This document discusses chronic diseases and proposes a framework for early detection. It begins by defining chronic diseases and listing some common types like arthritis, cancer, stroke, and heart attack. Parkinson's disease is then discussed in more detail, including its causes, symptoms, increasing rates, and challenges around early detection. The importance of early detection for Parkinson's is explained. Recent research on potential biomarkers from cerebrospinal fluid and blood is mentioned. Research from India and abroad is summarized, including studies using smartphones to assess tremors, deep learning to analyze sensor data, and quantifying finger tremors. The document concludes by emphasizing the need for more research to enable earlier detection of Parkinson's disease.

1. DESIGN A FRAMEWORK FOR
EARLY DETECTION OF CHRONIC
DISEASES

2. INTRODUCTION TO CHRONIC
DISEASES
• Chronic disease is disease that persists over a long period of time. Chronic disease can
hinder independence and the health of people with disabilities, as it may create additional
activity limitations. People with chronic disease often think that they are free from the
disease when they have no symptoms. Having no symptoms, however, does not
necessarily mean that chronic disease has disappeared. The good news is that chronic
disease can be prevented or controlled through 1) regular participation in physical
activity, 2) eating healthy, 3) not smoking, and 4) avoiding excessive alcohol
consumption.

3. COMMON CHRONIC DISEASES
• Arthritis is the “wear and tear” on the joints such as the knees, hips and
wrists. Its early signs include:
• Cancer is the uncontrollable growth of abnormal cells in the body.
• Stroke is a blockage of blood flow to the brain.
• Heart attack is a blockage of blood flow to the heart.
• Obesity is a chronic health condition of being above normal body weight.

4. PARKINSON’S DISEASE
Parkinson’s disease is a disorder that affects
small regions in the brain that control movement,
posture and balance.

5. MORE ABOUT PD
• Parkinson’s disease is named after the British doctor who wrote the first
book about the disease, in 1817, that made it an easily recognized entity.
• After Alzheimer’s disease, it is the second most common progressive,
neuro degenerative disorder in the world.
• There is a large amount of research on PD, we still don’t know what
causes it. And we even have some trouble diagnosing it at times.

6. INCREASING RATE
OF PARKINSON’S
DISEASE

7. WHY EARLY DETECTION IN PD
• In the recent work done in this area we have seen that there is no cure to this
disease we can only cure the symptoms and not the disease.
• There is a need for more sensitive diagnostic tools for PD detection because,
as the disease progresses, more symptoms arise that make PD harder to treat.
• PD is difficult to detect early due to the subtle initial symptoms. There is a
significant burden to patients and the health care system due to delays in
diagnosis

8. Parkinson’s Disease Research Work
• How to identify potential biomarkers for Parkinson’s disease.
• Cerebrospinal fluid in many ways is the tissue of choice for biomarkers of
brain disease but is limited by patients so attention is being paid to the
research for blood-based biomarkers.

9. RESEARCH WORK IN INDIA AND
ABROAD
Through different libraries like IEEE, BIOMED, PUBMED we have selected
various papers and came across that this is widely spreading neuro degenerative
disorder after alzeimer’s disease. We have selected few papers and explained
how the symptoms of this disease is tried to cured.

10. RESEARCH WORK DONE IN ABROAD
In 2015, the authors N. Kostikis, D. Hristu-Varsakelis, M. Arnaoutoglou, and C.
Kotsavasiloglou have discussed in their paper “A SMARTPHONE-BASED TOOL
FOR ASSESSING PARKINSONIAN HAND TREMOR”
In this paper they found a smart phone technology. The aim of this study is to propose a
practical smartphone-based tool to accurately assess upper limb tremor in Parkinson’s
disease (PD) patients. The tool uses signals from the phone’s accelerometer and gyroscope
(as the phone is held or mounted on a subject’s hand) to compute a set of metrics which can
be used to quantify a patient’s tremor symptoms.

