Detection of Movement Disorders Using Multi SVM

A. Athisakthi
A. Athisakthi
Dr.M.Pushparani
Dr.M.Pushparani
Mother Teresa Women's University

Send Message

To: Author

Detection of Movement Disorders Using Multi SVM

Article Fingerprint

ReserarchID

79BYM

Detection of Movement Disorders Using Multi SVM Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Font Type
Font Size
Font Size
Bedground

Abstract

Gait analysis is very significant for early diagnosis of gait diseases and treatment assessment. Gait analysis is used to assess, plan and to treat the individuals with conditions affecting their ability to walk. In recent years, doctors gain more clarity and exact disease assessment by means of machine learning technologies and this has gained much application of gait analysis. The patients suffering from movement disorders such as Parkinson’s disease (PD), Huntington’s disease (HD), and Amyotrophic Lateral Sclerosis (ALS) can best be diagnosed by gait analysis. For these reasons, analysis of the above said diseases are taken into consideration. In this paper we propose an effective method for detection of movement disorders using multi SVM technique.

References

15 Cites in Article
  1. โ‡‘ Masood Banaie A,Mohammad Pooyan B,Mohammad Mikaili B (2010). Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes.
  2. James Cutting,Lynn Kozlowski (1977). Recognizing friends by their walk: Gait perception without familiarity cues.
  3. S Prabhakar,S Pankanti,A Jain (2003). Biometric recognition: security and privacy concerns.
  4. K Delac,M Grgic (2004). A survey of biometric recognition methods.
  5. Paul Addison (2002). the Illustrated Wavelet Transform Handbook.
  6. Ali Akansu,Richard Haddad (1992). Orthogonal Transforms.
  7. M Murray,A Drought,Ross Kory (1964). Walking Patterns of Normal Men.
  8. M Richardson,L Johnston (2005). Person recognition from dynamic events: The kinematic specification of individual identity in walking style.
  9. W Vapnik (1995). The Nature of Statistical Learning Theory.
  10. J Wu,J Wang,L Liu (2007). Feature extraction via KPCA for classification of gait patterns.
  11. S Gong,S Mckenna,A Psarrou (2000). Dynamic vision: From images to face recognition.
  12. Paola Cesari,Francesca Chiaromonte,Karl Newell (2007). Support vector machines categorize the scaling of human grip configurations.
  13. C Cheng,R Tutwiler,S Slobounov (2008). Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine.
  14. Norman Ricker (1953). Wavelet contraction, wavelet expansion, and the control of seismic resolution.
  15. D Christopher,Prabhakar Manning,Hinrich Raghavan,Schรผtze (2008). Introduction to Information Retrieval.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

A. Athisakthi. 1970. \u201cDetection of Movement Disorders Using Multi SVM\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 13 (GJCST Volume 13 Issue G1).

More Citation Formats

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Version of record

v1.2

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 24789
Total Downloads: 10858
2026 Trends
Related Research
Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Detection of Movement Disorders Using Multi SVM

A. Athisakthi
A. Athisakthi <p>Mother Teresa Women’s University</p>
Dr.M.Pushparani
Dr.M.Pushparani

Research Journals