research accessible for other researchers?

Yes, the system is open-source, meaning the hardware designs, software, and related materials are publicly available. Other researchers can access these resources at no cost and adapt them for their own studies.

Where can more details about the research and the system be found?

More details about the research, including published articles, source code, and other related materials, can be accessed through the project's open-source repository linked in the description on YouTube.