11. IN YEAR 2016
In 2016, the authors Bjoern M. Eskofier , Sunghoon I. Lee, Jean-Francois Daneault,
Fateman N. Golabchi, Gabriela Ferreira-Carvalho,Gloria Vergara-Diaz have discussed
in therir paper “ RECENT MACHINE LEARNING ADVANCEMENTS IN SENSOR-
BASED MOBILITY ANALYSIS: DEEP LEARNING FOR PARKINSON’S DISEASE
ASSESSMENT”
The first one is a discussion of the advantages and disadvantages of deep learning for
sensor-based movement assessment and conclude that deep learning is a promising method
for this field. Here they collected data from ten patients with idiopathic Parkinson’s disease
using inertial measurement units. Several motor tasks were expert-labelled and used for
classification. Their results showed that deep learning outperformed other state-of-the-art
machine learning algorithms by at least 4.6 % in terms of classification rate.

12. IN YEAR 2017
In 2017, the authors M. Bravo, A. Bermeo, M. Huerta, C. Llumiguano, J. Bermeo, R.
Clotet, and A. Soto have discussed in their paper “A SYSTEM FOR FINGER
TREMOR QUANTIFICATION IN PATIENTS WITH PARKINSON’S DISEASE”
The current diagnosis of Parkinson's disease (PD) is based on a subjective assessment by the
specialist. The monitoring of the tremor that presents in the hand index fingers in a patient
with Parkinson's is one of the most important parameters to diagnose the evolution of the
disease in an objective manner. This research analyze the tremor in the hand index fingers of
patients with PD with medication and without medication.

13. IN THE YEAR 2018
• In this year, the authors Satyabrata Aich , Kim younga , Kueh Lee Hui , Ahmed
Abdulhakim Al-Absi and Mangal Sain have discussed in their paper “ A
NONLINEAR DECISION TREE BASED CLASSIFICATION APPROACH TO
PREDICT THE PARKINSON’S DISEASE USING DIFFERENT FEATURES
SETS OF VOICE DATA”
• The researchers used Machine learning based approach which is used in many cases of
Parkinson’s disease using gait data as well as voice data. However, so far no body has
compared the performance metrics using different feature sets by applying non-linear
based classification approach based on the voice data. So in this year we have proposed a
new approach by comparing the performance metrics with different feature sets such as
original feature sets as well as Principal component Analysis based feature reduction
technique for selecting the feature sets.

14. IN THE YEAR 2019
In 2019, the authors Juan Camilo V´asquez-Correa , Tomas Arias-Vergara, J. R.
Orozco-Arroyave, Bjorn Eskofier , Jochen Klucken, and Elmar Noth have discussed in
their paper “ MULTIMODALASSESSMENT OF PARKINSON’S DISEASE: A DEEP
LEARNING APPROACH”
Machine learning based approach has been used by many researchers across the field
because of its accuracy on the complex data. Machine learning based approach has been
used in many cases of Parkinson’s disease using gait data as well as voice data. However, so
far no body has compared the performance metrics using different feature sets by applying
non-linear based classification approach based on the voice data.

15. RESEARCH WORK DONE IN INDIA
The first description of Parkinson's disease (PD) was given by James
Parkinson in early 19th century. But the knowledge about this disease has
been present in India since ancient times. Though the prevalence of PD in
India is less compared to other countries, the total burden of PD is much
higher as a result of large population.
Through our literature survey we have find that not much work done in this
area and symptoms recognised later are harder to cure at times so we have
decided to work in this area so that this disease can be recognised earlier.

16. In the year 2017
In this year the authors Vrutangkumar V. Shah, Sachin Goyal, and Harish J.
Palanthandalam-Madapusi have discussed in their paper “A Possible Explanation of
How High-Frequency Deep Brain Stimulation Suppresses Low-Frequency Tremors in
Parkinson’s Disease”
In this paper, we set out to analyse how a high-frequency stimulation applied through
DBS can help reduce the low-frequency rest tremors observed in PD patients.We
identify key elements in the sensorimotor loop (the feedback loop consisting of sensory
feedbacks and motor responses) that play a role in the interaction of highfrequency
DBS signal and the low-frequency tremor. Based on the analysis of these elements, we
draw insights about the working of DBS and the role of frequency and the nature of
stimulation. We verify these observations with numerical examples and a bench top
experimental example.

