Detection Hand Tremor Through Each Finger Movement Based On Arduino For Parkinson’s Patients

Author(s):  
Eka Mistiko Rini ◽  
Endi Sailul Haq
Keyword(s):  
2018 ◽  
Author(s):  
Warwick R. Adams

AbstractParkinson’s Disease (PD) is a neurodegenerative movement disease affecting over 6 million people worldwide. Current diagnosis is based on clinical and observational criteria only, resulting in a high misdiagnosis rate. Approximately 75% of people with PD have hand tremor, which can precede clinical diagnosis by up to 6 years. Previous studies have shown that early PD can be accurately detected from keystroke features while typing, and this study investigated whether tremor can be detected as well. Typing data from 76 subjects, with and without PD, including 27 with PD and 15 with tremor, was analysed and showed that hand tremor in PD can be detected from keystroke features. This novel technique has not been used before and was able to achieve an overall sensitivity of 67% and a specificity of 80% and was also able to differentiate PD tremor from essential tremor. This means that the diagnosis of early PD through typing can achieve the clinical requirement of at least two cardinal features being present (bradykinesia and tremor). Less than half a page of typing is needed, the technique does not require any specialised equipment, and can take place in the patient’s home as they type normally on a computer.


2009 ◽  
Vol 36 (S 02) ◽  
Author(s):  
R Gentner ◽  
A Hefny ◽  
W Farhan ◽  
F Segor ◽  
D Dees ◽  
...  

Author(s):  
Wenqiang Chen ◽  
Lin Chen ◽  
Meiyi Ma ◽  
Farshid Salemi Parizi ◽  
Shwetak Patel ◽  
...  

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.


2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 890-890
Author(s):  
JuHee Lee ◽  
Yujin Suh ◽  
Yielin Kim

Abstract Smart phone-based technology for people with Parkinson’s disease has been developed worldwide. Unmonitored non-motor symptoms decrease quality of life of people with Parkinson’s disease, so the needs for technology to manage non-motor symptoms are increasing. The technology is needed to detect subtle changes in non-motor symptoms by healthcare professional. There is no mobile app which manage comprehensive symptoms of Parkinson’s disease including non-motor symptoms. It is necessary to develop a new tracking system that can effectively manage non-motor symptoms as well as motor symptoms of Parkinson’s disease. We developed a prototype of mobile app for Android smartphones, with cooperation with Mazelone company. we also have shaped functions for monitoring of motor symptoms and medication adherence. It also provided a section for caregivers to use on behalf of people with Parkinson’s disease who have difficulty to use app due to hand tremor. Through Delphi technique, we obtained content validity from eight medical and nursing experts on the contents of the application. We provided regular telephone counseling to improve and encourage their app usage. Fifteen participants used the app for 6 weeks. To evaluate usability of mobile app, we provided constructed questionnaire and conducted individual telephone interview. A mobile app for tracking non-motor symptoms demonstrated high usability and satisfaction. We learned lessons about facilitators and barriers when implementing an app such as perception and acceptance of mobile technology. The mobile app will improve continuum of care. Future studies need to improve the contents and refine technical approach for people with Parkinson’s disease.


2005 ◽  
Vol 166 (2) ◽  
pp. 190-199 ◽  
Author(s):  
Justine M. Mayville ◽  
Armin Fuchs ◽  
J. A. Scott Kelso
Keyword(s):  

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