17. In the year 2019
In yhis year the authors Navleen Kour , Sunanda , and Sakshi Arora have discussed
in their paper “Computer-Vision Based Diagnosis of Parkinson’s Disease via Gait: A
Survey”
The objective of this article is to systematically analyze the applications of computer vision
in PD evaluation through gait. This paper surveys the VB PD gait acquisition modalities as
well as provides a concise overview of preprocessing techniques. The study presents a
description of PD related gait features, extraction and selection methods used for PD
analysis. A number of machine learning techniques for classication of PD and healthy gait
are also discussed. This article extensively surveys PD gait datasets considering data from
1997 to 2018. Also, several research gaps in existing studies have identied that need to be
addressed in the future.

18. CONCLUSION
Through literature survey on the Parkinson’s disease we have come to the
conclusion that this disease is not curable and only symptoms can be treated.
So we want to identify a biomarker for this disease through which disease can
be detected as early as possible. In India very few journals are published in this
area through IT perspective so we have to lookout for good research which can
be beneficial in future. So we have to work further in this field to find out
which algorithm and technologies scan be used to detect this disease early.

19. WORKSHOP ATTENDED
• NATIONAL WORKSHOP ON ARTIFICIAL INTELLIGENCE AND
MACHINE LEARNING - 2019

20. REFERENCES
[1] Toward Monitoring Parkinson’s Through Analysis of Static Handwriting Samples: A Quantitative
Analytical Framework
Naiqian Zhi, Beverly Kris Jaeger, Andrew Gouldstone, Rifat Sipahi, Member, IEEE, and Samuel
Frank
[2] Mobile Devices for the Real-Time Detection of Specific Human Motion Disorders
Paolo Lorenzi, Rosario Rao, Giulio Romano, Ardian Kita, and Fernanda Irrera
[3] Prediction of Parkinson’s Disease using Speech Signal with Extreme Learning Machine
1Aarushi Agarwal, 1Spriha Chandrayan and 1Sitanshu S Sahu 1Department of Electronics and
Communication Engineering

21. REFERENCES
[4] A System for Finger Tremor Quantification in Patients with
Parkinson’s Disease
M. Bravo, Student Member, IEEE, A. Bermeo, Student Member, IEEE, M. Huerta, Senior Member,IEEE, C. Llumiguano, J.
Bermeo, Member, IEEE, R. Clotet, Student Member, IEEE, and A. Soto,Member, IEEE
[5] Smartphone Allows Capture of Speech Abnormalities Associated With High Risk of Developing Parkinson’s Disease
Jan Rusz , Jan Hlavniˇcka, Tereza Tykalová, Michal Novotný, Petr Dušek, Karel Šonka, and Evžen R° užiˇcka
[6] Deep Learning-Based Parkinson's Disease Classification Using Vocal Feature Sets
HAKAN GUNDUZ Computer Engineering Department, Duzce University, 81620 Duzce, Turkeym

22. REFERENCES
[7] A Smartphone-Based Tool for Assessing Parkinsonian Hand Tremor N. Kostikis, Member, IEEE, D. Hristu-Varsakelis, Senior Member, IEEE,
M. Arnaoutoglou, and C. Kotsavasiloglou
[8] Using Machine Learning to Diagnose Parkinson’s Disease from Voice Recordings
Akshaya Dinesh,North Brunswick Township High School,Rutgers School of Engineering Piscataway, New Jersey
Jennifer He Bergen County Technical High School,Rutgers School of Engineering Piscataway, New Jersey
[9] Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
Diane J. Cook, Fellow, IEEE, Maureen Schmitter-Edgecombe, and Prafulla Dawadi, Member, IEEE
[10] Classification of Parkinson’s Disease Gait Using Spatial-Temporal Gait Features
Ferdous Wahid, Rezaul K. Begg, Chris J. Hass, Saman Halgamuge, and David C. Ackland
•